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Vehicular traffic alerts for avoidance of abnormal traffic conditionsMethods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.What is claimed is:
| 1. A computer-implemented method of generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile, the method comprising:
detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle;
generating, via the one or more processors associated with the first vehicle, an electronic message regarding the abnormal traffic condition;
transmitting, via a vehicle-mounted transceiver associated with the first vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receiving, via the one or more processors associated with the first vehicle, telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
transmitting, via the one or more processors associated with the first vehicle, the telematics data to a remote server, wherein the remote server updates a vehicle-usage profile associated with the nearby vehicle.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 5. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 6. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 7. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 8. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the operating environment of the first vehicle.
| 9. The computer-implemented method of claim 1, the method further comprising transmitting the electronic message to a smart infrastructure component, wherein the smart infrastructure component:
analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition; and
performs an action based on the type of anomalous condition in order to modify the anomalous condition.
| 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and wherein the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 11. A computer system configured to generate a vehicle-to-vehicle traffic alert and update a vehicle-usage profile, the computer system comprising one or more processors, the one or more processors configured to:
detect that an abnormal traffic condition exists in an operating environment of a first vehicle;
generate an electronic message regarding the abnormal traffic condition;
transmit, via a vehicle-mounted transceiver associated with the first vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receive telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
transmit the telematics data to a remote server, wherein the remote server updates a vehicle-usage profile associated with the nearby vehicle.
| 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 14. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 15. The computer system of claim 11, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 16. The computer system of claim 11, wherein the one or more processors is one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 20. The computer system of claim 11, wherein the nearby vehicle travels to the operating environment of the first vehicle. | The method involves detecting that an abnormal traffic condition exists in an operating environment of the first vehicle, generating an electronic message regarding the abnormal traffic condition, transmitting the electronic message to a nearby vehicle, in which the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition, receiving telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmitting the telematics data to a remote server via the processors associated with the first vehicle, in which the remote server updates a vehicle-usage profile associated with the nearby vehicle. An INDEPENDENT CLAIM is also included for a computer system. Computer-implemented method of generating vehicle-to-vehicle traffic alert and updating vehicle-usage profile. Helps improve driving behavior by providing for feedback to the evaluated driver. Saves processing power and battery life since the second computing device ignores the telematics data. The drawing shows the block diagram of the telematics collection system. 100Telematics collection system106External computing device108Vehicle110Computing device114On-board computer | Please summarize the input |
Vehicular traffic alerts for avoidance of abnormal traffic conditionsMethods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.What is claimed is:
| 1. A computer-implemented method of generating a vehicle traffic alert, the method comprising:
detecting, via one or more processors, that an abnormal traffic condition exists in a vehicle operating environment;
generating, via the one or more processors, an electronic message regarding the abnormal traffic condition;
transmitting the electronic message to a smart infrastructure component within a proximity of the vehicle operating environment, wherein the smart infrastructure component analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition, the abnormal traffic condition having already occurred in the vehicle operating environment, wherein the type of anomalous condition is selected from at least one of a set of transient conditions or non-transient conditions, and wherein determining the type of anomalous condition comprises comparing sensor data with previously recorded data for the operating environment, and wherein the smart infrastructure component performs an action based upon the type of anomalous condition in order to modify the anomalous condition into an altered roadway condition; and
transmitting, via the one or more processors, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the altered roadway condition, to allow the nearby vehicle to avoid or approach the altered roadway condition.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the nearby vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1 further comprising updating, via the one or more processors, a vehicle-usage profile associated with the nearby vehicle based upon received telematics data regarding operation of the nearby vehicle, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 5. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 6. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 7. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 8. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the vehicle operating environment.
| 9. The computer-implemented method of claim 1, wherein the smart infrastructure component comprises a smart traffic light.
| 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 11. A computer system configured to generate a vehicle traffic alert, the computer system comprising one or more processors, the one or more processors configured to:
detect that an abnormal traffic condition exists in a vehicle operating environment;
generate an electronic message regarding the abnormal traffic condition;
transmit the electronic message to a smart infrastructure component within a proximity of the vehicle operating environment, wherein the smart infrastructure component analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition, the abnormal traffic condition having already occurred in the vehicle operating environment, wherein the type of anomalous condition is selected from at least one of a set of transient conditions or non-transient conditions, and wherein determining the type of anomalous condition comprises comparing sensor data with previously recorded data for the operating environment, and wherein the smart infrastructure component performs an action based upon the type of anomalous condition in order to modify the anomalous condition into an altered roadway condition; and
transmit the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the altered roadway condition, to allow the nearby vehicle to avoid or approach the altered roadway condition.
| 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the nearby vehicle.
| 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather, and the electronic message indicates a GPS location of the bad weather.
| 14. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 15. The computer system of claim 11, the system further configured to update a vehicle-usage profile associated with the nearby vehicle based upon received telematics data regarding operation of the nearby vehicle, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 16. The computer system of claim 11, wherein the one or more processors include one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 20. The computer system of claim 11, wherein the nearby vehicle travels to the vehicle operating environment. | The method involves detecting that an abnormal traffic condition exists in a vehicle operating environment, and generating an electronic message regarding the condition. The message is transmitted to a smart infrastructure component within a proximity of the environment. The component analyzes the message to determine a type of anomalous condition for the condition, where the component performs an action based upon the type of condition to modify the condition into an altered roadway condition, and transmits the message via wireless communication to alert a nearby vehicle of the altered condition to allow the vehicle to avoid or approach the roadway condition. An INDEPENDENT CLAIM is included for a computer system configured to generate a vehicle traffic alert. Computer-implemented method for generating a vehicle traffic alert. The data collected may be used to generate vehicle-usage profiles that more accurately reflect vehicle risk, or lack thereof, and facilitate more appropriate auto insurance pricing. The electronic message may then be transmitted through the vehicle's transceiver using a wireless communication to the nearby vehicle to alert the nearby vehicles of the abnormal traffic condition and to allow the neighboring vehicles to avoid the abnormally occurring traffic condition. The drawing shows a schematic diagram of a telematics collection system. | Please summarize the input |
Accident risk model determination using autonomous vehicle operating dataMethods and systems for evaluating the effectiveness of autonomous operation features of autonomous vehicles using an accident risk model are provided. According to certain aspects, an accident risk model may be determined using effectiveness information regarding autonomous operation features associated with a vehicle. The effectiveness information may indicate a likelihood of an accident for the vehicle and may include test data or actual loss data. Determining the likelihood of an accident may include determining risk factors for the features related to the ability of the features to make control decisions that successfully avoid accidents. The accident risk model may further include information regarding effectiveness of the features relative to location or operating conditions, as well as types and severity of accidents. The accident risk model may further be used to determine or adjust aspects of an insurance policy associated with an autonomous vehicle.What is claimed is:
| 1. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, the method comprising:
generating, by one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies, test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated;
receiving, by one or more processors, effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;
determining, by one or more processors, an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;
determining, by one or more processors, an accident risk model based upon the received effectiveness information and the determined indication of reliability;
determining, by one or more processors, an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and
causing, by one or more processors, information regarding all or a portion of the determined insurance policy for the vehicle to be presented to a customer for review by the customer via a display of a computing device associated with the customer.
| 2. The computer-implemented method of claim 1, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.
| 3. The computer-implemented method of claim 1, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.
| 4. The computer-implemented method of claim 1, further comprising:
storing, by a non-transient computer-readable medium, the accident risk model;
receiving, by one or more processors, a request to determine the insurance policy for the vehicle; and
accessing, by one or more processors, the accident risk model based upon the received request.
| 5. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology involves at least one of a vehicle self-braking functionality or a vehicle self-steering functionality.
| 6. The computer-implemented method of claim 1, wherein determining the insurance policy includes calculating at least one of the following based upon the autonomous or semi-autonomous vehicle technology and the accident risk model: an automobile insurance premium, a discount, or a reward.
| 7. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology is related to at least one of the following:
driver alertness monitoring;
driver responsiveness monitoring;
pedestrian detection;
artificial intelligence;
a back-up system;
a navigation system;
a positioning system;
a security system;
an anti-hacking measure;
a theft prevention system; or
remote vehicle location determination.
| 8. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology is related to at least one of the following:
a point of impact;
a type of road;
a time of day;
a weather condition;
a type of a trip;
a length of a trip;
a vehicle style;
a vehicle-to-vehicle communication; or
a vehicle-to-infrastructure communication.
| 9. The computer-implemented method of claim 1, wherein causing information regarding all or a portion of the determined insurance policy for the vehicle to be presented to the customer includes causing to be presented on the display a cost of automobile insurance coverage.
| 10. The computer-implemented method of claim 1, wherein determining the accident risk model includes determining at least one risk level associated with the autonomous or semi-autonomous vehicle technology based upon observed responses of the autonomous or semi-autonomous vehicle technology in other vehicles.
| 11. The computer-implemented method of claim 1, wherein determining the insurance policy for the vehicle includes at least one of generating a new insurance policy associated with the vehicle or updating an existing insurance policy associated with the vehicle.
| 12. The computer-implemented method of claim 1, wherein the accident risk model further accounts for an effect of one or more of the following on the effectiveness information: weather, road type, or vehicle type.
| 13. A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, comprising:
one or more processors;
one or more communication modules adapted to communicate data;
one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies to generate test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated, and wherein the test results are communicated to the one or more processors via the one or more communication modules; and
a program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to:
receive effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;
determine an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;
determine an accident risk model based upon the received effectiveness information and the determined indication of reliability;
determine an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and
cause, via the one or more communication modules, information regarding all or a portion of the determined insurance policy for the vehicle to be presented to a customer for review by the customer via a display of a computing device associated with the customer.
| 14. The computer system of claim 13, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.
| 15. The computer system of claim 13, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.
| 16. The computer system of claim 13, wherein the executable instructions further cause the computer system to:
store the accident risk model;
receive, via the one or more communication modules, a request to determine the insurance policy for the vehicle; and
access the accident risk model based upon the received request.
| 17. A tangible, non-transitory computer-readable medium storing instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology that, when executed by at least one processor of a computer system, cause the computer system to:
generate, using one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies, test data regarding results of virtual testing within the virtual test environment in which responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data are simulated;
receive effectiveness information regarding the autonomous or semi-autonomous vehicle technology, the effectiveness information including both (i) actual accident data associated with vehicles having the autonomous or semi-autonomous vehicle technology and (ii) the test data associated with the autonomous or semi-autonomous vehicle technology;
determine an indication of reliability of the autonomous or semi-autonomous vehicle technology based at least in part upon compatibility of a version of or an update to computer-readable instructions involved in implementation of part or all of the autonomous or semi-autonomous vehicle technology with one or more versions of the at least one additional autonomous or semi-autonomous vehicle technologies tested;
determine an accident risk model based upon the received effectiveness information and the determined indication of reliability;
determine an insurance policy for a vehicle equipped with the autonomous or semi-autonomous vehicle technology based at least in part upon the accident risk model; and
cause information regarding all or a portion of the determined insurance policy for the vehicle to be presented to a customer for review by the customer via a display of a computing device associated with the customer.
| 18. The tangible, non-transitory computer-readable medium of claim 17, wherein the accident risk model is associated with a likelihood that vehicles having the autonomous or semi-autonomous vehicle technology will be involved in vehicle accidents.
| 19. The tangible, non-transitory computer-readable medium of claim 17, wherein the accident risk model comprises a data structure containing entries associated with at least one of (1) the autonomous or semi-autonomous vehicle technology or (2) a likelihood of a vehicle accident.
| 20. The tangible, non-transitory computer-readable medium of claim 17, further comprising executable instructions further cause the computer system to:
store the accident risk model;
receive, via one or more communication modules, a request to determine the insurance policy for the vehicle; and
access the accident risk model based upon the received request. | The computer-based method involves generating test data regarding results of virtual testing. The effectiveness information regarding the autonomous or semi-autonomous vehicle technology is received. An indication of reliability of the autonomous or semi-autonomous vehicle technology is determined. An insurance policy is determined for a vehicle (108) equipped with the autonomous or semi-autonomous vehicle technology. The information regarding all or a portion of the determined insurance policy is caused for the vehicle to be presented to a customer for review. INDEPENDENT CLAIMS are included for the following:a computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology; anda tangible, non-transitory computer-readable medium storing instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology. Computer-based method of evaluating effectiveness of autonomous or semi-autonomous vehicle technology. The autonomous vehicle operation features either assist the vehicle operator to more safely or efficiently operate a vehicle or takes full control of vehicle operation under some or all circumstances. An automobile insurance premium is determined by evaluating how effectively the vehicle is able to avoid and mitigate crashes and the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence. The drawing shows a block diagram of a computer network, a computer server, a mobile device and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation and insurance processes. 100Autonomous vehicle insurance system102Front end component104Back end component108Vehicle110Mobile device | Please summarize the input |
Fully autonomous vehicle insurance pricingMethods and systems for determining risk associated with operation of fully autonomous vehicles are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including types and version of sensors, control systems, and software. This information may be used to determine a risk profile reflecting risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels may further be determined and used with the risk profile to determine a total risk level associated with operation of the vehicle by the autonomous operation features. The expected use levels may indicate expected vehicle use, as well as traffic, weather, or other conditions in which the vehicle is likely to operate. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.What is claimed is:
| 1. A computer system for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, the computer system comprising one or more processors, one or more transceivers coupled to the one or more processors, and one or more program memories coupled to the one or more processors and storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
receive sensor data associated with the vehicle;
determine a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data;
determine a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, and (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determine types of one or more sensors installed in the vehicle based upon the sensor data associated with the vehicle;
adjust the total risk level associated with autonomous operation of the vehicle based at least in part upon the types of sensors installed in the vehicle; and
cause one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 2. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions and with each of the one or more autonomous operation features engaged or disengaged based upon the log of usage data; and
adjust the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the estimated future usage or operation of the vehicle, either by time or mileage, the vehicle is predicted to be operated in each of the plurality of weather and road conditions with each of the one or more autonomous operation features engaged or disengaged.
| 3. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions; and
adjust the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is estimated to be operated in each of the plurality of weather and road conditions indicated by the usage data received in the log of usage data.
| 4. The system of claim 1, wherein:
the risk profile associated with autonomous operation of the vehicle is based at least in part upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test results include responses of the test units to test inputs corresponding to test scenarios, the test scenarios include vehicle operation with an autonomous feature engaged during each of the plurality of weather and road conditions; and
the test results are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 5. The system of claim 1, wherein the risk profile associated with autonomous operation of the vehicle is based at least in part upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in each of the plurality of weather and road conditions.
| 6. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive a request for a quote of a premium associated with a vehicle insurance policy via wireless communication transmitted by the customer computing device;
determine a premium associated with the vehicle insurance policy based upon the total risk level; and
present an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 7. The system of claim 1, wherein the log of usage data regarding the one or more autonomous operation features includes a version of autonomous operation feature control software that is currently installed on the vehicle or in an autonomous operation feature system mounted on the vehicle.
| 8. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive information regarding a type and version of the one or more autonomous operation features; and
update the total risk level associated with autonomous operation of the vehicle based upon the type and version of the one or more autonomous operation features.
| 9. The system of claim 1, wherein the one or more autonomous operation features include a vehicle-to-vehicle (V2V) wireless communication capability, and wherein the executable instructions further cause the one or more processors to:
receive telematics data generated or broadcast from other vehicles; and
generate and display alternate routes based upon the telematics data received to facilitate safer vehicle travel and avoidance of bad weather, traffic, or road conditions.
| 10. A computer-implemented method for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, comprising:
determining, by one or more processors, a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receiving, at the one or more processors or an associated transceiver, a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission transmitted from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
determining, by the one or more processors, a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data;
determining, by the one or more processors, a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, and (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determining, via the one or more processors, types of one or more sensors installed in the vehicle based upon sensor data received from the vehicle; and
adjusting, via the one or more processors, the total risk level associated with autonomous operation of the vehicle based at least in part upon the types of sensors installed in the vehicle; and
causing, by the one or more processors, one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 11. The method of claim 10, the method comprising:
estimating future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions and with each of the one or more autonomous operation features engaged or disengaged; and
adjusting, via the one or more processors, the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is operated in each of the plurality of weather and road conditions with each of the one or more autonomous operation features engaged or disengaged indicated by the usage data received in the log of usage data.
| 12. The method of claim 10, the method comprising:
estimating future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions; and
adjusting, via the one or more processors, the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the amount of time or miles that the vehicle is expected to be operated in the future in each of the plurality of weather and road conditions for a given time period.
| 13. The method of claim 10, wherein:
the risk profile associated with autonomous operation of the vehicle is based at least in part upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test results include responses of the test units to test inputs corresponding to test scenarios, the test scenarios including vehicle operation with an autonomous feature engaged during each of the plurality of weather and road conditions; and
the test results are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 14. The method of claim 10, wherein the risk profile associated with autonomous operation of the vehicle is based at least in part upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in each of the plurality of weather and road conditions.
| 15. The method of claim 10, further comprising:
receiving, at the one or more processors or the associated transceiver, a request for a quote of a premium associated with a vehicle insurance policy via wireless communication transmitted by the customer computing device;
determining, by one or more processors, a premium associated with the vehicle insurance policy based upon the total risk level; and
presenting, by one or more processors, an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 16. The method of claim 10, the method further comprising:
receiving, via the one or more processors or the associated transceiver, information regarding a type and version of the one or more autonomous operation features; and
updating the total risk level associated with autonomous operation of the vehicle, via the one or more processors, based upon the type and version of the one or more autonomous operation features.
| 17. The method of claim 10, wherein the autonomous operation feature is a vehicle-to-vehicle (V2V) wireless communication capability, and the method comprises:
receiving, via one or more vehicle-mounted processors or associated transceiver, telematics data generated or broadcast from other vehicles; and
generating and displaying alternate routes, via the one or more vehicle-mounted processors, based upon the telematics data received to facilitate safer vehicle travel and avoidance of bad weather, traffic, or road conditions.
| 18. A computer system for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, the system comprising one or more processors, one or more transceivers coupled to the one or more processors, and one or more program memories coupled to the one or more processors and storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
determine from analysis of the usage data received in the log of usage data a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determine from analysis of the usage data received in the log of usage data, or from analysis of other vehicle or telematics data received from the vehicle or mobile device, an average amount of time or miles that the vehicle operator operates the vehicle during each of the plurality of weather and road operating conditions for a period of time;
determine a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions, and (c) the average amount of time or miles that the vehicle operator operates the vehicle during each of the plurality of weather and road operating conditions for the period of time to facilitate more accurate risk assessment and auto insurance pricing;
determine types of one or more sensors installed in the vehicle based upon sensor data received from the vehicle; and
adjust the total risk level associated with autonomous operation of the vehicle based at least in part upon the types of sensors installed in the vehicle; and
cause one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle. | The computer system comprises one or more processors (162), transceivers coupled to processors, and program memories (160) coupled to the processors and storing executable instructions that cause the one or more processors to determine a risk profile associated with operation of the vehicle that includes multiple risk levels associated with operation of the vehicle under multiple weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and under multiple weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged. A log of usage data regarding previous use of the one or more autonomous operation features of the vehicle is received by a vehicle operator during multiple weather and road conditions, via wireless communication or data transmission from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, where the mobile device (110) generates the log of usage data from the data received from the on-board computer during a vehicle trip. The log of usage data includes timestamp indicating a beginning of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with one or more autonomous operation features when engaged, and where the log of usage data further includes current weather and road conditions during the vehicle trip, receive sensor data associated with the vehicle, determine multiple expected use levels of the vehicle during multiple weather and road operating conditions, where the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of multiple weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data. The total risk level associated with overall operation of the vehicle based a portion upon the determined risk profile, and expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of multiple weather and road operating conditions are determine. The determine types of one or more sensors installed in the vehicle based upon the sensor data associated with the vehicle, adjusting the total risk level associated with autonomous operation of the vehicle based in portion upon the types of sensors installed in the vehicle. An INDEPENDENT CLAIM is included for a computer-implemented method for monitoring usage of vehicle having autonomous operation features for controlling the vehicle, which involves:determining, by one or more processors, a risk profile associated with operation of the vehicle that includes multiple risk levels associated with operation of the vehicle under multiple weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and under multiple weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;receiving, at the one or more processors or an associated transceiver, a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during multiple weather and road conditions, via wireless communication or data transmission transmitted from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, where the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and where the log of usage data further includes current weather and road conditions during the vehicle trip;determining, by the one or more processors, multiple expected use levels of the vehicle during multiple weather and road operating conditions, where the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of multiple weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data;determining, by the one or more processors, a total risk level associated with overall operation of the vehicle based portion upon the determined risk profile, and determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of multiple weather and road operating conditions;determining, via the one or more processors, types of one or more sensors installed in the vehicle based upon sensor data received from the vehicle;adjusting, via the one or more processors, total risk level associated with autonomous operation of the vehicle based portion upon the types of sensors installed in the vehicle; andcausing, by the one or more processors, one or more of the following actions to be performed based upon the determined total risk level to adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle. Computer system for monitoring usage of vehicle having one or more autonomous operation features for controlling vehicle. The computer system allows to monitor the driving experience and/or usage of the autonomous or semi-autonomous vehicle technology, small time frames, and/or periodically to provide feedback to the driver, insurance provider, and/or adjust insurance policies or premiums, determine the automobile insurance premium by evaluating how effectively the vehicle to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence. The drawing shows a block diagram of a computer system 102Front End Components110Mobile device160Program memories162Processors165Address/data Bus | Please summarize the input |
Autonomous vehicle technology effectiveness determination for insurance pricingMethods and systems for determining the effectiveness of one or more autonomous (and/or semi-autonomous) operation features of a vehicle are provided. According to certain aspects, information regarding autonomous operation features of the vehicle may be used to determine an effectiveness metric indicative of the ability of each autonomous operation feature to avoid or mitigate accidents or other losses. The information may include operating data from the vehicle or other vehicles having similar autonomous operation features, test data, or loss data from other vehicles. The determined effectiveness metric may then be used to determine part or all of an insurance policy, which may be reviewed by an insured and updated based upon the effectiveness metric.What is claimed is:
| 1. A computer-implemented method for evaluating a vehicle having a plurality of autonomous or semi-autonomous vehicle technologies, the method comprising:
implementing, by a test computing system, the plurality of autonomous or semi-autonomous vehicle technologies within a virtual test environment;
presenting, by the test computing system, virtual test sensor data to the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
in response to the virtual test sensor data generating, by the test computing system, test responses of the plurality of autonomous or semi-autonomous vehicle technologies;
based upon the test responses, determining, by the test computing system, an effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies, wherein the effectiveness metric indicates a combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies;
receiving, by a computing system, an indication of the vehicle having the plurality of autonomous or semi-autonomous vehicle technologies; and
updating, by the computing system, an insurance policy associated with the vehicle based upon the determined effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies.
| 2. The computer-implemented method of claim 1, wherein:
the plurality of autonomous or semi-autonomous vehicle technologies includes an updated version of at least one of the plurality of autonomous or semi-autonomous vehicle technologies; and
determining the effectiveness metric includes determining an update to the effectiveness metric based at least in part upon a compatibility of the updated version of the at least one of the plurality of autonomous or semi-autonomous vehicle technologies with at least another one of the plurality of autonomous or semi-autonomous vehicle technologies.
| 3. The computer-implemented method of claim 2, wherein determining the update to the effectiveness metric includes determining a change in accident avoidance effectiveness for the updated version of the at least one of the plurality of autonomous or semi-autonomous vehicle technologies.
| 4. The computer-implemented method of claim 1, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 5. The computer-implemented method of claim 1, wherein the operating conditions are associated with one or more of: a road type, a time of day, or a weather condition.
| 6. The computer-implemented method of claim 1, wherein:
determining the effectiveness metric includes generating a plurality of effectiveness metrics associated with a plurality of vehicle types;
the indication of the vehicle includes an indication of a vehicle type of the vehicle; and
updating the insurance policy associated with the vehicle is further based upon a corresponding effectiveness metric of the plurality of effectiveness metrics that is associated with the vehicle type of the vehicle.
| 7. The computer-implemented method of claim 1, wherein:
the plurality of test scenarios include test scenarios associated with points of impact during virtual vehicle collisions; and
the effectiveness metric further indicates the combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies to mitigate damages during the virtual vehicle collisions.
| 8. A computer system for evaluating a vehicle having a plurality of autonomous or semi-autonomous vehicle technologies, the system comprising:
a test computing system including a processor and a memory storing executable instructions that, when executed by the processor, cause the test computing system to:
implement the plurality of autonomous or semi-autonomous vehicle technologies within a virtual test environment;
present virtual test sensor data to the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
in response to the virtual test sensor data, generate test responses of the plurality of autonomous or semi-autonomous vehicle technologies; and
based upon the test responses, determining an effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies, wherein the effectiveness metric indicates a combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies; and
a computing system including a processor and a memory storing executable instructions that, when executed by the processor, cause the computing system to:
receive an indication of the vehicle having the plurality of autonomous or semi-autonomous vehicle technologies; and
update an insurance policy associated with the vehicle based upon the determined effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies.
| 9. The computer system of claim 8, wherein:
the plurality of autonomous or semi-autonomous vehicle technologies includes an updated version of at least one of the plurality of autonomous or semi-autonomous vehicle technologies; and
the executable instructions that cause the test computing system to determine the effectiveness metric further cause the test computing system to determine an update to the effectiveness metric based at least in part upon a compatibility of the updated version of the at least one of the plurality of autonomous or semi-autonomous vehicle technologies with at least another one of the plurality of autonomous or semi-autonomous vehicle technologies.
| 10. The computer system of claim 9, wherein the executable instructions that cause the test computing system to determine the update to the effectiveness metric further cause the test computing system to determine a change in accident avoidance effectiveness for the updated version of the at least one of the plurality of autonomous or semi-autonomous vehicle technologies.
| 11. The computer system of claim 8, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 12. The computer system of claim 8, wherein the operating conditions are associated with one or more of: a road type, a time of day, or a weather condition.
| 13. The computer system of claim 8, wherein:
the executable instructions that cause the test computing system to determine the effectiveness metric further cause the test computing system to generate a plurality of effectiveness metrics associated with a plurality of vehicle types;
the indication of the vehicle includes an indication of a vehicle type of the vehicle; and
the executable instructions that cause the computing system to update the insurance policy associated with the vehicle further cause the computing system to update the insurance policy based upon a corresponding effectiveness metric of the plurality of effectiveness metrics that is associated with the vehicle type of the vehicle.
| 14. The computer system of claim 8, wherein:
the plurality of test scenarios include test scenarios associated with points of impact during virtual vehicle collisions; and
the effectiveness metric further indicates the combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies to mitigate damages during the virtual vehicle collisions.
| 15. A non-transitory computer-readable medium storing instructions for evaluating a vehicle having a plurality of autonomous or semi-autonomous vehicle technologies that, when executed by at least one processor of a computer system, cause the computer system to:
implement the plurality of autonomous or semi-autonomous vehicle technologies within a virtual test environment;
present virtual test sensor data to the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
in response to the virtual test sensor data, generate test responses of the plurality of autonomous or semi-autonomous vehicle technologies;
based upon the test responses, determine an effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies, wherein the effectiveness metric indicates a combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies;
receive an indication of the vehicle having the plurality of autonomous or semi-autonomous vehicle technologies; and
update an insurance policy associated with the vehicle based upon the determined effectiveness metric for the plurality of autonomous or semi-autonomous vehicle technologies.
| 16. The computer-readable medium of claim 15, wherein:
the plurality of autonomous or semi-autonomous vehicle technologies includes an updated version of at least one of the plurality of autonomous or semi-autonomous vehicle technologies; and
the executable instructions that cause the computer system to determine the effectiveness metric further cause the computer system to determine an update to the effectiveness metric based at least in part upon a compatibility of the updated version of the at least one of the plurality of autonomous or semi-autonomous vehicle technologies with at least another one of the plurality of autonomous or semi-autonomous vehicle technologies.
| 17. The computer-readable medium of claim 15, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 18. The computer-readable medium of claim 15, wherein the operating conditions are associated with one or more of: a road type, a time of day, or a weather condition.
| 19. The computer-readable medium of claim 15,
the executable instructions that cause the computer system to determine the effectiveness metric further cause the computer system to generate a plurality of effectiveness metrics associated with a plurality of vehicle types;
the indication of the vehicle includes an indication of a vehicle type of the vehicle; and
the executable instructions that cause the computer system to update the insurance policy associated with the vehicle further cause the computer system to update the insurance policy based upon a corresponding effectiveness metric of the plurality of effectiveness metrics that is associated with the vehicle type of the vehicle.
| 20. The computer-readable medium of claim 15,
the plurality of test scenarios include test scenarios associated with points of impact during virtual vehicle collisions; and
the effectiveness metric further indicates the combined reliability of operating the vehicle by using different combinations of the plurality of autonomous or semi-autonomous vehicle technologies to mitigate damages during the virtual vehicle collisions. | The method involves generating an effectiveness metric associated with autonomous or semi-autonomous vehicle technologies based upon test responses by processors of a test computing system. An indication of a vehicle (108) including the autonomous or semi-autonomous vehicle technologies is received by the processors of a computing system. An insurance policy associated with the vehicle is updated based upon the effectiveness metric associated with the autonomous or semi-autonomous vehicle technologies by the processors of the computing system. INDEPENDENT CLAIMS are also included for the following:a computer system for evaluating effectiveness of autonomous or semi-autonomous vehicle technologies for controlling a vehicle to avoid or mitigate vehicle accidentsa tangible, non-transitory computer-readable medium comprising a set of instructions for evaluating effectiveness of autonomous or semi-autonomous vehicle technologies for controlling a vehicle to avoid or mitigate vehicle accidents. Method for evaluating effectiveness of autonomous or semi-autonomous vehicle technologies for controlling a vehicle i.e. automobile, to avoid or mitigate vehicle accidents. The method enables allows an insurance provider to adjust or update insurance policies, premiums, rates, discounts, and/or other insurance-related items based upon a smart equipment warning functionality that can alert drivers of a vehicle equipment or a vehicle safety equipment that can need replaced or repaired, and thus reducing collision risk. The method enables allows a vehicle operator to maximize effectiveness of an autonomous operation feature, maximize vehicle insurance coverage, and/or minimize vehicle insurance expense. The drawing shows a schematic block diagram of a computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes. 100Autonomous vehicle insurance system102Front-end components108Vehicle110Mobile device120Sensors130Network135Link155Controller164RAM | Please summarize the input |
Accident risk model determination using autonomous vehicle operating dataMethods and systems for evaluating the effectiveness of autonomous operation features of autonomous vehicles using an accident risk model are provided. According to certain aspects, an accident risk model may be determined using effectiveness information regarding autonomous operation features associated with a vehicle. The effectiveness information may indicate a likelihood of an accident for the vehicle and may include test data or actual loss data. Determining the likelihood of an accident may include determining risk factors for the features related to the ability of the features to make control decisions that successfully avoid accidents. The accident risk model may further include information regarding effectiveness of the features relative to location or operating conditions, as well as types and severity of accidents. The accident risk model may further be used to determine or adjust aspects of an insurance policy associated with an autonomous vehicle.The invention claimed is:
| 1. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, the method comprising:
presenting, by the one or more processors, virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within a virtual test environment;
generating, by the one or more processors, test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generating, by the one or more processors, an accident risk model indicating one or more risk levels for vehicles associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receiving, at the one or more processors, actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment, the actual accident data comprising data collected by a vehicle sensor; and
adjusting, by the one or more processors, the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels.
| 2. The computer-implemented method of claim 1, the method further comprising:
identifying, by the one or more processors, a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and
generating or updating, by the one or more processors, an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.
| 3. The computer-implemented method of claim 2, further comprising:
causing, by the one or more processors, information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle via a display of a customer computing device for review.
| 4. The computer-implemented method of claim 1, wherein:
generating the test responses includes generating test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology.
| 5. The computer-implemented method of claim 4, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 6. The computer-implemented method of claim 1, wherein generating the accident risk model includes determining the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 7. The computer-implemented method of claim 1, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 8. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology involves at least one of a vehicle self-braking functionality or a vehicle self-steering functionality.
| 9. A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, comprising:
one or more processors;
one or more program memories coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment;
generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generate an accident risk model indicating one or more risk levels for vehicles associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment, the actual accident data comprising data collected by a vehicle sensor; and
adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level.
| 10. The computer system of claim 9, wherein the executable instructions further cause the computer system to:
identify a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and
generate or update an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.
| 11. The computer system of claim 9, wherein:
the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology.
| 12. The computer system of claim 11, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 13. The computer system of claim 9, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 14. The computer system of claim 9, wherein the executable instructions further cause the computer system to:
communicate to a customer computing device, via a communication network, information regarding all or a portion of an insurance policy to be presented to a customer associated with the customer vehicle for review via a display of the customer computing device.
| 15. The computer system of claim 9, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 16. A tangible, non-transitory computer-readable medium storing executable instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology that, when executed by at least one processor of a computer system, cause the computer system to:
present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment;
generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generate an accident risk model indicating one or more risk levels for vehicles associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment, the actual accident data comprising data collected by a vehicle sensor; and
adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level.
| 17. The tangible, non-transitory computer-readable medium of claim 16, wherein:
the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology.
| 18. The tangible, non-transitory computer-readable medium of claim 17, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 19. The tangible, non-transitory computer-readable medium of claim 16, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 20. The tangible, non-transitory computer-readable medium of claim 16, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data. | The method involves generating test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data by the one or more processors (162). An accident risk model indicating one or more risk levels for vehicles (108) associated with the autonomous or semi-autonomous vehicle technology is generated based upon the test responses by the one or more processors. The actual accident data associated with accidents involving vehicles is generated using the autonomous or semi-autonomous vehicle technology in a non-test environment at the one or more processors, the actual accident data comprising data collected by a vehicle sensor. The accident risk model is adjusted based upon the actual accident data by adjusting one of the one or more risk levels by the one or more processors. INDEPENDENT CLAIMS are included for:A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology;Atangible, non-transitory computer-readable medium storing. Computer-implemented method for evaluating effectiveness of autonomous or semi-autonomous vehicle technology such as driverless operation, accident avoidance or collision warning systems. The autonomous vehicle operation features either assist the vehicle operator to more safely or efficiently operate a vehicle or may take full control of vehicle operation under some or all circumstances. The risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features is facilitated. The insurance premium for automobile insurance coverage or another cost associated with the insurance policy is presented through a display screen to a customer for review, acceptance, and/or approval. The drawing shows a block diagram of the computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes.108Vehicle 110Mobile device 114Client device 130Network 162Processor | Please summarize the input |
Fully autonomous vehicle insurance pricingMethods and systems for determining risk associated with operation of fully autonomous vehicles are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including types and version of sensors, control systems, and software. This information may be used to determine a risk profile reflecting risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels may further be determined and used with the risk profile to determine a total risk level associated with operation of the vehicle by the autonomous operation features. The expected use levels may indicate expected vehicle use, as well as traffic, weather, or other conditions in which the vehicle is likely to operate. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.What is claimed is:
| 1. A computer system for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, the computer system comprising one or more processors, one or more transceivers coupled to the one or more processors, and one or more program memories coupled to the one or more processors and storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
receive sensor data associated with the vehicle;
determine a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data;
determine a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, and (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determine types of one or more sensors installed in the vehicle based upon the sensor data associated with the vehicle; and
adjust the total risk level associated with autonomous operation of the vehicle based at least in part upon the types of sensors in the vehicle.
| 2. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions and with each of the one or more autonomous operation features engaged or disengaged based upon the log of usage data; and
adjust the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the estimated future usage or operation of the vehicle, either by time or mileage, the vehicle is predicted to be operated in each of the plurality of weather and road conditions with each of the one or more autonomous operation features engaged or disengaged.
| 3. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions; and
adjust the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is estimated to be operated in each of the plurality of weather and road conditions indicated by the usage data received in the log of usage data.
| 4. The method of claim 1, wherein:
the risk profile associated with autonomous operation of the vehicle is based at least in part upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test result data include responses of the test units to test inputs corresponding to test scenarios, the test scenarios include vehicle operation with an autonomous feature engaged during each of the plurality of weather and road conditions; and
the test result data are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 5. The system of claim 1, wherein the risk profile associated with autonomous operation of the vehicle is based at least in part upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in each of the plurality of weather and road conditions.
| 6. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
cause one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, or present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level.
| 7. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive a request for a quote of a premium associated with a vehicle insurance policy via wireless communication transmitted by the customer computing device;
determine a premium associated with the vehicle insurance policy based upon the total risk level; and
present an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 8. The system of claim 1, wherein the log of usage data regarding the one or more autonomous operation features includes a version of autonomous operation feature control software that is currently installed on the vehicle or in the autonomous operation feature system mounted on the vehicle.
| 9. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive information regarding a type and version of the one or more autonomous operation features; and
update the total risk level associated with autonomous operation of the vehicle based upon the type and version of the one or more autonomous operation features.
| 10. The system of claim 1, wherein the one or more autonomous operation features include a vehicle-to-vehicle (V2V) wireless communication capability, and wherein the executable instructions further cause the one or more processors to:
receive telematics data generated or broadcast from other vehicles; and
generate and display alternate routes based upon the telematics data received to facilitate safer vehicle travel and avoidance of bad weather, traffic, or road conditions.
| 11. A computer-implemented method for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, comprising:
determining, by one or more processors, a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receiving, at the one or more processors or an associated transceiver, a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission transmitted from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
determining, by the one or more processors, a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions as determined from processor analysis of the usage data received in the log of usage data;
determining, by the one or more processors, a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, and (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determining, via the one or more processors, types of one or more sensors installed in the vehicle based upon sensor data received from the vehicle; and
adjusting, via the one or more processors, the total risk level associated with autonomous operation of the vehicle based at least in part upon the types of sensors installed in the vehicle.
| 12. The computer-implemented method of claim 11, the method comprising:
estimating future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions and with each of the one or more autonomous operation features engaged or disengaged; and
adjusting, via the one or more processors, the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is operated in each of the plurality of weather and road conditions with each of the one or more autonomous operation features engaged or disengaged indicated by the usage data received in the log of usage data.
| 13. The computer-implemented method of claim 11, the method comprising:
estimating future usage or operation of the vehicle, either by time or mileage, during each of the plurality of weather and road conditions; and
adjusting, via the one or more processors, the total risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the amount of time or miles that the vehicle is expected to be operated in the future in each of the plurality of weather and road conditions for a given time period.
| 14. The computer-implemented method of claim 11, wherein:
the risk profile associated with autonomous operation of the vehicle is based at least in part upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test result data include responses of the test units to test inputs corresponding to test scenarios, the test scenarios including vehicle operation with an autonomous feature engaged during each of the plurality of weather and road conditions; and
the test result data are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 15. The computer-implemented method of claim 11, wherein the risk profile associated with autonomous operation of the vehicle is based at least in part upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in each of the plurality of weather and road conditions.
| 16. The computer-implemented method of claim 11, the method further comprising:
adjusting, via the one or more processors, an insurance policy associated with the vehicle.
| 17. The method of claim 11, further comprising:
receiving, at the one or more processors or the associated transceiver, a request for a quote of a premium associated with a vehicle insurance policy via wireless communication transmitted by the customer computing device;
determining, by one or more processors, a premium associated with the vehicle insurance policy based upon the total risk level; and
presenting, by one or more processors, an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 18. The method of claim 11, the method further comprising:
receiving, via the one or more processors or the associated transceiver, information regarding a type and version of the one or more autonomous operation features; and
updating the total risk level associated with autonomous operation of the vehicle, via the one or more processors, based upon the type and version of the one or more autonomous operation features.
| 19. The method of claim 11, wherein the autonomous operation feature is a vehicle-to-vehicle (V2V) wireless communication capability, and the method comprises:
receiving, via one or more vehicle-mounted processors or associated transceiver, telematics data generated or broadcast from other vehicles; and
generating and displaying alternate routes, via the one or more vehicle-mounted processors, based upon the telematics data received to facilitate safer vehicle travel and avoidance of bad weather, traffic, or road conditions.
| 20. A computer system for monitoring usage of a vehicle having one or more autonomous operation features for controlling the vehicle, the system comprising one or more processors, one or more transceivers coupled to the one or more processors, and one or more program memories coupled to the one or more processors and storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle that includes a plurality of risk levels associated with operation of the vehicle (i) under a plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle engaged, and (ii) under the plurality of weather and road operating conditions with the one or more autonomous operation features of the vehicle disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features of the vehicle by a vehicle operator during the plurality of weather and road conditions, via wireless communication or data transmission from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current weather and road conditions during the vehicle trip;
determine from analysis of the usage data received in the log of usage data a plurality of expected use levels of the vehicle during the plurality of weather and road operating conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions;
determine from analysis of the usage data received in the log of usage data, or from analysis of other vehicle or telematics data received from the vehicle or mobile device, an average amount of time or miles that the vehicle operator operates the vehicle during each of the plurality of weather and road operating conditions for a period of time;
determine a total risk level associated with overall operation of the vehicle based at least in part upon (a) the determined risk profile, (b) the determined expected use levels that indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during each of the plurality of weather and road operating conditions, and (c) the average amount of time or miles that the vehicle operator operates the vehicle during each of the plurality of weather and road operating conditions for the period of time to facilitate more accurate risk assessment and auto insurance pricing;
determine types of one or more sensors installed in the vehicle based upon sensor data received from the vehicle; and
adjust the total risk associated with autonomous operation of the vehicle based at least in part upon the types of sensors installed in the vehicle. | The system has transceivers coupled to processors (162), and program memories (160) coupled to the processors and storing executable instructions that cause the processors to determine a total risk level associated with overall operation of a vehicle based upon a determined risk profile, and the determined expected use levels that indicate whether or not a vehicle operator is expected to engage or disengage autonomous operation features during each of weather and road operating conditions. The processors determine types of sensors installed in the vehicle based on the sensor data associated with the vehicle, and adjust the total risk levels associated with autonomous operation of the vehicle. An INDEPENDENT CLAIM is included for a method for monitoring usage of vehicle. Computer system for monitoring usage of a vehicle i.e. autonomous vehicle. Can also be used for a semi-autonomous vehicle and a driverless vehicle. The risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features can be facilitated. The driverless operation or accident avoidance can be achieved. The financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability can be provided. The drawing drawing shows the block diagram of an exemplary computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes.100Autonomous vehicle insurance system 104Back-end components 110Mobile device 130Network 140Server 146Database 160Program memory 162Processor 164RAM | Please summarize the input |
FULLY AUTONOMOUS VEHICLE INSURANCE PRICINGMethods and systems for determining risk associated with operation of fully autonomous vehicles are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including types and version of sensors, control systems, and software. This information may be used to determine a risk profile reflecting risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels may further be determined and used with the risk profile to determine a total risk level associated with operation of the vehicle by the autonomous operation features. The expected use levels may indicate expected vehicle use, as well as traffic, weather, or other conditions in which the vehicle is likely to operate. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.|1. A computer system for monitoring usage of a vehicle having one or more autonomous operation features, comprising one or more processors and one or more program memories storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle (i) under operating environment conditions with the one or more autonomous operation features engaged, and (ii) under operating environment conditions with the one or more autonomous operation features disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features by a vehicle operator during the operating environment conditions;
determine a plurality of expected use levels of the vehicle during the operating environment conditions, including whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during the operating environment conditions as determined from processor analysis of the log of usage data;
determine a risk level associated with operation of the vehicle based upon (a) the determined risk profile, and (b) the determined expected use levels; and
cause the one or more processors to automatically perform an action based upon the determined risk level, wherein the action includes one or more of: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined risk level to a reviewer via a display.
| 2. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle during the operating environment conditions and with each of the one or more autonomous operation features engaged or disengaged based upon the log of usage data; and
adjust the risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the estimated future usage or operation of the vehicle.
| 3. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
estimate future usage or operation of the vehicle, either by time or mileage, during the operating environment conditions; and
adjust the risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is estimated to be operated in the operating environment conditions as indicated by the log of usage data.
| 4. The method of claim 1, wherein:
the risk profile associated with autonomous operation of the vehicle is based upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test result data include responses of the test units to test inputs corresponding to test scenarios, the test scenarios include vehicle operation with an autonomous feature engaged during the operating environment conditions; and
the test result data are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 5. The system of claim 1, wherein the risk profile associated with autonomous operation of the vehicle is based upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in the operating environment conditions.
| 6. The system of claim 1, wherein receiving the log of usage data comprises receiving the log via wireless communication from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current operating environment conditions during the vehicle trip.
| 7. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive a request for a quote of a premium associated with a vehicle insurance policy via wireless communication;
determine a premium associated with the vehicle insurance policy based upon the risk level; and
present an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 8. The system of claim 1, wherein the log of usage data includes a version of autonomous operation feature control software that is currently installed on the vehicle or in the autonomous operation feature system mounted on the vehicle.
| 9. The system of claim 1, wherein the executable instructions further cause the one or more processors to:
receive information regarding a type and version of the one or more autonomous operation features; and
update the risk level associated with autonomous operation of the vehicle based upon the type and version of the one or more autonomous operation features.
| 10. The system of claim 1, wherein the one or more autonomous operation features include a vehicle-to-vehicle (V2V) wireless communication capability, and wherein the executable instructions further cause the one or more processors to:
receive telematics data from other vehicles; and
generate and display alternate routes based upon the received telematics data.
| 11. A computer-implemented method for use in connection with a vehicle having one or more autonomous operation features, comprising:
determining, by one or more processors, a risk profile associated with operation of the vehicle (i) under operating environment conditions with the one or more autonomous operation features engaged, and (ii) under the operating environment conditions with the one or more autonomous operation features disengaged;
receiving, at the one or more processors or an associated transceiver, a log of usage data regarding previous use of the one or more autonomous operation features by a vehicle operator during the operating environment conditions;
determining, by the one or more processors, a plurality of expected use levels of the vehicle during the operating environment conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during the operating environment conditions as determined from processor analysis of the log of usage data;
determining, by the one or more processors, a risk level associated with operation of the vehicle based upon (a) the determined risk profile, and (b) the determined expected use levels; and
causing the one or more processors to automatically perform an action based upon the determined total risk level, wherein the actions include one or more of: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined risk level to a reviewer via a display.
| 12. The computer-implemented method of claim 11, the method comprising:
estimating future usage or operation of the vehicle during the operating environment conditions and with the one or more autonomous operation features engaged or disengaged; and
adjusting, via the one or more processors, the risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) an amount of time or miles that the vehicle is operated in the operating environment conditions with the one or more autonomous operation features engaged or disengaged indicated by the log of usage data.
| 13. The computer-implemented method of claim 11, the method comprising:
estimating future usage or operation of the vehicle, either by time or mileage, during the operating environment conditions; and
adjusting, via the one or more processors, the risk level for the vehicle based upon (1) the determined risk profile, (2) the determined expected use levels, and (3) the amount of time or miles that the vehicle is expected to be operated in the future in the operating environment conditions.
| 14. The computer-implemented method of claim 11, wherein:
the risk profile associated with autonomous operation of the vehicle is based upon test result data generated from test units corresponding to the one or more autonomous operation features;
the test result data include responses of the test units to test inputs corresponding to test scenarios, the test scenarios including vehicle operation with an autonomous feature engaged during the operating environment conditions; and
the test result data are generated and recorded by the test units disposed within one or more test vehicles in response to sensor data from a plurality of sensors, and video recording devices, within the one or more test vehicles.
| 15. The computer-implemented method of claim 11, wherein the risk profile associated with autonomous operation of the vehicle is based upon actual losses associated with insurance policies covering a plurality of other vehicles having at least one of the one or more autonomous operation features, the actual losses incurred through vehicle operation in the operating environment conditions.
| 16. The computer-implemented method of claim 11, wherein receiving the log of usage data regarding comprises receiving the log via wireless communication from a mobile device of the vehicle operator in communication with an on-board computer of the vehicle, wherein the mobile device generates the log of usage data from the data received from the on-board computer during a vehicle trip, the log of usage data including: a timestamp indicating a beginning of the vehicle trip, a timestamp indicating an end of the vehicle trip, one or more timestamps associated with engagement or disengagement of the one or more autonomous operation features, and configuration data associated with the one or more autonomous operation features when engaged, and wherein the log of usage data further includes current operating environment conditions.
| 17. The method of claim 11, further comprising:
receiving, at the one or more processors or an associated transceiver, a request for a quote of a premium associated with a vehicle insurance policy via wireless communication;
determining, by one or more processors, a premium associated with the vehicle insurance policy based upon the risk level; and
presenting, by one or more processors, an option to purchase the vehicle insurance policy to the customer associated with the vehicle.
| 18. The method of claim 11, the method further comprising:
receiving, via the one or more processors or an associated transceiver, information regarding a type and version of the one or more autonomous operation features; and
updating the total level associated with autonomous operation of the vehicle, via the one or more processors, based upon the type and version of the one or more autonomous operation features.
| 19. The method of claim 11, wherein the autonomous operation feature is a vehicle-to-vehicle (V2V) wireless communication capability, and the method comprises:
receiving, via one or more vehicle-mounted processors or associated transceiver, telematics data from other vehicles; and
generating and displaying alternate routes, via the one or more vehicle-mounted processors, based upon the telematics data.
| 20. A computer system for monitoring usage of a vehicle having one or more autonomous operation features, the system comprising one or more processors and one or more program memories storing executable instructions that cause the one or more processors to:
determine a risk profile associated with operation of the vehicle (i) under a plurality of operating environment conditions with the one or more autonomous operation features engaged, and (ii) under the operating environment conditions with the one or more autonomous operation features disengaged;
receive a log of usage data regarding previous use of the one or more autonomous operation features during the operating environment conditions;
determine from analysis of the log of usage data a plurality of expected use levels of the vehicle during the operating environment conditions, wherein the expected use levels indicate whether or not the vehicle operator is expected to engage or disengage the one or more autonomous operation features during the operating environment conditions;
determine from analysis of the log of usage data, or from analysis of other vehicle or telematics data received from the vehicle or mobile device, an average amount of time or miles that the vehicle operator operates the vehicle during the operating environment conditions for a period of time;
determine a risk level associated with operation of the vehicle based upon (a) the determined risk profile, (b) the determined expected use levels, and (c) the average amount of time or miles that the vehicle operator operates the vehicle during the operating environment conditions for the period of time; and
cause the one or more processors to automatically perform an action based upon the determined total risk level, wherein the action includes one or more of: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display. | The computer system (100) has processors (162) and program memories (160) storing executable instructions that cause the processors to determine expected use levels of a vehicle (108) during operating environment conditions, including whether or not the vehicle operator is expected to engage or disengage the autonomous operation features during operating environment conditions as determined from processor analysis of log of usage data, and determine risk level associated with operation of the vehicle based upon determined risk profile, and determined expected use levels. The processors automatically perform an action based upon determined risk level. The action includes one or more of adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined risk level to a reviewer via a display. An INDEPENDENT CLAIM is included for a method for use in connection with a vehicle having one or more autonomous operation features. Computer system for monitoring usage of vehicle having autonomous operation features such as autonomous vehicle, semi-autonomous vehicle. The risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features can be facilitated. The driverless operation or accident avoidance can be achieved. The financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability can be provided. The drawing shows a block diagram of a computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes. 100Computer system 108Vehicle 110Mobile device 160Program memory 162Processor | Please summarize the input |
Accident risk model determination using autonomous vehicle operating dataMethods and systems for evaluating the effectiveness of autonomous operation features of autonomous vehicles using an accident risk model are provided. According to certain aspects, an accident risk model may be determined using effectiveness information regarding autonomous operation features associated with a vehicle. The effectiveness information may indicate a likelihood of an accident for the vehicle and may include test data or actual loss data. Determining the likelihood of an accident may include determining risk factors for the features related to the ability of the features to make control decisions that successfully avoid accidents. The accident risk model may further include information regarding effectiveness of the features relative to location or operating conditions, as well as types and severity of accidents. The accident risk model may further be used to determine or adjust aspects of an insurance policy associated with an autonomous vehicle.What is claimed is:
| 1. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, the method comprising:
implementing, by one or more processors, the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;
presenting, by the one or more processors, virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
generating, by the one or more processors, test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generating, by the one or more processors, an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receiving, at the one or more processors, actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;
adjusting, by the one or more processors, the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;
identifying, by the one or more processors, a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and
generating or updating, by the one or more processors, an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.
| 2. The computer-implemented method of claim 1, wherein:
generating the test responses includes generating test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and
the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.
| 3. The computer-implemented method of claim 2, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 4. The computer-implemented method of claim 1, wherein generating the accident risk model includes determining the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 5. The computer-implemented method of claim 1, further comprising:
causing, by the one or more processors, information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle via a display of a customer computing device for review.
| 6. The computer-implemented method of claim 1, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 7. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology involves at least one of a vehicle self-braking functionality or a vehicle self-steering functionality.
| 8. The computer-implemented method of claim 1, wherein the operating conditions are associated with one or more of the following: a road type, a time of day, or a weather condition.
| 9. A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, comprising:
one or more processors;
one or more program memories coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
implement the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;
present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generate an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;
adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;
identify a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and
generate or update an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.
| 10. The computer system of claim 9, wherein:
the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and
the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.
| 11. The computer system of claim 10, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 12. The computer system of claim 9, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 13. The computer system of claim 9, wherein the executable instructions further cause the computer system to:
communicate to a customer computing device, via a communication network, information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle for review via a display of the customer computing device.
| 14. The computer system of claim 9, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data.
| 15. A tangible, non-transitory computer-readable medium storing executable instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology that, when executed by at least one processor of a computer system, cause the computer system to:
implement the autonomous or semi-autonomous vehicle technology within a virtual test environment configured to simultaneously test multiple autonomous or semi-autonomous vehicle technologies;
present virtual test sensor data to the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment, wherein the virtual test sensor data simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
generate test responses of the autonomous or semi-autonomous vehicle technology implemented within the virtual test environment in response to the virtual test sensor data;
generate an accident risk model indicating one or more risk levels for vehicle accidents associated with the autonomous or semi-autonomous vehicle technology based upon the test responses;
receive actual accident data associated with accidents involving vehicles using the autonomous or semi-autonomous vehicle technology in a non-test environment;
adjust the accident risk model based upon the actual accident data by adjusting at least one of the one or more risk levels of the accident risk level;
identify a customer vehicle having the autonomous or semi-autonomous vehicle control technology; and
generate or update an insurance policy associated with the customer vehicle based upon the adjusted at least one of the one or more risk levels of the adjusted accident risk model.
| 16. The tangible, non-transitory computer-readable medium of claim 15, wherein:
the executable instructions that cause the computer system to generate the test responses cause the computer system to generate test responses relative to additional test responses of another autonomous or semi-autonomous vehicle technology; and
the one or more risk levels of the accident risk model are generated based in part upon compatibility of the test responses of the autonomous or semi-autonomous vehicle technology with the additional test responses of the other autonomous or semi-autonomous vehicle technology.
| 17. The tangible, non-transitory computer-readable medium of claim 16, wherein the compatibility of the test responses and the additional test responses is determined for a plurality of versions of the other autonomous or semi-autonomous vehicle technology.
| 18. The tangible, non-transitory computer-readable medium of claim 15, wherein the executable instructions that cause the computer system to generate the accident risk model further cause the computer system to determine the one or more risk levels based upon an effectiveness metric associated with the autonomous or semi-autonomous vehicle technology calculated from the test responses.
| 19. The tangible, non-transitory computer-readable medium of claim 15, further storing executable instructions that, when executed by at least one processor of the computer system, cause the computer system to:
cause information regarding all or a portion of the insurance policy to be presented to a customer associated with the customer vehicle via a display of a customer computing device for review.
| 20. The tangible, non-transitory computer-readable medium of claim 15, wherein the virtual test sensor data includes virtual test communication data simulating autonomous vehicle-to-vehicle communication data. | The method involves receiving actual accident data associated with accidents involving vehicles (108) at processors (162) using autonomous or semi-autonomous vehicle technology in a non-test environment, where the autonomous or semi-autonomous vehicle technology includes a vehicle self-braking functionality or vehicle self-steering functionality. An accident risk model is adjusted by the processor based on the actual accident data by adjusting one of the risk levels of the accident risk level. A customer vehicle including the autonomous or semi-autonomous vehicle control technology is identified by the processor. An insurance policy associated with the customer vehicle is generated or updated by the processor based on the adjusted risk level of the adjusted accident risk model. INDEPENDENT CLAIMS are also included for the following:a computer systema tangible, non-transitory computer-readable medium comprising a set of instructions for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology by a computer system during urban driving or motorway driving conditions. Method for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology e.g. vehicle self-braking functionality or vehicle self-steering functionality, by a computer system (all claimed) during urban driving or motorway driving conditions. The method enables assisting a vehicle operator to safely or efficiently operate the vehicle or to take full control vehicle operation by providing autonomous vehicle operation features. The method enables monitoring driving experience and/or usage of the autonomous or semi-autonomous vehicle technology and small time-frames in real-time and periodically providing feedback to a driver and an insurance provider and/or to adjust the insurance policies or premiums. The method enables determining vehicle insurance premium by effectively evaluating the vehicle to avoid and/or mitigate crashes and/or extent to which driver's control of the vehicle is enhanced or replaced by vehicle's software and artificial intelligence. The drawing shows a schematic block diagram of a computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes. 102Front end components104Back-end components108Accidents involving vehicles114Client device162Processor | Please summarize the input |
VEHICULAR TRAFFIC ALERTS FOR AVOIDANCE OF ABNORMAL TRAFFIC CONDITIONSMethods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.What is claimed is:
| 1. A computer-implemented method of analyzing abnormal traffic conditions, the method comprising:
determining, via one or more processors, a risk level of an abnormal traffic condition detected in a vehicle operating environment;
transmitting data comprising the abnormal traffic condition to a smart infrastructure component within a proximity of the vehicle operating environment, wherein the smart infrastructure component performs an action based upon a type of anomalous condition to modify the anomalous condition into an altered roadway condition with an adjusted risk level; and
transmitting, via the one or more processors, an electronic message to a nearby vehicle via wireless communication to alert the nearby vehicle of the altered roadway condition and to allow the nearby vehicle to determine whether to avoid or approach the altered roadway condition.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of a vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1 further comprising updating, via the one or more processors, a risk averse profile associated with an operator of the nearby vehicle based upon whether the nearby vehicle was operated in a manner to avoid or approach the altered roadway condition.
| 5. The computer-implemented method of claim 4, wherein the smart infrastructure component comprises a smart traffic light.
| 6. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 7. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 8. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 9. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the vehicle operating environment.
| 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 11. A computer system configured to analyze abnormal traffic conditions, the computer system comprising one or more processors, the one or more processors configured to:
determine, via one or more processors, a risk level of an abnormal traffic condition detected in a vehicle operating environment;
transmit data comprising the abnormal traffic condition to a smart infrastructure component within a proximity of the vehicle operating environment, wherein the smart infrastructure component performs an action based upon a type of anomalous condition to modify the anomalous condition into an altered roadway condition with an adjusted risk level; and
transmit, via the one or more processors, an electronic message to a nearby vehicle via wireless communication to alert the nearby vehicle of the altered roadway condition and to allow the nearby vehicle to determine whether to avoid or approach the altered roadway condition.
| 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of a vehicle.
| 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather, and the electronic message indicates a GPS location of the bad weather.
| 14. The computer system of claim 11, the system further configured to update, via the one or more processors, a risk averse profile associated with an operator of the nearby vehicle based upon whether the nearby vehicle was operated in a manner to avoid or approach the altered roadway condition.
| 15. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 16. The computer system of claim 11, wherein the one or more processors include one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 20. The computer system of claim 11, wherein the nearby vehicle travels to the vehicle operating environment. | The computer-implemented method involves determining a risk level of an abnormal traffic condition detected in a vehicle operating environment through several processors. Data comprising the abnormal traffic condition is transmitted to a smart infrastructure component (208) within a proximity of the vehicle operating environment. The smart infrastructure component performs an action based upon a type of anomalous condition to modify the anomalous condition into an altered roadway condition with an adjusted risk level. An electronic message is transmitted to a nearby vehicle (202a) through wireless communication to alert the nearby vehicle of the altered roadway condition and to allow the nearby vehicle to determine whether to avoid or approach the altered roadway condition through the processors. An INDEPENDENT CLAIM is included for a computer system configured to analyze abnormal traffic conditions. Computer-implemented method for analyzing abnormal traffic conditions such as an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or several other vehicles braking ahead of the vehicle. The data collected may be used to generate vehicle-usage profiles that more accurately reflect vehicle risk, or lack thereof, and facilitate more appropriate auto insurance pricing. The electronic message may then be transmitted through the vehicle's transceiver using a wireless communication to the nearby vehicle to alert the nearby vehicles of the abnormal traffic condition and to allow the neighboring vehicles to avoid the abnormally occurring traffic condition. The drawing shows a block diagram of the system that collects telematics and/or other data, and uses V2x wireless communication to broadcast the data collected to other vehicles, mobile devices, remote servers, and smart infrastructure. 200Notification system 201Network 202aNearby vehicle 203Direct rRadio link 208Smart infrastructure component | Please summarize the input |
Autonomous communication feature useMethods and systems for determining collision risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine collision risk levels for a plurality of features, which may be based upon test data regarding the features or actual collision data. Expected use levels and autonomous communication levels may further be determined and used with the collision risk levels to determine a total collision risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication.What is claimed is:
| 1. A computer-implemented method for determining collision risk of one or more autonomous operation features of a vehicle, comprising:
receiving, by an autonomous communication feature of the one or more autonomous operation features, autonomous vehicle-to-vehicle communication data from one or more additional vehicles operating within communication range of the vehicle;
controlling, by an on-board computer of the vehicle and the one or more autonomous operation features, operation of the vehicle using the one or more autonomous operation features and the received autonomous vehicle-to-vehicle communication data;
communicating, from the on-board computer of the vehicle to one or more processors of a server via a communication network, information regarding the one or more autonomous operation features of the vehicle, including information regarding the autonomous communication feature of the vehicle and a log of vehicle operation data;
receiving, at the one or more processors of the server from the on-board computer of the vehicle via the communication network, the information regarding the one or more autonomous operation features of the vehicle;
determining, by the one or more processors of the server, a plurality of collision risk levels associated with autonomous operation of the vehicle under a plurality of operating environments based upon the information regarding the one or more autonomous operation features;
determining, by the one or more processors of the server, a plurality of expected use levels of the vehicle based upon entries in the log of vehicle operation data, wherein the expected use levels are associated with the plurality of operating environments;
determining, by the one or more processors of the server, a plurality of autonomous communication levels within the plurality of operating environments associated with the plurality of expected use levels for the vehicle based upon locations and times associated with the operating environments during prior operation of the vehicle, wherein the autonomous communication levels indicate availability of each of a plurality of types of autonomous communication capability in other vehicles as a proportion of the other vehicles in corresponding operating environments that exhibit the types of autonomous communication capability;
determining, by the one or more processors of the server, a total collision risk level associated with operation of the vehicle based at least in part upon the determined collision risk levels, the determined expected use levels, and the determined autonomous communication levels; and
causing, by the one or more processors of the server, one or more of the following actions to be performed based upon the determined total collision risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total collision risk level to a reviewer via a display of a reviewer computing device to verify the determined total collision risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 2. The method of claim 1, wherein the autonomous communication levels further include information relating to one or more of the following: levels of autonomous communication with infrastructure, or frequency of autonomous communications between the vehicle and the other vehicles.
| 3. The method of claim 1, further comprising receiving, at one or more processors, information regarding previous use of the one or more autonomous operation features of the vehicle, and wherein the plurality of expected use levels are determined, at least in part, based upon the information regarding previous use of the one or more autonomous operation features.
| 4. The method of claim 3, wherein the information regarding previous use of the autonomous operation features includes information regarding previous use of the autonomous communication feature.
| 5. The method of claim 1, wherein the information regarding the one or more autonomous operation features of the vehicle is based upon (i) test results for test units corresponding to the one or more autonomous operation features, which test results include responses of the test units to test inputs corresponding to test scenarios, and (ii) actual collision data associated with a plurality of other vehicles having at least one of the one or more autonomous operation features.
| 6. The method of claim 1, wherein the total collision risk level is determined without reference to factors relating to collision risk associated with a vehicle operator.
| 7. The method of claim 1, further comprising:
receiving, at one or more processors, information regarding a vehicle operator; and
determining, by one or more processors, an operator collision-risk profile associated with vehicle operation by the vehicle operator;
wherein the total collision risk level is determined, at least in part, based upon the operator collision-risk profile.
| 8. A computer system for determining collision risk of one or more autonomous operation features of a vehicle, comprising:
one or more processors;
an autonomous communication feature of the one or more autonomous operation features, configured to receive autonomous vehicle-to-vehicle communication data from one or more additional vehicles operating within communication range of the vehicle;
an on-board computer within the vehicle, configured to control operation of the vehicle using the one or more autonomous operation features and the received autonomous vehicle-to-vehicle communication data;
one or more communication modules adapted to communicate data from the on-board computer to the one or more processors via a communication network; and
a program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to:
receive, via the communication network, information regarding the one or more autonomous operation features of the vehicle, including information regarding the autonomous communication feature of the vehicle and a log of vehicle operation data;
determine a plurality of collision risk levels associated with autonomous operation of the vehicle under a plurality of operating environments based upon the information regarding the one or more autonomous operation features;
determine a plurality of expected use levels of the vehicle based upon entries in the log of vehicle operation data, wherein the expected use levels are associated with the plurality of operating environments;
determine a plurality of autonomous communication levels within the plurality of operating environments associated with the plurality of expected use levels for the vehicle based upon locations and times associated with the operating environments during prior operation of the vehicle, wherein the autonomous communication levels indicate availability of each of a plurality of types of autonomous communication capability in other vehicles as a proportion of the other vehicles in corresponding operating environments that exhibit the types of autonomous communication capability;
determine a total collision risk level associated with operation of the vehicle based at least in part upon the determined collision risk levels, the determined expected use levels, and the determined autonomous communication levels; and
cause one or more of the following actions to be performed based upon the determined total collision risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total collision risk level to a reviewer via a display of a reviewer computing device to verify the determined total collision risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 9. The computer system of claim 8, wherein the autonomous communication levels further include information relating to one or more of the following: levels of autonomous communication with infrastructure, or frequency of autonomous communications between the vehicle and the other vehicles.
| 10. The computer system of claim 8, wherein the executable instructions further cause the computer system to receive information regarding previous use of the one or more autonomous operation features of the vehicle, and wherein the plurality of expected use levels are determined, at least in part, based upon the information regarding previous use of the one or more autonomous operation features.
| 11. The computer system of claim 10, wherein the information regarding previous use of the autonomous operation features includes information regarding previous use of the autonomous communication feature.
| 12. The computer system of claim 8, wherein the information regarding the one or more autonomous operation features of the vehicle is based upon (i) test results for test units corresponding to the one or more autonomous operation features, which test results include responses of the test units to test inputs corresponding to test scenarios, and (ii) actual collision data associated with a plurality of other vehicles having at least one of the one or more autonomous operation features.
| 13. The computer system of claim 8, wherein the total collision risk level is determined without reference to factors relating to collision risks associated with a vehicle operator.
| 14. The computer system of claim 8, wherein the executable instructions further cause the computer system to:
receive information regarding a vehicle operator; and
determine an operator collision-risk profile associated with vehicle operation by the vehicle operator;
wherein the total collision risk level is determined, at least in part, based upon the operator collision-risk profile.
| 15. A tangible, non-transitory computer-readable medium storing instructions for determining collision risk of one or more autonomous operation features of a vehicle, when executed by at least one processor of a computer system, cause the computer system to:
receive autonomous vehicle-to-vehicle communication data from one or more additional vehicles operating within communication range of the vehicle by an autonomous communication feature of the one or more autonomous operation features;
control operation of the vehicle using the one or more autonomous operation features and the received autonomous vehicle-to-vehicle communication data by an on-board computer of the vehicle and the one or more autonomous operation features;
communicate information regarding the one or more autonomous operation features of the vehicle, including information regarding the autonomous communication feature of the vehicle and a log of vehicle operation data, from the on-board computer of the vehicle to a server via a communication network;
receive the information regarding the one or more autonomous operation features of the vehicle at the server from the on-board computer
determine a plurality of collision risk levels associated with autonomous operation of the vehicle under a plurality of operating environments based upon the information regarding the one or more autonomous operation features;
determine a plurality of expected use levels of the vehicle based upon entries in the loci of vehicle operation data, wherein the expected use levels are associated with the plurality of operating environments;
determine a plurality of autonomous communication levels within the plurality of operating environments associated with the plurality of expected use levels for the vehicle based upon locations and times associated with the operating environments during prior operation of the vehicle, wherein the autonomous communication levels indicate availability of each of a plurality of types of autonomous communication capability in other vehicles as a proportion of the other vehicles in corresponding operating environments that exhibit the types of autonomous communication capability;
determine a total collision risk level associated with operation of the vehicle based at least in part upon the determined collision risk levels, the determined expected use levels, and the determined autonomous communication levels; and
cause one or more of the following actions to be performed based upon the determined total collision risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total collision risk level to a reviewer via a display of a reviewer computing device to verify the determined total collision risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 16. The tangible, non-transitory computer-readable medium of claim 15, wherein the autonomous communication levels further include information relating to one or more of the following: levels of autonomous communication with infrastructure, or frequency of autonomous communications between the vehicle and the other vehicles.
| 17. The tangible, non-transitory computer-readable medium of claim 15, further comprising executable instructions that, when executed by at least one processor of a computer system, cause the computer system to receive information regarding previous use of the one or more autonomous operation features of the vehicle, and wherein the plurality of expected use levels are determined, at least in part, based upon the information regarding previous use of the one or more autonomous operation features.
| 18. The tangible, non-transitory computer-readable medium of claim 17, wherein the information regarding previous use of the autonomous operation features includes information regarding previous use of the autonomous communication feature. | The method involves determining a total collision risk level associated with operation of a vehicle (108) based upon collision risk levels, expected use levels, and autonomous communication levels by processors (162) of a server (140). Following actions are caused to be performed based upon the determined total collision risk level by the processors of the server such that an insurance policy associated with the vehicle is adjusted, a coverage level associated with the insurance policy is determined, information regarding the determined total collision risk level is presented to a reviewer by a display of a reviewer computing device to verify the determined total collision risk level, or determination to a customer by a display of a customer computing device for review of adjustment to the insurance policy associated with the vehicle is presented. INDEPENDENT CLAIMS are also included for the following:a computer system for determining collision risk of autonomous operation features of a vehiclea tangible non-transitory computer-readable medium comprising a set of instructions for determining collision risk of autonomous operation features of a vehicle. Method for determining collision risk of autonomous operation features of a vehicle i.e. smart car. The method enables allowing near real-time uploads and downloads of information and periodic uploads and downloads of information. The method enables providing autonomous vehicle operation features to assist the vehicle operator to safely or efficiently operate the vehicle or take full control of vehicle operation under part or all circumstances. The method enables monitoring driving experience and/or usage of the autonomous or semi-autonomous vehicle technology in real time, small timeframes, and/or periodically to provide feedback to the driver, insurance provider, and/or adjust insurance policies or premiums. The method enables determining automobile insurance premium by effectively evaluating the vehicle to avoid and/or mitigate crashes and/or extent to which driver's control of the vehicle is enhanced or replaced by vehicle's software and artificial intelligence. The drawing shows a schematic block diagram of a computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes. 100Autonomous vehicle insurance system102Front-end components104Back-end components108Vehicle130Network140Server162Processors | Please summarize the input |
Accident fault determination for autonomous vehiclesMethods and systems for determining fault for an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features are provided. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine fault for a vehicle accident, such as a collision. The operating data may include information regarding use of the features at the time of the accident and may further be used to determine an allocation of fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to determine and/or adjust coverage levels for an insurance policy associated with the vehicle. The allocation of fault may further be used to adjust risk levels or profiles associated with the vehicle operator or with the autonomous operation features.What is claimed is:
| 1. A computer system for reconstructing a vehicle crash, the computer system comprising one or more processors, one or more transceivers coupled to the one or more processors, and one or more program memories coupled to the one or more processors and storing executable instructions that cause the one or more processors to:
receive vehicle operating data for a vehicle having one or more autonomous operation features for controlling the vehicle, the vehicle operating data being generated and transmitted by an on-board computer or mobile device using wireless communication or data transmission, wherein the vehicle operating data includes:
(i) sensor data from one or more vehicle-mounted sensors associated with the one or more autonomous operation features, the sensor data also indicating a configuration or setting of each autonomous operation feature before and during the vehicle crash; and
(ii) a recorded log of decisions made by the one or more autonomous operation features, and commands sent from the on-board computer to control components to operate the vehicle, before and during the vehicle crash;
receive an indication of an accident involving the vehicle, or otherwise determine that the vehicle has been involved in the accident based upon processor analysis of the (i) sensor data and (ii) recorded log received;
generate a crash reconstruction representing a sequence of events involved in the accident by automatically determining, for each of a plurality of times in the sequence of events, each of the following: (i) a location of the vehicle based upon the sensor data, (ii) a location of an obstruction involved in the accident based upon the sensor data, and (iii) a movement of the vehicle based upon the decisions in the recorded log;
determine an allocation of fault for the accident for each of the one or more autonomous operation features based at least in part upon the crash reconstruction; and
cause one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 2. The computer system of claim 1, wherein the one or more processors analyze the vehicle operating data received to determine an extent of vehicle damage, or a cost to repair the damage or replace part or all of the vehicle, the vehicle operating data including video or image data.
| 3. The computer system of claim 1, wherein determining an allocation of fault for the accident for the one or more autonomous operation features further includes the one or more processors analyzing data generated by the vehicle-mounted sensors or cameras depicting a vehicle environment and data from the sensors regarding the response of the vehicle to its environment prior to, or during, the vehicle crash.
| 4. The computer system of claim 1, wherein determining an allocation of fault for the accident for the one or more autonomous operation features further includes the one or more processors analyzing wireless communications or data transmissions to and from the vehicle, including vehicle-to-vehicle or infrastructure-to-vehicle communications.
| 5. The computer system of claim 1, wherein the one or more vehicle-mounted sensors include one or more of a GPS (Global Positioning System) unit, a radar unit, a LIDAR unit, an ultrasonic sensor, an infrared sensor, a camera, an accelerometer, a tachometer, or a speedometer, and at least one sensor is configured to actively or passively scan a vehicle environment of the vehicle for obstacles, including other vehicles, buildings, and pedestrians.
| 6. The computer system of claim 1, wherein the one or more vehicle-mounted sensors include one or more of an ignition sensor, an odometer, a system clock, a speedometer, a tachometer, an accelerometer, a gyroscope, a compass, a geolocation or GPS unit, a camera, or a distance sensor.
| 7. The computer system of claim 1, the one or more processors further configured to:
determine an allocation of fault for the accident based, at least in part, upon whether or not the vehicle was being operated in accordance with optimal use levels for a variety of combinations of configurations and settings associated with the one or more autonomous operation features based upon road, weather, or traffic conditions at the time of, or prior to, the vehicle crash, the optimal use levels being associated with a lowest risk of vehicle crash.
| 8. The computer system of claim 1, the one or more processors further configured to:
determine an optimal use level for a variety of combinations of configurations and settings associated with the one or more autonomous operation features based upon current road, weather, or traffic conditions;
compare the optimal use level with a current actual use level for the variety of combinations of configurations and settings associated with the one or more autonomous operation features; and
if the optimal use level differs from the current actual use level, generate and transmit an electronic notification to the vehicle or vehicle operator's mobile device recommending that the optimal use level be used.
| 9. The computer system of claim 1, wherein determining the allocation of fault further includes determining, by a processor, a point of impact on the vehicle, or an indication of a state of one or more traffic signals before, or during, the vehicle crash.
| 10. The computer system of claim 1, the one or more processors further configured to receive data indicating engagement of at least one of the one or more autonomous operation features before the vehicle crash; and
determining the allocation of fault for the vehicle crash includes the one or more processors analyzing whether the autonomous operation feature failed to take appropriate control actions or whether control signals were ineffective in controlling the vehicle immediately prior to the vehicle crash.
| 11. The computer system of claim 1, the one or more processors further configured to receive data indicating engagement or disengagement of the one or more autonomous operation features before the vehicle crash; and
wherein determining the allocation of fault for the vehicle crash includes the one or more processors analyzing whether the vehicle had time to take action to avoid the accident but that action was not taken.
| 12. The computer system of claim 1, the one or more processors and transceivers further configured to receive data indicating engagement or disengagement of the one or more autonomous operation features before the vehicle crash; and
wherein determining the allocation of fault for the vehicle crash includes the one or more processors determining that autonomous operation of the vehicle prior to the vehicle crash was no longer feasible due to conditions in a vehicle environment of the vehicle.
| 13. The computer system of claim 1, the one or more processors and transceivers configured to receive data indicating engagement of the one or more autonomous operation features before the vehicle crash; and
wherein determining the allocation of fault for the vehicle crash includes the one or more processors determining whether the one or more autonomous operation features attempted to return control of the vehicle to the vehicle operator prior to the vehicle crash and whether or not an adequate period of time for transition was available prior to the vehicle crash.
| 14. The computer system of claim 1, wherein the vehicle operating data received via wireless communication further includes telematics data indicating vehicle operation before and during a vehicle crash, including vehicle speed, heading, acceleration, and braking; and
the one or more processors are configured to (1) determine that the vehicle has been involved in the accident based upon processor analysis of the (i) sensor data, (ii) recorded log received, and (iii) telematics data; and (2) determine an allocation of fault for the accident for the one or more autonomous operation features based at least in part upon the received (i) sensor data, (ii) recorded log received, and (iii) telematics data.
| 15. A computer-implemented method for reconstructing a vehicle crash, comprising:
receiving, via one or more processors or an associated transceiver, vehicle operating data for a vehicle having one or more autonomous operation features for controlling the vehicle, the vehicle operating data being generated and transmitted by an on-board computer or mobile device using wireless communication or data transmission, wherein the vehicle operating data includes:
(i) sensor data from one or more vehicle-mounted sensors associated with the one or more autonomous operation features, the sensor data also indicating a configuration or setting of each autonomous operation feature before and during the vehicle crash; and
(ii) a recorded log of decisions made by the one or more autonomous operation features, and commands sent from the on-board computer to control components to operate the vehicle, before and during the vehicle crash;
receiving, via the one or more processors or associated transceiver, an indication of an accident involving the vehicle, or otherwise determining, via the one or more processors, that the vehicle has been involved in the accident based upon processor analysis of the (i) sensor data, and (ii) recorded log received;
generating, by the one or more processors, a crash reconstruction representing a sequence of events involved in the accident by automatically determining, for each of a plurality of times in the sequence of events, each of the following: (i) a location of the vehicle based upon the sensor data, (ii) a location of an obstruction involved in the accident based upon the sensor data, and (iii) a movement of the vehicle based upon the decisions in the recorded log;
determining, by the one or more processors, an allocation of fault for each of the accident for the one or more autonomous operation features based at least in part upon the crash reconstruction; and
causing, by the one or more processors, one or more of the following actions to be performed based upon the determined total risk level: adjust an insurance policy associated with the vehicle, determine a coverage level associated with the insurance policy, present information regarding the determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level, or present the determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle.
| 16. The computer-implemented method of claim 15, wherein, the one or more processors analyze the vehicle operating data received to determine an extent of vehicle damage, or cost to repair the damage or replace part or all of the vehicle, the vehicle operating data including video or image data.
| 17. The computer-implemented method of claim 15, wherein determining, by the one or more processors, an allocation of fault for the accident for the one or more autonomous operation features further includes processor analysis of data generated by the vehicle-mounted sensors or cameras depicting a vehicle environment and data from the sensors regarding the response of the vehicle to its environment prior to, and during, the vehicle crash.
| 18. The computer-implemented method of claim 15, wherein determining, by the one or more processors, an allocation of fault for the accident for the one or more autonomous operation features further includes processor analysis of wireless communications or data transmissions to and from the vehicle, including vehicle-to-vehicle or infrastructure-to-vehicle communications.
| 19. The computer-implemented method of claim 15, the method comprising:
determining, by the one or more processors, an allocation of fault for the accident based, at least in part, upon whether or not the vehicle being operated in accordance with optimal use levels for a variety of combinations of configurations and settings associated with the one or more autonomous operation features based upon road, weather, or traffic conditions at the time of, or prior to, the vehicle crash, the optimal use levels being associated with a lowest risk of vehicle crash.
| 20. The computer-implemented method of claim 15, the method comprising:
determining, by the one or more processors, an optimal use level for a variety of combinations of configurations and settings associated with the one or more autonomous operation features based upon current road, weather, or traffic conditions;
comparing the optimal use level with a current actual use level for the variety of combinations of configurations and settings associated with the one or more autonomous operation features; and if the optimal use level differs from the current actual use level, generating and transmitting a notification to the vehicle or vehicle operator's mobile device recommending that the optimal use level be used. | The system has a processor for determining an allocation of fault for accident for set of autonomous operation features based on crash reconstruction. The processor causes set of following actions to be performed based on determined total risk level, adjusts an insurance policy associated with a vehicle, determines coverage level associated with insurance policy, presents information regarding determined total risk level to a reviewer via a display of a reviewer computing device to verify the determined total risk level or present determination to a customer via a display of a customer computing device for review of an adjustment to the insurance policy associated with the vehicle. An INDEPENDENT CLAIM is also included for a method for reconstructing a vehicle crash. Computer system for reconstructing a vehicle e.g. autonomous vehicle crash. The system reduces risks associated with vehicle operation to control a vehicle to a vehicle operator and utilizes server to allocate fault for accident to set of autonomous operation features and adjusts risk levels and/or risk profiles associated with set of autonomous operation features at block. The system increases autonomous operation feature performance by facilitating near real-time uploads and downloads of information. The drawing shows a schematic block diagram of a computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes. 100Autonomous vehicle insurance system102Front end component104Back-end component110Mobile device114Communication component | Please summarize the input |
Vehicular traffic alerts for avoidance of abnormal traffic conditionsMethods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.What is claimed is:
| 1. A computer-implemented method of generating a vehicle traffic alert and updating a vehicle-usage profile, the method comprising:
detecting, via one or more processors, that an abnormal traffic condition exists in an operating environment of a first vehicle;
generating, via the one or more processors, an electronic message regarding abnormal traffic condition;
transmitting, via the one or more processors, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receiving, via the one or more processors, telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
updating, via the one or more processors, a vehicle-usage profile associated with the nearby vehicle based upon the received telematics data regarding operation of the nearby vehicle.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 5. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 6. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 7. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 8. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the operating environment of the first vehicle.
| 9. The computer-implemented method of claim 1, the method further comprising transmitting the electronic message to a smart infrastructure component, wherein the smart infrastructure component:
analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition; and
performs an action based upon the type of anomalous condition in order to modify the anomalous condition.
| 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 11. A computer system configured to generate a vehicle traffic alert and update a vehicle-usage profile, the computer system comprising one or more processors, the one or more processors configured to:
detect that an abnormal traffic condition exists in an operating environment of a first vehicle;
generate an electronic message regarding the abnormal traffic condition;
transmit the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receive telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
update a vehicle-usage profile associated with the nearby vehicle based upon the received telematics data regarding operation of the nearby vehicle.
| 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather, and the electronic message indicates a GPS location of the bad weather.
| 14. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 15. The computer system of claim 11, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 16. The computer system of claim 11, wherein the one or more processors include one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 20. The computer system of claim 11, wherein the nearby vehicle travels to the operating environment of the first vehicle. | The method involves detecting (1104) that an abnormal traffic condition exists in an operating environment of a first vehicle through a processor. An electronic message is generated (1106) regarding abnormal traffic condition. The electronic message transmitted to a nearby vehicle. The electronic message is transmitted (1108) through wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The telematics data regarding operation of the nearby vehicle is received after the nearby vehicle received the electronic message. A vehicle-usage profile associated with the nearby vehicle is updated based upon the received telematics data regarding operation of the nearby vehicle. An INDEPENDENT CLAIM is included for a system configured to generate a vehicle traffic alert and update a vehicle-usage profile. Method for generating vehicle traffic alert and updating vehicle-usage profile. The travel recommendations reduce or lower risk and enhance driver or vehicle safety. The insurance policies are adjusted, generated and updated. The drawing shows a flow diagram of a traffic condition broadcast method. 1100Method for traffic condition broadcast1102Step for collecting sensor data regarding a vehicle operating environment from sensors1104Step for detecting that an abnormal traffic condition exists in an operating environment1106Step for generating an electronic message regarding abnormal traffic condition1108Step for transmitting the electronic message through wireless communication to alert the nearby vehicle of the abnormal traffic condition | Please summarize the input |
Method and system for enhancing the functionality of a vehicleMethods and systems for enhancing the functionality of a semi-autonomous vehicle are described herein. The semi-autonomous vehicle may receive a communication from a fully autonomous vehicle within a threshold distance of the semi-autonomous vehicle. If the vehicles are travelling on the same route or the same portion of a route, the semi-autonomous vehicle may navigate to a location behind the fully autonomous vehicle. Then the semi-autonomous vehicle may operate autonomously by replicating one or more functions performed by the fully autonomous vehicle. The functions and/or maneuvers performed by the fully autonomous vehicle may be detected via sensors in the semi-autonomous vehicle and/or may be identified by communicating with the fully autonomous vehicle to receive indications of upcoming maneuvers. In this manner, the semi-autonomous vehicle may act as a fully autonomous vehicle.What is claimed is:
| 1. A computer-implemented method for enhancing the functionality of a vehicle, comprising:
broadcasting, via one or more processors and/or associated transceivers of a semiautonomous vehicle having one or more autonomous operation features, a request to follow a fully autonomous vehicle within a predetermined communication range of the semi-autonomous vehicle via vehicle-to-vehicle wireless communication;
receiving, at the one or more processors and/or associated transceivers of the semiautonomous vehicle via vehicle-to-vehicle communication, an indication directly from several autonomous vehicles that each autonomous vehicle is within the predetermined communication range of the semi-autonomous vehicle, wherein each indication includes identification information for the autonomous vehicle for determining a safety rating of the autonomous vehicle;
selecting, at the one or more processors of the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semi-autonomous vehicle-based upon the safety rating of each of the several autonomous vehicles as determined according to the identification information for each autonomous vehicle; and
for a portion of the route, causing, by the one or more processors, the semi-autonomous vehicle to follow the selected autonomous vehicle and mimic each maneuver performed by the autonomous vehicle.
| 2. The computer-implemented method of claim 1, wherein the one or more processors periodically re-verify that the semi-autonomous vehicle remains within a predetermined distance of the selected autonomous vehicle, and when a distance between the vehicles exceeds the predetermined threshold distance, the semi-autonomous vehicle maneuvers to the side of the road and parks.
| 3. The computer-implemented method of claim 1, wherein at least one component in the semi-autonomous vehicle is malfunctioning, such that the semi-autonomous vehicle requires input from a vehicle operator to operate.
| 4. The computer-implemented method of claim 3, wherein the semi-autonomous vehicle is damaged in a vehicle collision and the selected autonomous vehicle is a tow service vehicle.
| 5. The computer-implemented method of claim 1, wherein the semi-autonomous vehicle includes fewer sensors for autonomous operation than the selected autonomous vehicle.
| 6. The computer-implemented method of claim 1, wherein causing the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle includes:
receiving, at the one or more processors, an indication of an upcoming maneuver to be performed by the selected autonomous vehicle and an indication of a time or location at which the upcoming maneuver will be performed; and
causing, by the one or more processors, the semi-autonomous vehicle to perform the upcoming maneuver at the indicated time or location.
| 7. The computer-implemented method of claim 6, further comprising:
receiving, at the one or more processors, an indication of a speed at which the selected autonomous vehicle is travelling; and
causing, by the one or more processors, the semi-autonomous vehicle to travel slower than the selected autonomous vehicle based upon the received speed.
| 8. The computer-implemented method of claim 1, wherein causing the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle includes:
detecting, via one or more sensors within the semi-autonomous vehicle, a maneuver performed by the selected autonomous vehicle; and
causing, by the one or more processors, the semi-autonomous vehicle to perform a same maneuver as the detected maneuver.
| 9. The computer-implemented method of claim 1, wherein a vehicle operator for the semi-autonomous vehicle provides input to the semi-autonomous vehicle to direct the semi-autonomous vehicle to a location behind the autonomous vehicle; and
when the semi-autonomous vehicle detects the selected autonomous vehicle in front of the semi-autonomous vehicle, the method further includes causing, by the one or more processors, the semi-autonomous vehicle to operate without input from a vehicle operator.
| 10. The computer-implemented method of claim 1, wherein selecting, at the one or more processors of the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semi-autonomous vehicle is based upon a comparison of the current route of the semi-autonomous vehicle with each of the several autonomous vehicles' route, respectively.
| 11. A computer system configured to enhance the functionality of a vehicle, the computer system comprising one or more local or remote processors, transceivers, and/or sensors configured to:
broadcast, via a semi-autonomous vehicle having one or more autonomous operation features, a request to follow a fully autonomous vehicle within a predetermined communication range of the semi-autonomous vehicle via vehicle-to-vehicle wireless communication;
receive, at the semi-autonomous vehicle via vehicle-to-vehicle communication, an indication directly from several fully autonomous or fully operational autonomous vehicles that each fully autonomous or fully operational autonomous vehicle is within the predetermined communication range of the semi-autonomous vehicle, wherein each indication includes identification information for the autonomous vehicle for determining a safety rating of the autonomous vehicle;
select, at the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semiautonomous vehicle based upon the safety rating of each of the several autonomous vehicles as determined according to the identification information for each autonomous vehicle; and
for a portion of the route, cause the semi-autonomous vehicle to follow the selected autonomous vehicle and mimic each maneuver performed by the selected autonomous vehicle.
| 12. The computer system of claim 11, wherein the semiautonomous vehicle periodically re-verifies that the semi-autonomous vehicle remains within a predetermined distance of the selected autonomous vehicle, and when a distance between the vehicles exceeds the predetermined threshold distance, the semi-autonomous vehicle maneuvers to the side of the road and parks.
| 13. The computer system of claim 11, wherein at least one component in the semi-autonomous vehicle is malfunctioning, such that the semi-autonomous vehicle requires input from a vehicle operator to operate.
| 14. The computer system of claim 13, wherein the semiautonomous vehicle is damaged in a vehicle collision and the selected autonomous vehicle is a tow service vehicle.
| 15. The computer system of claim 11, wherein the semiautonomous vehicle includes fewer sensors for autonomous operation than the selected autonomous vehicle.
| 16. The computer system of claim 11, wherein to cause the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to:
receive an indication of an upcoming maneuver to be performed by the selected autonomous vehicle and an indication of a time or location at which the upcoming maneuver will be performed; and
cause the semi-autonomous vehicle to perform the upcoming maneuver at the indicated time or location.
| 17. The computer system of claim 16, wherein one or more local or remote processors, transceivers, and/or sensors are further configured to:
receive an indication of a speed at which the selected autonomous vehicle is travelling; and
cause the semi-autonomous vehicle to travel slower than the selected autonomous vehicle based upon the received speed.
| 18. The computer system of claim 11, wherein to cause the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to:
detect, via one or more sensors within the semi-autonomous vehicle, a maneuver performed by the selected autonomous vehicle; and
cause the semi-autonomous vehicle to perform a same maneuver as the detected maneuver.
| 19. The computer system of claim 11, wherein a vehicle operator for the semi-autonomous vehicle provides input to the semi-autonomous vehicle to direct the semi-autonomous vehicle to a location behind the selected autonomous vehicle; and
when the semi-autonomous vehicle detects the selected autonomous vehicle in front of the semi-autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to cause the semi-autonomous vehicle to operate without input from a vehicle operator.
| 20. The computer system of claim 11, wherein selecting at the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semiautonomous vehicle is based upon a comparison of the current route of the semi-autonomous vehicle with each of the several autonomous vehicles' route, respectively. | The method involves broadcasting a request to follow a fully autonomous vehicle within a predetermined communication range of the semi-autonomous vehicle (108) through vehicle-to-vehicle wireless communication through processors and/or associated transceivers of a semi-autonomous vehicle having autonomous operation features. An autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semi-autonomous vehicle-based upon the safety rating of each of the several autonomous vehicles as determined according to the identification information for each autonomous vehicle is selected at the processors of the semi-autonomous vehicle and for a portion of the route. The semi-autonomous vehicle to follow the selected autonomous vehicle and mimic each maneuver performed by the autonomous vehicle is caused by the processors. An INDEPENDENT CLAIM is included for a computer system configured to enhance the functionality of a vehicle. Computer based method for enhancing functionality of vehicle by caravanning with fully autonomous vehicles. The data application facilitates data communication between the front-end components and the back-end components are more efficient processing and data storage. The automobile insurance premium may be determined by evaluating how effectively the vehicle may be able to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence. The drawing shows a block diagram of an autonomous vehicle data system for autonomous vehicle operation, monitoring, communication and related functions.100Autonomous vehicle data system 108Semi-autonomous vehicle 110Mobile devices 120Sensors 130Network | Please summarize the input |
Autonomous vehicle insurance pricing and offering based upon accident riskMethods and systems for monitoring use, determining risk, and pricing insurance policies for an autonomous vehicle having one or more autonomous operation features are provided. According to certain aspects, accident risk factors may be determined for autonomous operation features of the vehicle using information regarding the autonomous operation features of the vehicle or other accident related factors associated with the vehicle. The accident risk factors may indicate the ability of the autonomous operation features to avoid accidents during operation, particularly without vehicle operator intervention. The accident risk levels determined for a vehicle may further be used to determine or adjust aspects of an insurance policy associated with the vehicle.What is claimed is:
| 1. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, the method comprising:
generating, by one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies, test results for the autonomous or semi-autonomous vehicle technology, wherein the computing systems generate the test results as hardware or software responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data that simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
receiving, at one or more processors, information regarding the test results;
determining, by one or more processors, an indication of reliability of the autonomous or semi-autonomous vehicle technology based upon the test results, including compatibility of the autonomous or semi-autonomous vehicle technology with the at least one additional autonomous or semi-autonomous vehicle technologies tested;
determining, by one or more processors, an accident risk factor based upon the received information regarding the test results and the indication of reliability by analyzing an effect on a risk associated with a potential vehicle accident of the autonomous or semi-autonomous vehicle technology, wherein the accident risk factor is determined based upon an ability of a version of artificial intelligence of the autonomous or semi-autonomous vehicle technology to avoid collisions without human interaction;
determining, by one or more processors, one or more vehicle insurance policy premiums for one or more vehicles based at least in part upon the determined accident risk factor; and
causing, by one or more processors, information regarding the one or more vehicle insurance policies to be presented to one or more customers for review.
| 2. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology includes at least one of a fully autonomous vehicle feature or a limited human driver control feature.
| 3. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology performs at least one of the following functions:
steering;
accelerating;
braking;
monitoring blind spots;
presenting a collision warning;
adaptive cruise control; or
parking.
| 4. The computer-implemented method of claim 1, wherein the autonomous or semi-autonomous vehicle technology is related to at least one of the following:
driver alertness monitoring;
driver responsiveness monitoring;
pedestrian detection;
artificial intelligence;
a back-up system;
a navigation system;
a positioning system;
a security system;
an anti-hacking measure;
a theft prevention system; or
remote vehicle location determination.
| 5. The computer-implemented method of claim 1, further comprising receiving, at one or more processors, an accident-related factor, wherein:
the accident risk factor is further determined based in part upon the received accident-related factor, and
the accident-related factor is related to at least one of the following:
a point of impact;
a type of road;
a time of day;
a weather condition;
a type of a trip;
a length of a trip;
a vehicle style;
a vehicle-to-vehicle communication; or
a vehicle-to-infrastructure communication.
| 6. The computer-implemented method of claim 1, wherein the accident risk factor is further determined for the autonomous or semi-autonomous vehicle technology based upon at least one of the following: (1) a type of the autonomous or semi-autonomous vehicle technology, (2) a version of computer instructions of the autonomous or semi-autonomous vehicle technology, (3) an update to computer instructions of the autonomous or semi-autonomous vehicle technology, or (4) an update to the artificial intelligence associated with the autonomous or semi-autonomous vehicle technology.
| 7. The computer-implemented method of claim 1, wherein the method further includes determining at least one of a discount, a refund, or a reward associated with the one or more vehicle insurance policies based upon the accident risk factor determined for the autonomous or semi-autonomous vehicle technology.
| 8. The computer-implemented method of claim 1, wherein the received information further includes at least one of a database or a model of accident risk assessment based upon information regarding past vehicle accident information.
| 9. The computer-implemented method of claim 1, wherein causing information regarding the one or more vehicle insurance policies to be presented to the one or more customers for review includes communicating to each customer an insurance premium for automobile insurance coverage.
| 10. A computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology, comprising:
one or more processors;
one or more communication modules adapted to communicate data;
one or more computing systems configured to evaluate the autonomous or semi-autonomous vehicle technology operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous vehicle technologies to generate test results for the autonomous or semi-autonomous vehicle technology, wherein the computing systems generate the test results as hardware or software responses of the autonomous or semi-autonomous vehicle technology to virtual test sensor data that simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment, and wherein the test results are communicated to the one or more processors via the one or more communication modules; and
a program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to:
receive information regarding the test results;
determine an indication of reliability of the autonomous or semi-autonomous vehicle technology based upon the test results, including compatibility of the autonomous or semi-autonomous vehicle technology with the at least one additional autonomous or semi-autonomous vehicle technologies tested;
determine an accident risk factor based upon the received information regarding the test results and the indication of reliability by analyzing an effect on a risk associated with a potential vehicle accident of the autonomous or semi-autonomous vehicle technology, wherein the accident risk factor is determined based upon an ability of a version of artificial intelligence of the autonomous or semi-autonomous vehicle technology to avoid collisions without human interaction;
determine one or more vehicle insurance policy premiums for one or more vehicles based at least in part upon the determined accident risk factor;
and cause information regarding the one or more vehicle insurance policies to be presented to one or more customers for review.
| 11. The computer system of claim 10, wherein the accident risk factor is further determined for the autonomous or semi-autonomous vehicle technology based upon at least one of the following: (1) a type of the autonomous or semi-autonomous vehicle technology, (2) a version of computer instructions of the autonomous or semi-autonomous vehicle technology, (3) an update to computer instructions of the autonomous or semi-autonomous vehicle technology, or (4) an update to the artificial intelligence associated with the autonomous or semi-autonomous vehicle technology.
| 12. The computer system of claim 10, wherein the received information further includes at least one of a database or a model of accident risk assessment based upon information regarding past vehicle accident information.
| 13. The computer system of claim 10, wherein the autonomous or semi-autonomous vehicle technology includes at least one of a fully autonomous vehicle feature or a limited human driver control feature.
| 14. The computer system of claim 10, wherein the executable instructions that cause the computer system to cause information regarding the one or more vehicle insurance policies to be presented to the one or more customers for review include instructions that cause the computer system to communicate to each customer an insurance premium for automobile insurance coverage.
| 15. A computer-implemented method of evaluating effectiveness of an autonomous or semi-autonomous driving package of computer instructions, the method comprising:
generating, by one or more computing systems configured to evaluate the autonomous or semi-autonomous driving package operating within a virtual test environment configured to simultaneously test at least one additional autonomous or semi-autonomous driving packages of computer instructions, test results for the autonomous or semi-autonomous driving package of computer instructions in the virtual test environment, wherein the computing systems generate the test results as responses of the computer instructions implemented within the virtual test environment to virtual test sensor data that simulates sensor data for operating conditions associated with a plurality of test scenarios within the virtual test environment;
determining, by one or more processors, an indication of reliability of the autonomous or semi-autonomous driving package based upon the test results, including compatibility of the autonomous or semi-autonomous driving package with the at least one additional autonomous or semi-autonomous driving packages tested;
analyzing, by one or more processors, loss experience associated with the computer instructions to determine effectiveness in actual driving situations;
determining, by one or more processors, a relative accident risk factor for artificial intelligence of the computer instructions based upon the ability of the computer instructions to make automated or semi-automated driving decisions for a vehicle that avoid collisions using the test results, the indication of reliability, and analysis of loss experience;
determining, by one or more processors, one or more vehicle insurance policy premiums for one or more vehicles based at least in part upon the relative risk factor assigned to the artificial intelligence of the autonomous or semi-autonomous driving package of computer instructions; and
causing, by one or more processors, information regarding the one or more vehicle insurance policies to be presented to one or more customers for review.
| 16. The computer-implemented method of claim 15, wherein the autonomous or semi-autonomous driving package of computer instructions are stored on a non-transitory computer readable medium and direct autonomous or semi-autonomous vehicle functionality related to at least one of the following functions:
steering;
accelerating;
braking;
monitoring blind spots;
presenting a collision warning;
adaptive cruise control; or
parking.
| 17. The computer-implemented method of claim 15, wherein the autonomous or semi-autonomous driving package of computer instructions are stored on a non-transitory computer readable medium and direct autonomous or semi-autonomous vehicle functionality related to at least one of the following:
driver alertness monitoring;
driver responsiveness monitoring;
pedestrian detection;
artificial intelligence;
a back-up system;
a navigation system;
a positioning system;
a security system;
an anti-hacking measure;
a theft prevention system; or
remote vehicle location determination.
| 18. The computer-implemented method of claim 15, wherein the relative accident factor is based upon, at least in part, at least one accident-related factor, including:
a point of impact;
a type of road;
a time of day;
a weather condition;
a type of a trip;
a length of a trip;
a vehicle style;
a vehicle-to-vehicle communication; or
a vehicle-to-infrastructure communication.
| 19. The computer-implemented method of claim 15, the method further comprising adjusting at least one of an insurance premium, a discount, a refund, or a reward associated with the one or more vehicle insurance policies based upon the relative accident risk factor.
| 20. The computer-implemented method of claim 15, wherein causing information regarding the one or more vehicle insurance policies to be presented to the one or more customers for review by the one or more customers includes communicating to each customer a cost of automobile insurance coverage. | The method involves generating test results for autonomous or semi-autonomous vehicle technology. The information regarding test results are received (1004). An indication of reliability of autonomous or semi-autonomous vehicle technology is determined (1010). An accident risk factor is determined (1012) based upon received information regarding test results and indication of reliability. The vehicle insurance policy premiums for vehicles are determined (1014). The information regarding vehicle insurance policies are presented to the customers for review. INDEPENDENT CLAIMS are included for the following:a computer system for evaluating effectiveness of an autonomous or semi-autonomous vehicle technology; anda computer-based method of evaluating effectiveness of an autonomous or semi-autonomous driving package of computer instructions. Computer-based method of evaluating effectiveness of autonomous or semi-autonomous vehicle technology. An automobile insurance premium is determined by evaluating how effectively the vehicle is able to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence. The autonomous vehicle operation features assist the vehicle operator to more safely or efficiently operate a vehicle or take full control of vehicle operation under some or all circumstances. The autonomous or semi-autonomous vehicle technology and/or the autonomous or semi-autonomous driving package of computer instructions can perform the following functions such as steering, accelerating, braking, monitoring blind spots, presenting a collision warning, adaptive cruise control, and/or parking and relate to the following driver alertness monitoring, driver responsiveness monitoring, pedestrian detection, artificial intelligence, a back-up system, a navigation system, a positioning system, a security system, an anti-hacking measure, a theft prevention system, and/or remote vehicle location determination. The drawing shows a flow diagram depicting an autonomous vehicle insurance pricing method for determining risk and premiums for vehicle insurance policies covering autonomous vehicles with autonomous communication features. 1004Step for receiving information regarding test results1006Step for determining risk levels associated with autonomous operation1010Step for determining indication of reliability of autonomous or semi-autonomous vehicle technology1012Step for determining accident risk factor1014Step for determining vehicle insurance policy premiums | Please summarize the input |
Method and system for enhancing the functionality of a vehicleMethods and systems for enhancing the functionality of a semi-autonomous vehicle are described herein. The semi-autonomous vehicle may receive a communication from a fully autonomous vehicle within a threshold distance of the semi-autonomous vehicle. If the vehicles are travelling on the same route or the same portion of a route, the semi-autonomous vehicle may navigate to a location behind the fully autonomous vehicle. Then the semi-autonomous vehicle may operate autonomously by replicating one or more functions performed by the fully autonomous vehicle. The functions and/or maneuvers performed by the fully autonomous vehicle may be detected via sensors in the semi-autonomous vehicle and/or may be identified by communicating with the fully autonomous vehicle to receive indications of upcoming maneuvers. In this manner, the semi-autonomous vehicle may act as a fully autonomous vehicle.What is claimed is:
| 1. A computer-implemented method for enhancing the functionality of a vehicle, comprising:
broadcasting, via one or more processors and/or associated transceivers of a semiautonomous vehicle having one or more autonomous operation features, a request to follow a fully autonomous vehicle within a predetermined communication range of the semi-autonomous vehicle via vehicle-to-vehicle wireless communication when the semi-autonomous vehicle is operating in a partially autonomous mode of operation with at least some of the control decisions being made by a vehicle operator;
receiving, at the one or more processors and/or associated transceivers of the semiautonomous vehicle via vehicle-to-vehicle communication, an indication directly from several autonomous vehicles that each autonomous vehicle is within the predetermined communication range of the semi-autonomous vehicle, wherein each indication includes identification information for the autonomous vehicle for determining a safety rating of the autonomous vehicle;
selecting, at the one or more processors of the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semi-autonomous vehicle based upon the safety rating of each of the several autonomous vehicles as determined according to the identification information for each autonomous vehicle; and
for a portion of the route, causing, by the one or more processors, the semi-autonomous vehicle to follow the selected autonomous vehicle and mimic each maneuver performed by the autonomous vehicle, such that the semi-autonomous vehicle is capable of operating without input from the vehicle operator along the same portion of the route.
| 2. The computer-implemented method of claim 1, wherein the one or more processors periodically re-verify that the semi-autonomous vehicle remains within a predetermined distance of the selected autonomous vehicle, and when a distance between the vehicles exceeds the predetermined threshold distance, the semi-autonomous vehicle maneuvers to the side of the road and parks.
| 3. The computer-implemented method of claim 1, wherein at least one component in the semi-autonomous vehicle is malfunctioning, such that the semi-autonomous vehicle requires input from the vehicle operator to operate.
| 4. The computer-implemented method of claim 3, wherein the semi-autonomous vehicle is damaged in a vehicle collision and the selected autonomous vehicle is a tow service vehicle.
| 5. The computer-implemented method of claim 1, wherein the semi-autonomous vehicle includes fewer sensors for autonomous operation than the selected autonomous vehicle.
| 6. The computer-implemented method of claim 1, wherein causing the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle includes:
receiving, at the one or more processors, an indication of an upcoming maneuver to be performed by the selected autonomous vehicle and an indication of a time or location at which the upcoming maneuver will be performed; and
causing, by the one or more processors, the semi-autonomous vehicle to perform the upcoming maneuver at the indicated time or location.
| 7. The computer-implemented method of claim 6, further comprising:
receiving, at the one or more processors, an indication of a speed at which the selected autonomous vehicle is travelling; and
causing, by the one or more processors, the semi-autonomous vehicle to travel slower than the semi-autonomous vehicle based upon the received speed.
| 8. The computer-implemented method of claim 1, wherein causing the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle includes:
detecting, via one or more sensors within the semi-autonomous vehicle, a maneuver performed by the selected autonomous vehicle; and
causing, by the one or more processors, the semi-autonomous vehicle to perform a same maneuver as the detected maneuver.
| 9. The computer-implemented method of claim 1, wherein the vehicle operator for the semi-autonomous vehicle provides input to the semi-autonomous vehicle to direct the semi-autonomous vehicle to a location behind the autonomous vehicle; and
when the semi-autonomous vehicle detects the selected autonomous vehicle in front of the semi-autonomous vehicle, the method further includes causing, by the one or more processors, the semi-autonomous vehicle to operate without input from the vehicle operator.
| 10. The computer-implemented method of claim 1, wherein selecting, at the one or more processors of the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semi-autonomous vehicle is based upon a comparison of the current route of the semi-autonomous vehicle with each of the several autonomous vehicles' route, respectively.
| 11. A computer system configured to enhance the functionality of a vehicle, the computer system comprising one or more local or remote processors, transceivers, and/or sensors configured to:
broadcast, via a semi-autonomous vehicle having one or more autonomous operation features, a request to follow a fully autonomous vehicle within a predetermined communication range of the semi-autonomous vehicle via vehicle-to-vehicle wireless communication when the semi-autonomous vehicle is operating in a partially autonomous mode of operation with at least some of the control decisions being made by a vehicle operator;
receive, at the semi-autonomous vehicle via vehicle-to-vehicle communication, an indication directly from several fully autonomous or fully operational autonomous vehicles that each fully autonomous or fully operational autonomous vehicle is within the predetermined communication range of the semi-autonomous vehicle, wherein each indication includes identification information for the autonomous vehicle for determining a safety rating of the autonomous vehicle;
select, at the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semiautonomous vehicle based upon the safety rating of each of the several autonomous vehicles as determined according to the identification information for each autonomous vehicle; and
for a portion of the route, cause the semi-autonomous vehicle to follow the selected autonomous vehicle and mimic each maneuver performed by the selected autonomous vehicle, such that the semi-autonomous vehicle is capable of operating without input from the vehicle operator.
| 12. The computer system of claim 11, wherein the semiautonomous vehicle periodically re-verifies that the semi-autonomous vehicle remains within a predetermined distance of the selected autonomous vehicle, and when a distance between the vehicles exceeds the predetermined threshold distance, the semi-autonomous vehicle maneuvers to the side of the road and parks.
| 13. The computer system of claim 11, wherein at least one component in the semi-autonomous vehicle is malfunctioning, such that the semi-autonomous vehicle requires input from the vehicle operator to operate.
| 14. The computer system of claim 13, wherein the semiautonomous vehicle is damaged in a vehicle collision and the selected autonomous vehicle is a tow service vehicle.
| 15. The computer system of claim 11, wherein the semiautonomous vehicle includes fewer sensors for autonomous operation than the selected autonomous vehicle.
| 16. The computer system of claim 11, wherein to cause the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to:
receive an indication of an upcoming maneuver to be performed by the selected autonomous vehicle and an indication of a time or location at which the upcoming maneuver will be performed; and
cause the semi-autonomous vehicle to perform the upcoming maneuver at the indicated time or location.
| 17. The computer system of claim 16, wherein one or more local or remote processors, transceivers, and/or sensors are further configured to:
receive an indication of a speed at which the selected autonomous vehicle is travelling; and
cause the semi-autonomous vehicle to travel slower than the semi-autonomous vehicle based upon the received speed.
| 18. The computer system of claim 11, wherein to cause the semi-autonomous vehicle to mimic each maneuver performed by the selected autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to:
detect, via one or more sensors within the semi-autonomous vehicle, a maneuver performed by the selected autonomous vehicle; and
cause the semi-autonomous vehicle to perform a same maneuver as the detected maneuver.
| 19. The computer system of claim 11, wherein the vehicle operator for the semi-autonomous vehicle provides input to the semi-autonomous vehicle to direct the semi-autonomous vehicle to a location behind the selected autonomous vehicle; and
when the semi-autonomous vehicle detects the selected autonomous vehicle in front of the semi-autonomous vehicle, the one or more local or remote processors, transceivers, and/or sensors are configured to cause the semi-autonomous vehicle to operate without input from the vehicle operator.
| 20. The computer system of claim 11, wherein selecting at the semi-autonomous vehicle, an autonomous vehicle from among the several autonomous vehicles within the predetermined communication range of the semiautonomous vehicle is based upon a comparison of the current route of the semi-autonomous vehicle with each of the several autonomous vehicles' route, respectively. | The method involves broadcasting, via a processor and/or associated transceivers of a semi-autonomous vehicle, a request to follow a fully autonomous vehicle (502) within a predetermined communication range of the semi-autonomous vehicle via vehicle-to-vehicle wireless communication. An indication is received directly from a number of autonomous vehicles that each autonomous vehicle is within the predetermined communication range (504). An autonomous vehicle is selected from among the autonomous vehicles within the predetermined communication range based upon a safety rating of each of the autonomous vehicles as determined according to identification information for each autonomous vehicle. The semi-autonomous vehicle is caused to follow the selected autonomous vehicle (510) and mimic each maneuver performed by the autonomous vehicle (512), such that the semi-autonomous vehicle is capable of operating without input from the vehicle operator along the same portion of the route. An INDEPENDENT CLAIM is also included for a computer system configured to enhance the functionality of a vehicle. Method for enhancing functionality of semi-autonomous vehicle. The method enables the fully autonomous vehicle to act as a guide to ensure the semi-autonomous vehicle is safe to make a particular maneuver, when the semi-autonomous vehicle does not have the sensor capabilities to detect and/or monitor all of its surroundings. The drawing shows the flow diagram of an autonomous vehicle caravan method for causing a semi-autonomous vehicle to follow a follow autonomous vehicle. 502Broadcast request to follow fully autonomous vehicle504Receive communication from autonomous vehicle within predetermined communication range506Compare route for fully autonomous vehicle to route for semi-autonomous vehicle510Cause semi-autonomous vehicle to follow selected autonomous vehicle512Mimic each maneuver performed by autonomous vehicle | Please summarize the input |
Vehicular traffic alerts for avoidance of abnormal traffic conditionsMethods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.What is claimed is:
| 1. A computer-implemented method of generating a vehicle traffic alert and updating a vehicle-usage profile, the method comprising:
detecting, via one or more processors, that an abnormal traffic condition exists in an operating environment of a first vehicle;
generating, via the one or more processors, an electronic message regarding the abnormal traffic condition;
transmitting, via the one or more processors, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receiving, via the one or more processors, telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
updating, via the one or more processors, a vehicle-usage profile associated with the nearby vehicle based upon the received telematics data regarding operation of the nearby vehicle, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 5. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 6. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 7. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the operating environment of the first vehicle.
| 8. The computer-implemented method of claim 1, the method further comprising transmitting the electronic message to a smart infrastructure component, wherein the smart infrastructure component:
analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition; and
performs an action based upon the type of anomalous condition in order to modify the anomalous condition.
| 9. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 10. A computer system configured to generate a vehicle traffic alert and update a vehicle-usage profile, the computer system comprising one or more processors, the one or more processors configured to:
detect that an abnormal traffic condition exists in an operating environment of a first vehicle;
generate an electronic message regarding the abnormal traffic condition;
transmit the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition;
receive telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and
update a vehicle-usage profile associated with the nearby vehicle based upon the received telematics data regarding operation of the nearby vehicle, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
| 11. The computer system of claim 10, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
| 12. The computer system of claim 10, wherein the abnormal traffic condition is bad weather, and the electronic message indicates a GPS location of the bad weather.
| 13. The computer system of claim 10, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 14. The computer system of claim 10, wherein the one or more processors include one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 15. The computer system of claim 10, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 16. The computer system of claim 10, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 17. The computer system of claim 10, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 18. The computer system of claim 10, wherein the nearby vehicle travels to the operating environment of the first vehicle.
| 19. A computer-implemented method of generating a vehicle traffic alert and updating a vehicle-usage profile, the method comprising:
detecting, via one or more processors, that an abnormal traffic condition exists in an operating environment of a first vehicle;
generating, via the one or more processors, an electronic message regarding the abnormal traffic condition;
transmitting, via the one or more processors, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition; and
receiving, via the one or more processors, telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message,
wherein the electronic message contains location information of the abnormal traffic condition, and the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle. | The method involves detecting that an abnormal traffic condition exists in an operating environment of a first vehicle through multiple processors. An electronic message regarding the abnormal traffic condition is generated. The electronic message is transmitted to a nearby vehicle, where the electronic message is transmitted through wireless communication to alert a nearby vehicle (108) of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The telematics data regarding operation of the nearby vehicle is received after the nearby vehicle received the electronic message. A vehicle-usage profile is updated associated with the nearby vehicle based upon the received telematics data regarding operation of the nearby vehicle, where updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle. An INDEPENDENT CLAIM is included for a computer system for generating vehicle traffic alert and update vehicle-usage profile. Method for generating vehicle traffic alert and updating usage profile of vehicle such as slow-moving vehicle e.g. farm machinery, construction equipment, oversized load vehicle, or emergency vehicle e.g. ambulance, fire truck, police vehicle equipped to transmit electronic message indicate presence to nearby vehicle. The communication unit is configured to conditionally send data, which is particularly advantageous when computing device is implemented as a mobile computing device, as such conditions helps to reduce power usage and prolong battery life. The second computing device ignores the telematics data, thus saving processing power and battery life. The external computing device updates the earlier profile based upon new telematics data, which updates occur periodically or upon occurrence of an event. The drawing shows a block diagram of the telematics collection system. 100Telematics collection system106External computing device108Nearby vehicle110Computing device120Tactile alert system | Please summarize the input |
Vehicular traffic alerts for avoidance of abnormal traffic conditionsMethods and systems are described for generating a vehicle-to-vehicle traffic alert. Various aspects may include detecting that an abnormal traffic condition exists in an operating environment of a vehicle and generating a related electronic message. The electronic message may be transmitted via the vehicle's transceiver using a wireless communication to a nearby vehicle to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition.What is claimed is:
| 1. A computer-implemented method of generating a vehicle-to-vehicle traffic alert, the method comprising:
detecting, via one or more processors, that an abnormal traffic condition exists in an operating environment of a vehicle;
generating, via the one or more processors, an electronic message regarding the abnormal traffic condition;
transmitting, via a vehicle-mounted transceiver associated with the vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition; and
updating a risk aversion profile associated with a vehicle operator of the nearby vehicle based upon the electronic message, wherein the risk aversion profile is associated with a travel environment for the nearby vehicle, the travel environment including at least an environment where the nearby vehicle has traveled two or more times.
| 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the vehicle.
| 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 4. The computer-implemented method of claim 1, the method further comprising generating an auto insurance discount associated with the vehicle.
| 5. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 6. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
| 7. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 8. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the operating environment of the vehicle.
| 9. The computer-implemented method of claim 1, the method further comprising transmitting the electronic message to a smart infrastructure component, wherein the smart infrastructure component:
analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition; and
performs an action based on the type of anomalous condition in order to modify the anomalous condition.
| 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and wherein the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
| 11. A computer system configured to generate a vehicle-to-vehicle traffic alert, the computer system comprising one or more processors, the one or more processors configured to:
detect that an abnormal traffic condition exists in an operating environment of a vehicle;
generate an electronic message regarding the abnormal traffic condition;
transmit, via a vehicle-mounted transceiver associated with the vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition; and
update a risk aversion profile associated with a vehicle operator of the nearby vehicle based upon the electronic message, wherein the risk aversion profile is associated with a travel environment for the nearby vehicle, the travel environment including at least an environment where the nearby vehicle has traveled two or more times.
| 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the vehicle.
| 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
| 14. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
| 15. The computer system of claim 11, the system further configured to generate, an auto insurance discount associated with the vehicle.
| 16. The computer system of claim 11, wherein the one or more processors is one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
| 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
| 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
| 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
| 20. The computer system of claim 11, wherein the nearby vehicle travels to the operating environment of the vehicle. | The method involves transmitting the electronic message through the wireless communication to alert nearby vehicle (108) of abnormal traffic condition and to allow nearby vehicle to avoid abnormal traffic condition. A risk aversion profile associated with a vehicle operator (106) of the nearby vehicle is updated based upon the electronic message. The risk aversion profile is associated with a travel environment for the nearby vehicle, the travel environment including an environment where the nearby vehicle has traveled several times. An INDEPENDENT CLAIM is included for a computer system configured to generate a vehicle-to-vehicle traffic alert. Computer based method for generating vehicle-to-vehicle traffic alert. The insurance policies such as vehicle or life insurance policies can be adjusted, generated, and/or updated, based upon an individual's usage and/or taking travel recommendations, such as travel recommendations that reduce or lower risk and/or enhance driver or vehicle safety. The risk can be reduced, by reducing road rage by reporting negative driving behavior. The risk averse customers can receive insurance discounts or other insurance cost savings based upon data that reflects low risk driving behavior and/or technology that mitigates or prevents risk to insured assets, such as vehicles or even homes, and/or vehicle operators or passengers. The drawing shows a block diagram of the telematics collection system. 106Vehicle operator108Vehicle114Board computer116Link122Speaker | Please summarize the input |
Shared control for vehicles travelling in formationMethods and apparatus for controlling two or more vehicles travelling in formation. Selected vehicles may be fully or partially autonomously controlled; at least one vehicle is partially controlled by a human driver. Information is collected at each vehicle and from the drivers and it is shared with other vehicles and drivers to create a shared world model. Aspects of the shared world model may be presented to the human driver, who may then respond with a control input. Autonomy systems and the drivers on the vehicles then collaborate to make a collective decision to act or not to act and execute any such action in a coordinated manner.The invention claimed is:
| 1. A method for collaborative control of a platoon of vehicles wherein a first vehicle is at least partially controllable by a human driver and a second vehicle is at least partially controllable by autonomy logic, comprising:
collecting information from human driver inputs on the first vehicle;
collecting information from sensors on both the first vehicle and the second vehicle;
sharing information thus collected between the first vehicle and the second vehicle to provide shared information;
each vehicle using the shared information to maintain a respective local copy of a shared world model, wherein maintaining the respective local copy of the shared world model comprises:
maintaining a local model, including by processing, by the first vehicle, at least a subset of the collected information from sensors on both the first vehicle and the second vehicle to derive perception information and situation information, wherein the derived perception information comprises one or more attributes of one or more sensed objects and wherein the derived situation information comprises information about one or more sensed events,
after deriving the derived perception information and the derived situation information, inputting the derived perception information and the derived situation information to a first local copy of the shared world model at the first vehicle,
receiving, by the second vehicle, the derived perception information and the derived situation information added to the first local copy of the shared world model, and
inputting the derived perception information and the derived situation information to a second local copy of the shared world model at the second vehicle;
collaboratively determining, by both the first and the second vehicle in direct vehicle to vehicle communication, to perform a proposed action based on the first and second local copies of the shared world model; and
in accordance with determining, by both the first and the second vehicle, to perform the proposed action, performing, by either or both the first and the second vehicle, the proposed action.
| 2. The method of claim 1 wherein the proposed action is to update a state of the world model.
| 3. The method of claim 1 wherein the proposed action is proposed by the autonomy logic and the proposed action may be vetoed by the human driver.
| 4. The method of claim 1 wherein the proposed action is proposed by the human driver and the proposed action may be vetoed by the autonomy logic.
| 5. The method of claim 1 wherein the proposed action includes either the first or second vehicle joining or leaving the platoon.
| 6. The method of claim 5 wherein the proposed action includes a third vehicle which is at least partially controlled by a human joining the platoon behind the first vehicle, and then the third vehicle entering an autonomous driving mode.
| 7. The method of claim 5 wherein the proposed action includes the first vehicle leaving an autonomous driving mode and entering a human-controlled mode and exiting the platoon.
| 8. The method of claim 1 wherein the proposed action includes swapping roles of a leader vehicle and a follower vehicle in the platoon.
| 9. The method of claim 1 wherein the proposed action includes
either the first or second vehicle changing lanes; or
either the first or second vehicle entering or leaving a travel lane; or
either the first or second vehicle increasing or decreasing speed or distance to another vehicle; or
either the first or second vehicle maneuvering to park next to another vehicle.
| 10. The method of claim 1 wherein the human driver inputs include information conveyed visually, via audio, or physically such as by forces on a joystick, or a steering device, or other input device.
| 11. The method of claim 1 wherein determining whether to perform the proposed action includes propagating, between the first vehicle and the second vehicle, constraints imposed on either the first vehicle or the second vehicle.
| 12. The method of claim 11 wherein the constraints include the autonomy logic discouraging but not preventing the human from making a steering decision.
| 13. The method of claim 1 wherein the shared information includes
data originating outside components of the autonomy logic or human control and derived data;
data originating inside the autonomy logic or human control; and/or
physical phenomena that is capable of being sensed by the human driver.
| 14. The method of claim 1, further comprising:
displaying at least a selected portion of the shared information on a display associated with the first vehicle. | The method involves collecting information from human driver inputs on the first vehicle. Information is collected from sensors (112) on both the first vehicle and the second vehicle. Information collected between the first vehicle and the second vehicle is shared to provide shared information. Each vehicle is enabled to collaboratively engage in a decision (124) with the other vehicle as a unit using a world model (126). A proposed action is proposed by a logic of autonomy (122), where the proposed action includes either the first or second vehicle joining or leaving the platoon. An INDEPENDENT CLAIM is included for an interface for enabling collaborative control of a platoon of vehicles. Method for realizing collaborative control of platoon of vehicles such as commercial vehicles e.g. long-haul truck. Can also be used in autonomous vehicles. The method enables improving operation of the platoon for the autonomy logic on one or both vehicles to collaborate with each other and with the human driver to improve operation of platoon. The method enables allowing a human to function as a resource for the robot, thus providing assistance with cognition and perception during task execution and enabling the human to compensate inadequacies of autonomy. The drawing shows a schematic diagram of an autonomous and a human-driven truck.112Sensors 114Actuators 122Autonomy 124Decision 126World model | Please summarize the input |
Systems and methods for simulating GNSS multipath and obscuration with networked autonomous vehiclesThe disclosed technology teaches testing an autonomous vehicle: shielding a GNSS receiving antenna of the vehicle from ambient GNSS signals while the vehicle is under test and supplanting the ambient GNSS signals with simulated GNSS signals. Testing includes using a GNSS signal generating system: receiving the ambient GNSS signals using an antenna of the system and determining a location and acceleration of the vehicle from the GNSS signals, accessing a model of an augmented environment that includes multi-pathing and obscuration of the GNSS signals along a test path, based on the determined location—generating the simulated GNSS signals to feed to the vehicle, in real time—simulating at least one constellation of GNSS satellite sources modified according to the augmented environment, based on the determined location, and feeding the simulated signals to a receiver in the vehicle, thereby supplanting ambient GNSS as the autonomous vehicle travels along the test path.We claim as follows:
| 1. A method of testing an autonomous vehicle, including
shielding a Global Navigation Satellite System (abbreviated GNSS) receiving antenna of the autonomous vehicle from ambient GNSS signals while the autonomous vehicle is under test and supplanting the ambient GNSS signals with simulated GNSS signals;
using a GNSS signal generating system,
receiving the ambient GNSS signals using an antenna of the GNSS signal generating system and determining a location and acceleration of the autonomous vehicle from the ambient GNSS signals;
accessing a model of an augmented environment that includes at least multi-pathing and obscuration of the ambient GNSS signals along a test path, based on the location determined from the GNSS signals;
generating the simulated GNSS signals to feed to the autonomous vehicle, in real time, simulating at least one constellation of GNSS satellite sources modified according to the augmented environment, based on the location determined from the GNSS signals; and
feeding the simulated GNSS signals to a receiver in the autonomous vehicle, thereby supplanting ambient GNSS as the autonomous vehicle travels along the test path.
| 2. The method of claim 1, further including spoofing by substituting pirate signals for ambient GNSS as the autonomous vehicle travels along the test path.
| 3. The method of claim 1, further including wireless and conductive feeds of the simulated GNSS signals.
| 4. The method of claim 1, further including using a Faraday cage to shield intent of the autonomous vehicle.
| 5. The method of claim 1, further including coupling the received ambient GNSS signals with inertial measurements unit (abbreviated IMU) input to determine the position of the vehicle in real time with reduced latency.
| 6. The method of claim 1, further including operating the vehicle on a track and simulating buildings.
| 7. The method of claim 1, further including operating the vehicle in an urban environment and combining impaired GNSS signals with object sensors (visual, LIDAR, SONAR, RADAR) used by the vehicle for navigation.
| 8. The method of claim 1, further including operating the vehicle in an urban environment and combining impaired GNSS signals with vehicle to vehicle (abbreviated V2V) and vehicle to infrastructure (Abbreviated V2I) communications used by the vehicle for navigation.
| 9. A method of testing a connected vehicle that is connected to other vehicles and/or infrastructure, including:
shielding a cellular receiving antenna of the connected vehicle from ambient cellular signals while the connected vehicle is under test and supplanting the ambient cellular signals with simulated cellular signals;
using a cellular signal generating system,
receiving the ambient cellular signals and ambient Global Navigation Satellite System (abbreviated GNSS) signals using at least one antenna of the cellular signal generating system and determining a location and acceleration of the connected vehicle from the ambient GNSS signals;
accessing a model of an augmented environment that includes at least multi-pathing and obscuration of the ambient cellular signals along a test path, based on the location determined from the cellular signals;
generating the simulated cellular signals to feed to the connected vehicle, in real time, simulating with at least one vehicle and/or infrastructure source modified according to the augmented environment, based on for the location determined from the cellular signals; and
feeding the simulated cellular signals to a receiver in the connected vehicle, thereby supplanting ambient cellular as the connected vehicle travels along the test path.
| 10. The method of claim 9, wherein the ambient signals include at least one of GNSS, Wi-Fi, 5G and LTE signals that can be manipulated and impaired to test situational awareness of the vehicle in fully controlled and challenging RF environments.
| 11. A tangible non-transitory computer readable storage media impressed with computer program instructions that, when executed, test an autonomous vehicle, including
shielding a Global Navigation Satellite System (abbreviated GNSS) receiving antenna of the autonomous vehicle from ambient GNSS signals while the autonomous vehicle is under test and supplanting the ambient GNSS signals with simulated GNSS signals;
using a GNSS signal generating system,
receiving the ambient GNSS signals using an antenna of the GNSS signal generating system and determining a location and acceleration of the autonomous vehicle from the ambient GNSS signals;
accessing a model of an augmented environment that includes at least multi-pathing and obscuration of the ambient GNSS signals along a test path, based on the location determined from the GNSS signals;
generating the simulated GNSS signals to feed to the autonomous vehicle, in real time, simulating at least one constellation of GNSS satellite sources modified according to the augmented environment, based on the location determined from the GNSS signals; and
feeding the simulated GNSS signals to a receiver in the autonomous vehicle, thereby supplanting ambient GNSS as the autonomous vehicle travels along the test path.
| 12. The tangible non-transitory computer readable storage media of claim 11, further including spoofing by substituting pirate signals for ambient GNSS as the autonomous vehicle travels along the test path.
| 13. The tangible non-transitory computer readable storage media of claim 11, further including wireless and conductive feeds of the simulated GNSS signals.
| 14. The tangible non-transitory computer readable storage media of claim 11, further including using a Faraday cage to shield intent of the autonomous vehicle.
| 15. The tangible non-transitory computer readable storage media of claim 11, further including coupling the received ambient GNSS signals with inertial measurements unit (abbreviated IMU) input to determine the position of the vehicle in real time with reduced latency.
| 16. The tangible non-transitory computer readable storage media of claim 11, further including operating the vehicle on a track and simulating buildings.
| 17. The tangible non-transitory computer readable storage media of claim 11, further including operating the vehicle in an urban environment and combining impaired GNSS signals with object sensors (visual, LIDAR, SONAR, RADAR) used by the vehicle for navigation.
| 18. A system for testing autonomous vehicles includes one or more processors coupled to memory, the memory loaded with computer instructions, that when executed on the processors, implement the shielding, receiving, accessing, generating and feeding of claim 11.
| 19. A tangible non-transitory computer readable storage media impressed with computer program instructions that, when executed, test a connected vehicle that is connected to other vehicles and/or infrastructure, including
shielding a cellular receiving antenna of the connected vehicle from ambient cellular signals while the connected vehicle is under test and supplanting the ambient cellular signals with simulated cellular signals;
using a cellular signal generating system,
receiving the ambient cellular signals and ambient Global Navigation Satellite System (abbreviated GNSS) signals using at least one antenna of the cellular signal generating system and determining a location and acceleration of the connected vehicle from the ambient GNSS signals;
accessing a model of an augmented environment that includes at least multi-pathing and obscuration of the ambient cellular signals along a test path, based on the location determined from the cellular signals;
generating the simulated cellular signals to feed to the connected vehicle, in real time, simulating with at least one vehicle and/or infrastructure source modified according to the augmented environment, based on for the location determined from the cellular signals; and
feeding the simulated cellular signals to a receiver in the connected vehicle, thereby supplanting ambient cellular as the connected vehicle travels along the test path.
| 20. A system for testing a connected vehicle that is connected to other vehicles and/or infrastructure, includes one or more processors coupled to memory, the memory loaded with computer instructions, that when executed on the processors, implement the shielding, receiving, accessing, generating and feeding of claim 19. | The method involves shielding a GNSS receiving antenna of an autonomous vehicle from ambient GNSS signals while the autonomous vehicle is under test and the ambient GNSS signals are supplanted with simulated GNSS signals. A GNSS signal generating system is used. The ambient GNSS signals are received using an antenna of the GNSS signal generating system and a location and acceleration of the autonomous vehicle is determined from the ambient GNSS signals. The simulated GNSS signals are generated to feed to the autonomous vehicle, in real time, and constellation of GNSS satellite sources modified is simulated according to the augmented environment, based on the location determined from the GNSS signals. The simulated GNSS signals are feed to a receiver in the autonomous vehicle, thus supplanting ambient GNSS as the autonomous vehicle travels along the test path. INDEPENDENT CLAIMS are included for the following: (1) a method for testing connected vehicle that is connected to other vehicles and infrastructure;(2) tangible non-transitory computer readable storage media storing program for testing autonomous vehicle; and(3) a system for testing autonomous vehicle. Method for testing autonomous vehicle. The cellular and GNSS testing is enhanced using an inertial measurement unit to improve on accuracy of location determination from GNSS signals, especially under jerk conditions. The GNSS correction data is used and additional sensors are integrated into the onboard navigation system, to increase accuracy, availability and integrity. The track and the required environment are first modelled within the three dimensional (3D) environment model simulation software and then used in real time, to calculate the obscuration, multipath and other impairments from the scene. The drawing shows a flow chart of the method for testing autonomous vehicle. 400Method for testing autonomous vehicle 428Vehicle antenna 445GNSS simulator 455Three dimensional environment 465Three dimensional module | Please summarize the input |
Information processing apparatus, information processing method, and mobile body apparatusProvided is an information processing apparatus that creates map information on the basis of sensor information obtained by an on-vehicle sensor. The information processing apparatus includes a creation section that creates a map of a surrounding area of a mobile body on the basis of sensor information acquired by one or more sensors mounted on the mobile body, a request section that issues an information request to an external apparatus on the basis of a state of the map created by the creation section, and a merge section that merges information acquired by the request section from the external apparatus with the created map. The request section issues an information request to the external apparatus on the basis of a condition of a dead angle included in the map created by the creation section.The invention claimed is:
| 1. An information processing apparatus, comprising:
a creation section configured to create a map of a surrounding area of a first mobile body based on sensor information acquired by at least one sensor mounted on the first mobile body, wherein the map includes a first grid map indicating object existence probabilities in respective grids;
a request section configured to:
issue a first information request to an external apparatus based on a state of the map; and
acquire information from the external apparatus, wherein the acquired information is a second grid map;
a merge section configured to merge the first grid map with the second grid map; and
a control section configured to control driving of the first mobile body based on one of a merging result or the map created by the creation section, wherein the merging result is based on the merger of the first grid map with the second grid map.
| 2. The information processing apparatus according to claim 1, wherein the issuance of the first information request to the external apparatus is based on a condition of a dead angle included in the map.
| 3. The information processing apparatus according to claim 1, wherein the issuance of the first information request to the external apparatus is based on detection of a failure in the at least one sensor.
| 4. The information processing apparatus according to claim 1, wherein, in a case where autonomous driving of the first mobile body based on the map is discontinued, the request section is further configured to issue a second information request to the external apparatus.
| 5. The information processing apparatus according to claim 4, wherein, in a case where evacuation of the first mobile body to a safe place is impossible due to a dead angle included in the map created by the creation section, the request section is further configured to issue a third information request to the external apparatus.
| 6. The information processing apparatus according to claim 1, wherein the request section is further configured to issue a second information request to the external apparatus based on a result of comparison of information regarding a current position of the first mobile body with map information.
| 7. The information processing apparatus according to claim 6, wherein, in a case where the map information indicates that a plurality of dead angles from the current position of the first mobile body exists, the request section is further configured to issue a third information request to the external apparatus.
| 8. The information processing apparatus according to claim 1, wherein the request section is further configured to issue a request to the external apparatus for one of map information to complement a dead angle included in the map or sensor information that is used to create a specific map to complement the dead angle.
| 9. The information processing apparatus according to claim 1, wherein the request section is further configured to control issuance of a second information request to the external apparatus, based on the merging result.
| 10. The information processing apparatus according to claim 9, wherein
the information acquired from the external apparatus is merged with the map created at the merge section, and
the request section is further configured to continue issuance of a request to the external apparatus until dead angles included in the map become equal to or less than a specific value, or stop the issuance of the request to the external apparatus when the dead angles included in the map become equal to or less than the specific value.
| 11. The information processing apparatus according to claim 1, wherein the request section is further configured to issue a second information request to a second mobile body.
| 12. The information processing apparatus according to claim 1, wherein
the first mobile body includes a first vehicle, and
the request section is further configured to issue a second information request to a second vehicle through vehicle-to-vehicle communication.
| 13. The information processing apparatus according to claim 1, wherein
each of the creation section, the request section, and the merge section is further configured to perform information processing on the map for each grid.
| 14. An information processing method, comprising:
creating a map of a surrounding area of a mobile body based on sensor information acquired by at least one sensor mounted on the mobile body, wherein the map includes a first grid map indicating object existence probabilities in respective grids;
issuing an information request to an external apparatus based on a state of the map;
acquiring information from the external apparatus, wherein the acquired information is a second grid map;
merging the first grid map with the second grid map; and
controlling driving of the mobile body based on one of a merging result or the created map, wherein the merging result is based on the merger of the first grid map with the second grid map.
| 15. An information processing apparatus, comprising:
a creation section configured to create a map of a surrounding area of a first mobile body based on sensor information acquired by at least one sensor mounted on the first mobile body, wherein the map includes a grid map indicating object existence probabilities in respective grids; and
a providing section configured to provide at least partial information of the map created by the creation section, in response to a request from an external apparatus, wherein the external apparatus controls a second mobile body based on the at least partial information of the map created by the creation section.
| 16. The information processing apparatus according to claim 15, wherein the providing section is further configured to:
receive the request together with position information of the external apparatus, and
provide information of the map to the external apparatus that exists within a specific range from current position information of the first mobile body.
| 17. The information processing apparatus according to claim 15, wherein
the first mobile body includes a first vehicle, and
the providing section is further configured to provide information of the map to a second vehicle through vehicle-to-vehicle communication.
| 18. An information processing method, comprising:
creating a map of a surrounding area of a first mobile body based on sensor information acquired by at least one sensor mounted on the first mobile body, wherein the map includes a grid map indicating object existence probabilities in respective grids; and
providing, by a request section, at least partial information of the created map, in response to a request from an external apparatus, wherein the external apparatus controls a second mobile body based on the at least partial information of the map created.
| 19. A mobile body apparatus, comprising:
a mobile body comprising a mobile body main part;
at least one sensor mounted on the mobile body main part;
a creation section configured to create a map of a surrounding area of the mobile body based on sensor information acquired by the at least one sensor, wherein the map includes a first grid map indicating object existence probabilities in respective grids;
a request section configured to:
issue information request to an external apparatus based on a state of the map; and
acquire information from the external apparatus, wherein the acquired information is a second grid map;
a merge section configured to merge the first grid map with the second grid map; and
a control section configured to control driving of the mobile body main part based on one of a merging result or the map created by the creation section, wherein the merging result is based on the merger of the first grid map with the second grid map. | The information processing apparatus has a preparation unit which produces the map around the mobile object such as vehicle (200) based on the sensor information acquired by one or more sensors mounted in the mobile object. The request unit requests information of an external device based on the state of the map produced by preparation unit. A synthetic unit synthesizes the information obtained from the external device by the request unit with the produced map. The request unit requests the information of the external device based on the condition of the blind spot contained in the map created by the preparation unit. INDEPENDENT CLAIMS are included for the following:the information processing method; andthe mobile object apparatus. Information processing apparatus used during processing of sensor information from vehicle-mounted sensors mounted in vehicle, of vehicle control system. Can also be used in processing of sensor information from sensors mounted in robot, ship, aircraft, unmanned aircraft such as drone, and predetermined working spaces such as home, office and factory. The synthetic unit synthesizes the information obtained from the external device by the request unit with the produced map, so that the provision of the information processing apparatus which complements the blind spot contained in the map information based on own sensor information based on the information from an external device can be possible. The drawing shows an explanatory view illustrating the synthesize of the grid map of the own vehicle and grid map of the surrounding vehicle. (Drawing includes non-English language text) 200Vehicle201-204,221,222Vehicle-mounted cameras210Bicycle220Surrounding vehicle700Grid map | Please summarize the input |
DEVELOPMENT OF WIRELESS RESOURCE AND COMPUTATION OFFLOADING FOR ENHANCED ENERGY EFFICIENCY IN THE INTERNET OF VEHICLES (IOV)The advent of the Internet of Vehicles (IoV) has brought about a paradigm shift in the field of computing, leading to enhanced vehicle intelligence and improved computational services for applications that require high processing power and minimal latency. These applications include autonomous driving, vehicular virtual reality, and real-time traffic control. The Internet of Vehicles (IoV) holds significant potential for extensive development due to the rapid advancements in vehicle wireless connection technologies. Security applications are of paramount importance within the realm of the Internet of Vehicles (IoV) due to their direct impact on vehicle safety. The concept of Vehicle-to-Vehicle (V2V) communication has garnered significant academic interest within the field of intelligent transportation systems (ITS). This technology is recognised for its potential to fulfil the stringent latency and reliability criteria necessary for safety applications. The Internet of Vehicles (IoV) is a nascent concept that is anticipated to play a crucial role in future mobile networks beyond the fifth and sixth generations. Nevertheless, the computational demands and stringent time limitations of Internet of Vehicles (IoV) applications provide a formidable obstacle for vehicle processing units. In order to achieve this objective, multi-access edge computing (MEC) has the potential to utilise the computing resources located at the periphery of the network in order to fulfil the high computational requirements. However, the allocation of computer resources in an optimal manner is a significant challenge due to the presence of multiple parameters, including the quantity of cars, the availability of resources, and the specific demands associated with each individual activity. This study examines a network comprising several vehicles linked to roadside units (RSUs) equipped with mobile edge computing (MEC) capabilities. We present a methodology that aims to minimise the overall energy consumption of the system by concurrently optimising the decision-making process for task offloading, power and bandwidth allocation, and task assignment to MEC-enabled RSUs. In order to address the inherent complexity of the original problem, we employ a strategy of decoupling it into smaller subproblems. To iteratively optimise these subproblems, we utilise the block coordinate descent approach. The numerical findings provide evidence that the suggested system is capable of significantly reducing overall energy usage across different quantities of cars and MEC nodes, all the while ensuring a minimal likelihood of service disruption.|1. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) provides ground work for future research.
| 2. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) wherein said that the Internet of Things (IoT) refers to a network of physical items that possess the ability to interact, communicate, and exchange data with each other and the surrounding environment through a network, without requiring human interaction.
| 3. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) wherein said with the rapid expansion of the Internet of Things (IoT) across all domains, The computational requirements posed by growing automotive applications have presented a significant problem within the context of the Internet of Vehicles (IoVs).
| 4. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) wherein said It is anticipated that forthcoming wireless networks will possess the capacity to deliver data and voice services to a substantial quantity of mobile devices (MDs), while also enabling the integration of computational and artificial intelligence (AI) functionalities within these MDs.
| 5. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) wherein said that in this paper, we analysed and discussed various aspects.
| 6. Development Of Wireless Resource And Computation Offloading For Enhanced Energy Efficiency In The Internet Of Vehicles (Iov) wherein said that additionally, The emergence of Smart Internet of Vehicles (IoV) as a promising application within the realm of Internet of Things (IoT) can be attributed to the advancements in fifth generation mobile connectivity. | The method involves providing ground work for future research, where an Internet of Things (IoT) refers to a network of physical items that possess an ability to interact, communicate, and exchange data with each other and a surrounding environment through a network without requiring human interaction. Data and voice services are delivered to a quantity of mobile devices, while enabling integration of computational and artificial intelligence (AI) functionalities within the mobile devices. Wireless resource and computer offloading method for internet of vehicles (Iov). The method enables minimizing overall energy consumption of the Iov by concurrently optimizing decision-making process for task offloading, power and bandwidth allocation, and task assignment to mobile edge computing (MEC)-enabled roadside units (RSUs). The method enables efficient utilization of computing resources for facilitating resource sharing. | Please summarize the input |
A PROCESS OF MONITORING AND EVALUATING HUMAN PHYSICAL HEALTH PARAMETERS AND METHOD OF USE BY DASH-CAMThe present invention relates to monitoring, evaluating and reporting physical health parameters of human by a modular dash-cam. Further, it evaluates the driving skills of driver using dash-cam with unique hardware and software capabilities. The dash5 cam comprises a modular rotatable thermal camera with improved Field of view (FOV) that monitors and evaluates person's physical health parameters such as temperature, cough, mask detection, sanitization, oxygen levels measurement etc. and an optical camera monitors security surveillance inside vehicle and upon flipping it starts analyzing the exterior and route. The driver and auto vehicle driving skill results is communicated to multiple surrounding vehicles and pedestrians using similar V2V communication, to alert about potential near risky encounters. The design is scalable to multiple applications like oil/gas leakage, parking assistance, fire detection in vehicle, video game production in low light, AI based movie review, fire detection, drill automation, security monitoring, smart agriculture and integration to PPE smart jacket.|1. A system of determining the health parameters of the driver and/or co-passengers entering in to vehicle utilizing edge and/or cloud computing comprises:a modular dash cam attached to the interior of the windshield comprises; a rotatable thermal camera designed to capture the temperature and other health parameters inside the vehicle; and a flipable or pivoted optical camera to record the activities inside and outside of the vehicle; a processing unit connected to the memory; a plurality of sensors to detect the health parameters; a rechargeable battery assembly; wherein the collected data of health is store in a unstructured distributed way in an artificial intelligence engine and analysed by a processing unit to provide feedback in real-time to the driver.
| 2. A modular dash camera of claim 1, send alerts to the in-vehicle display, driver and/or any emergency contact.
| 3. A modular dash camera of claim 1, wherein the health parameters are such as but not limited to temperature and other body vitals.
| 4. A modular dash camera of claim 1, stores the identity of the driver and co-passenger with a unique identifier to protect privacy.
| 5. A modular dash camera of claim 1, where the thermal and optical camera arrangement is modular in nature and can be interchangeable.
| 6. A modular dash camera of claim 1, wherein the sensors are for detecting oxygen levels, temperature, humidity, blood pressure, heart rate and other sensors.
| 7. A modular dash camera of claim 1, is part of an artificial intelligence engine, where the artificial engine is trained for distributed computing.
| 8. A method of determining the health parameters of the drivers and/or co-passenger entering into the vehicle utilizing edge and cloud computing comprises the steps of: obtain a complete picture of region of interest, with overlapping field of vision with thermal camera; initiating a voice interaction with a microphone and speakers embedded within; conceal identity of the driver and co-passenger by providing a unique identifier; automatic flipping the optical camera for surround view of the interior or exterior of a place or a vehicle; determining the oxygen levels in the area of concern with oximeter installed with camera, and; Reporting via live stream and stream on identification of region and event of interest.
| 9. The method of claim 8, wherein health parameters may be temperature, cough and sneezing, blood pressure, Heart rate and other parameters detectable by dash cam.
| 10. The method of claim 8, sending alerts to the in-vehicle display, driver and/or any emergency contact.
| 11. The method of claim 8, wherein the health parameters are such as but not limited to temperature and other body vitals.
| 12. The method of claim 8, storing the identity of the driver and co-passenger with a unique identifier to protect privacy.
| 13. The method of claim 8, wherein the sensors are for detecting oxygen levels, temperature, humidity, blood pressure, heart rate and other sensors.
| 14. A method of determining the driving skills of the driver using a modular dash cam comprising the steps of: activating optical camera with inbuild AI processing unit with machine learning software and capable of identifying road region in front and, understand scene complexity, traffic signs, speed of car and various other parameters of analysis; comparing with previously trained artificial intelligence algorithms by collecting data at a similar situation during driving with a skilled driver /instructor with precision driving skills; simultaneous processing of thermal images in the software application from thermal camera to understand various driver health analytics such as if driver is drowsy or measure anxiety levels of driver during critical situations or understand precision in right or left turns or roundabouts, or understand whether speed limits were mainlined; creating the skill test report of the driver, based on parameters of analysis and comparison; send the report to the driver and others to improve or rate the driving skills of the driver.
| 15. The method of claim 14, is used by insurance companies to allow or deny the insurance in case of any accident due to driver's mistake.
| 16. The method of claim 14, wherein the driver can designate the controls or take controls in an autonomous vehicle if the skills of driver are not perfect or perfect respectively.
| 17. The method of claim 14, wherein the driver and auto vehicle driving skill with current circumstance is communicated to multiple surrounding vehicles through V2V (vehicle to vehicle) communication technology which can potentially alert multiple other vehicles about potential near risky encounters. | The system has a modular dash cam (810) that is attached to an interior of a windshield. A rotatable thermal camera captures temperature and health parameters inside a vehicle. A flipable or pivoted optical camera records activities inside and outside of the vehicle. Sensors detect the health parameters. A processing unit is connected to a memory. Collected data of health is stored in an unstructured distributed way in an artificial intelligence engine and analyzed by the processing unit to provide feedback in real-time to a driver. The sensors detect oxygen levels, temperature, humidity, blood pressure, heart rate and other sensors. INDEPENDENT CLAIMS are included for the following:a method of determining the health parameters of the drivers and/or co-passenger entering into the vehicle; anda method of determining the driving skills of the driver using a modular dash cam. System for determining health parameters of driver and/or co-passengers entering in vehicle, particularly aeroplane utilizing edge and cloud computing. The method of dynamic pairing between electronic devices, based on the time and proximity of the devices, reduces the possibility for unintentional communications. The system for determining the health parameters of the driver and/or co-passengers entering in to vehicle utilizing edge and cloud computing comprises a modular dash cam attached to the interior of the windshield comprises a rotatable thermal camera designed to capture the temperature and other health parameters inside the vehicle, and a flipable or pivoted optical camera to record the activities inside and outside of the vehicle. The modular dash camera has thermal and optical camera arrangement is modular in nature and can be interchangeable. The drawing shows a schematic view of the dash-cams set up in an aeroplane. 810Modular dash cam812Seat | Please summarize the input |
Domain controller and automatic driving vehicleThe utility model discloses a domain controller and an automatic driving vehicle, wherein the domain controller comprises: from SOC, for performing signal processing to the image detection signal output by the multi-path high-definition camera and the radar data signal output by the multi-path vehicular Ethernet, and outputting the corresponding environment processing signal; a main SOC, the main SOC is connected with the secondary SOC, the main SOC is used for performing signal processing according to the environment processing signal, outputting the corresponding driving planning signal, The technical solution of the utility model is to improve the calculation force and calculation precision of the domain controller of the automatic driving automobile so as to improve the driving safety of the automatic driving automobile.|1. A domain controller, which is applied to automatic driving automobile, the automatic driving automobile comprises a plurality of high definition cameras, a plurality of laser radar and vehicle-mounted Ethernet, wherein the domain controller comprises: a slave SOC, the slave SOC is used for respectively accessing a multi-path high-definition camera and a multi-path vehicle-mounted Ethernet, for performing signal processing on the image detection signal output by the multi-path high-definition camera and the radar data signal output by the multi-path vehicle-mounted Ethernet, and outputting the corresponding environment processing signal, wherein the number of the secondary SOC is at least two; a main SOC, the main SOC is connected with the output end of the secondary SOC, the main SOC is used for performing signal processing according to the environment processing signal, outputting the corresponding driving planning signal, so as to control the function module of the automatic driving automobile to work.
| 2. The domain controller according to claim 1, wherein the number of the secondary SOC is two, which are respectively a first secondary SOC and a second secondary SOC; the first secondary SOC and the second secondary SOC are respectively used with the main SOC. the multi-path high-definition camera is electrically connected with the multi-path vehicle Ethernet; the first secondary SOC is used for performing signal processing on the received multi-path image detection signal and multi-path radar data signal, and outputting a corresponding first environment processing signal; the second secondary SOC is used for performing signal processing on the received multiple paths of image detection signals and multiple paths of radar data signals, and outputting a corresponding second environment processing signal; the main SOC is used for performing signal processing to the received first environment processing signal and/or second environment processing signal, and outputting corresponding driving planning signal.
| 3. The domain controller according to claim 2, wherein the number of the secondary SOC is four, which are respectively the first secondary SOC, the second secondary SOC, the third secondary SOC and the fourth secondary SOC, the first secondary SOC, the second secondary SOC and the third secondary SOC. the third secondary SOC and the fourth secondary SOC are respectively electrically connected with the main SOC; the first secondary SOC is used for outputting the received multi-path image detection signal and multi-path radar data signal to the third secondary SOC and/or the fourth secondary SOC through the main SOC; the second sub-SOC is used for outputting the received multi-path image detection signal and multi-path radar data signal to the third sub-SOC and/or the fourth sub-SOC through the main SOC; the third secondary SOC and the fourth secondary SOC are respectively used for processing the received multi-path image detection signal and multi-path radar data signal, and outputting the corresponding driving planning signal through the main SOC.
| 4. The domain controller according to claim 1, wherein the automatic driving vehicle further comprises a driving component, the domain controller further comprises: a function safety MCU, the function safety MCU is electrically connected with the main SOC, the function safety MCU is used for performing signal processing on the received driving planning signals, and outputting the corresponding driving control signal to the driving component, so as to control the driving route and driving speed of the driving component.
| 5. The domain controller according to claim 4, wherein the domain controller further comprises: a CANFD interface, the CANFD interface is electrically connected with the functional safety MCU, for accessing one or more of millimetre wave radar, ultrasonic radar or vehicle control ECU.
| 6. The domain controller according to claim 4, wherein the domain controller further comprises: a FlexRay interface electrically connected with the functional safety MCU and used for accessing one or more of laser radar, V2X communication module or EIMU detection system.
| 7. The domain controller according to claim 1, wherein the domain controller further comprises: an FAKRK interface, the FAKRK interface is used for electrically connecting with the image detection signal output by the multi-path high definition camera, and accessing the image detection signal output by the multi-path high definition camera; a de-serializing chip electrically connected with the FAKRK interface and the secondary SOC, respectively, for decoding the received image detection signal and outputting the image detection signal to the secondary SOC for signal processing so as to output corresponding environment processing signal; the main SOC is used for processing the received environment processing signal and outputting the corresponding driving planning signal.
| 8. The domain controller according to claim 1, wherein the domain controller further comprises: a plurality of storage modules, a plurality of storage modules are respectively electrically connected with the main SOC and the secondary SOC, a plurality of storage modules are respectively used for storing the corresponding temporary data.
| 9. The domain controller according to claim 1, wherein the domain controller further comprises: a plurality of power supply management modules, a plurality of power supply management modules are respectively electrically connected with the main SOC and the secondary SOC, a plurality of power supply management modules are respectively used for accessing the direct current power supply, and respectively controlling the direct current power supply to access/stop accessing the main SOC and/or the secondary SOC.
| 10. An automatic driving vehicle, comprising multiple high-definition cameras, a vehicle Ethernet and the domain controller according to any one of claims 1 to 9. | The controller has a slave security operation center (SOC) (10) which performs data processing on the image detection signal output by a multi-channel high-definition camera and the radar data signal output by the multi-channel vehicle Ethernet , and outputs the corresponding environment processing signal. A master SOC (30) is connected to the slave SOC. The master SOC performs signal processing according to the environment processing signal, and outputs corresponding driving planning signal, to control the operation of the functional modules of the autonomous driving vehicle. Domain controller for automatic driving automobile (claimed). The computing power and the calculation accuracy of the domain controller of the automatic driving automobile are improved, thus improving the driving safety of the automatic driving automobile. The drawing shows a block diagram of the domain controller. (Drawing includes non-English language text)10Slave SOC 11First Slave SOC 12Second Slave 13Third Slave SOC 20Master SOC 30Functional safety MCU 50Deserialization chip 60Storage Module 70Power management module | Please summarize the input |
TRAFFIC CONTROL USING SOUND SIGNALSMethods for vehicle to vehicle communication, vehicle detection, and vehicle to traffic sign communication are devised. Such methods can involve the use of one or a plurality of speakers to emit artificial sound signals, as well as the use of one or a plurality of sound detectors to record artificial or natural sound signals emitted by nearby vehicles or traffic signs. The use of an active sonar system will also allow autonomous vehicles to detect nearby surroundings. The Doppler Effect can also be used to determine the speeds of moving vehicles. These methods allow autonomous vehicles to drive and respond to their surroundings, and also allow traffic signs to respond to various traffic situations by detecting the presence of nearby vehicles.|1. A method for automobiles for detecting nearby traffic conditions that comprises the following steps:
record sound signals measured by one or a plurality of sound detectors in the automobile,
use the signal processing capabilities of the automobile to analyze the recorded sound signals to identify sound signals emitted by nearby traffic signs or vehicles,
use the sound signals emitted by nearby traffic signs or vehicles to assess surrounding traffic conditions, and
provide traffic information to direct the driving of the automobile.
| 2. The method in claim 1 wherein the step of recording sound signals comprises the step of recording sound signals measured by two or more microphones in the automobile.
| 3. The method in claim 1 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of comparing the recorded sound signals to a database of already known vehicle noise patterns to determine the types of nearby vehicles.
| 4. The method in claim 1 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of distinguishing sound signals coming from different vehicles in order to estimate the number of nearby vehicles.
| 5. The method in claim 1 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of using the Doppler Effect to determine the relative speeds of nearby vehicles.
| 6. The method in claim 1 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of distinguishing sound signals that are in a pre-defined format coming from nearby vehicles.
| 7. The method in claim 6 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of distinguishing amplitude modulated sound signals coming from nearby vehicles.
| 8. The method in claim 6 wherein the step of using the signal processing capabilities of the automobile to analyze recorded sound signals comprises a step of distinguishing frequency modulated sound signals coming from nearby vehicles.
| 9. The method in claim 1 further comprises a step that uses active sonar to transmit a sound signal and detect the echo of the transmitted sound in order to detect the surroundings of the automobile.
| 10. The method in claim 1 further comprises a step of transmitting a sound signal that is in a pre-defined format for communicating with nearby vehicles or traffic signs.
| 11. The method in claim 10 comprises a step of transmitting an amplitude modulated sound signal that is in a pre-defined format in order to communicate with nearby vehicles or traffic signs.
| 12. The method in claim 10 comprises a step of transmitting a frequency modulated sound signal that is in a pre-defined format for communicating with nearby vehicles or traffic signs.
| 13. The method in claim 1 is implemented on an autonomous automobile.
| 14. The method in claim 1 further comprises a step of receiving sound signals transmitted by traffic signs.
| 15. The method in claim 14 comprises a step of receiving amplitude modulated sound signals transmitted by traffic signs.
| 16. The method in claim 14 comprises a step of receiving frequency modulated sound signals transmitted by traffic signs.
| 17. A method for detecting nearby traffic conditions for an automobile that comprises the following steps:
transmit sound signals by one or a plurality of sound transmitting devices,
record echoes of said transmitted sound signals measured by one or a plurality of sound detectors in the automobile,
use the signal processing capabilities of the automobile to analyze the recorded echoed sound signals to assess the surroundings of the automobile.
| 18. The method in claim 17 wherein the step of recording sound signals comprises the step of recording sound signals measured by two or more microphones in the automobile.
| 19. The method in claim 17 wherein the step of transmitting sound signals comprises a step of including identification information in the transmitted sound signals.
| 20. The method in claim 17 wherein the step of using the signal processing capabilities of the automobile to analyze echoed sound signals comprises a step of using the Doppler Effect to determine the relative speeds of nearby vehicles. | The method involves recording sound signals measured by multiple sound detectors in the automobile. The signal processing capabilities of the automobile is used to analyze the recorded sound signals to identify sound signals emitted by nearby traffic signs (504) or vehicles (505). The sound signals emitted by nearby traffic signs or vehicles are used for assessing surrounding traffic conditions. The traffic information is provided to direct the driving of the automobile. The recorded sound signals are compared to a database of already known vehicle noise patterns to determine the types of nearby vehicles. The doppler effect is used for determining the relative speeds of nearby vehicles. An INDEPENDENT CLAIM is included for a method for detecting nearby traffic conditions for an automobile. Method for automobiles for detecting nearby traffic conditions by using sound signals with the help of doppler effect. By knowing the type, speed, distance, and direction of each nearby vehicle, the mobile phone is able to rank the level of potential danger that each vehicle poses and provides warnings for the user. The drawing shows a symbolic diagram that shows the traffic conditions near an intersection. 100Pedestrians cell phone109Earphones504Traffic signs505Vehicles581Pedestrian | Please summarize the input |
Mine automatic driving vehicle coordination planning method based on vehicle road cooperationThe invention claims a coordinated planning method of automatic driving vehicle under mine based on vehicle road cooperation, comprising the following steps: automatically driving the vehicle road to obtain a high precision map under mine provided by a cloud platform, planning a global smooth navigation path based on the road centre line in the high precision map, and realizing smooth reference line; the automatic driving vehicle bottom planner performs path and speed decision planning, and sends the track information output by the bottom planner as output to the control module; automatically driving the vehicle to run normally according to the planning track of the bottom layer of the vehicle, and performing advanced planning according to the specific condition; respectively planning by path and speed decoupling, iteratively solving the feasible self-vehicle track; the self-vehicle track of each automatic driving vehicle is input to the control module for executing the transverse and longitudinal control of the automatic driving vehicle to finish the vehicle meeting action through the mine crossing. The invention effectively plans the collision-free track of multiple automatic driving vehicles and improves the running efficiency of the automatic driving vehicle under the interactive scene.|1. A coordinated planning method of automatic driving vehicle under mine based on vehicle road cooperation, wherein it comprises the following steps: the automatic driving vehicle road obtains the high precision map under the mine provided by the cloud platform, based on the central line of the road in the high precision map, planning the global smooth navigation path, realizing the complete reference line smoothing; according to the planed smooth reference line, the automatic driving vehicle bottom planner performs path and speed decision planning, the track information output by the bottom planner is used as output and sent to the control module; automatically driving the vehicle to run normally according to the planning track of the bottom layer of the vehicle, triggering the coordination node when meeting the narrow tunnel meeting and the crossing meeting scene, and performing the advanced planning according to the specific condition; the planning result of the high-level planning period replaces the original reference line of the automatic driving vehicle, the path and the speed decoupling are used for planning respectively, and the feasible self-vehicle track is solved iteratively; The vehicle track of each automatic driving vehicle is input to the control module, the control module executes the transverse and longitudinal control of the automatic driving vehicle to finish the vehicle meeting action through the mine crossing.
| 2. The coordinated planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 1, wherein the automatic driving vehicle road obtains the high precision map under mine provided by the cloud platform, based on the road central line in the high precision map, for planning the global smooth navigation path, realizing complete reference line smoothing, comprising the following steps: the automatic driving vehicle road obtains the high precision map under the mine provided by the cloud platform, based on the road central line in the high precision map, firstly planning the global smooth navigation path; wherein the road central line is a discrete point set for smoothing processing as the reference line, the discrete point set of the road central line adopts cubic polynomial connection and uniform sampling encryption central line discrete point, polynomial connection adjacent discrete point (xi, yi) and (xi + 1, yi + 1): y is equal to f (x) = a0 + a1x + a2x2 + a3x3, wherein a0, a1, a2 and a3 respectively represent 0-order term coefficient, 1-order term coefficient, 2-order term coefficient and 3-order term coefficient of the cubic polynomial; planning the smooth reference line, searching the self-vehicle projection point in the discrete point set in a planning period, segmenting based on the projection point, taking the path after segmenting as the path section to be smoothed; converting the reference line smoothing problem into the secondary planning problem based on the sectioned path section composed of the densified discrete points, solving according to the cost function and the constraint condition of the sectioned central line point set smoothing to obtain the smooth reference line point set; finally, splicing the reference line segments with different periods so as to realize smooth reference line.
| 3. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 2, wherein the cost function of the centre line point set smoothing is as follows: wherein w1, w2, w3 are the weight of each item in the cost function, xi, yi and xref, yref are the horizontal and vertical coordinates of the reference line and the density central line, respectively.
| 4. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 3, wherein the high precision map under mine stores the road data and the fixed alignment information of the tunnel under mine as structured data, in the process of performing reference line smoothing processing on the road central line, the projection point of the vehicle on the road central line in each automatic driving vehicle planning period is used as the starting point, the point set in a certain range before and after the smooth starting point, and the point set after smooth is used as the reference line.
| 5. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 1, wherein the automatic driving vehicle bottom planner performs path and speed decision planning according to the planed smooth reference line. The track information output by the bottom planner is used as an output and is sent to the control module, which comprises the following steps: according to the planned smooth reference line, the automatic driving vehicle bottom planner performs path and speed decision planning based on Frenet coordinate system taking the navigation path as coordinate axis, and sends the track information output by the bottom planner as output to the control module; The bottom planner adopts the SLT dimension reduction method to decide the planning process as follows: (1) using SLT dimensionality reduction method to divide into SL layer and ST layer for planning, then constructing path and speed planning problem in SL coordinate system and ST coordinate system: wherein l represents the transverse offset of the automatic driving vehicle path relative to the central line of the road, s represents the longitudinal offset of the automatic driving vehicle path along the central line of the road; t represents the moment corresponding to the longitudinal offset in the speed plan; (2) based on static and low-speed obstacle projection, establishing SL image and discretizing the state space, adopting heuristic search method and numerical optimization method for path decision planning; (3) based on dynamic obstacle track prediction, establishing ST image and discretizing the state space, adopting heuristic search method and numerical optimization method for speed decision planning.
| 6. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 5, wherein the Cartesian coordinate system is converted into Frenet coordinate system in the planning process of the bottom planner. before the track information is sent to the control module, the Frenet coordinate system is converted into the global Cartesian coordinate system.
| 7. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 5, wherein the path planning in the SL diagram and the speed planning in the ST diagram set different non-uniform sampling scales according to the tunnel scene, firstly performing state space discretization, distributing the cost value of each discrete point according to the cost function, adopting the improved A* algorithm heuristic search to quickly obtain the initial solution; the initial solution is used as the decision solution to open the safe space, the original problem based on the safe space is converted into the convex optimized problem, the optimal track solution is obtained by the convex optimized solving method under the constraint condition; In the numerical optimization process of speed planning in the SL diagram, the cost function is: wherein w1, w2, w3, w4, w5 are the weight of each item in the cost function, li, lcentre respectively represent the path in the SL image and the longitudinal offset of the reference line; In the numerical optimization process of the speed planning in the ST diagram, the cost function is: wherein w1, w2, w3, w4 are the weights of each item in the cost function, si, vref respectively represent the transverse displacement of the path and the reference speed in the ST image.
| 8. The coordination planning method of automatic driving vehicle under mine based on vehicle-road cooperation according to claim 7, wherein when meeting narrow tunnel meeting vehicle and road junction meeting vehicle scene, triggering the coordination node, and performing advanced planning according to the specific condition, comprising the following steps: automatically driving the vehicle to drive normally according to the planning track of the bottom layer of the vehicle, triggering the coordination node when meeting the narrow tunnel meeting and the intersection meeting scene, firstly judging whether the original track of the automatic driving vehicle is conflicted, if there is no conflict, driving according to the original track; if there is conflict, forming a conflict area in the vehicle interaction area, forming a buffer coordination area in front of the conflict area of the mine narrow tunnel meeting and the road junction meeting scene; one or more automatic driving vehicles in the mine tunnel drive into the buffer coordination area to reduce speed or stop, and wait for coordination in turn; the coordination node receives the driving maneuvering state of all automatic driving vehicles in the intersection buffer coordination area through V2I communication, wherein the high-level planner performs coordination planning for the vehicles in all buffer coordination areas, generating the coordination reference track of the traffic conflict area, and the coordination reference track only considers the automatic driving vehicle in the buffer coordination area, and does not consider other static or dynamic obstacles; wherein the path generation of the advanced planner adopts a smooth optimization method based on straight line and circular arc, firstly sampling the straight line and circle: Knots: ((xk, m, yk, m, sk, m) m = 0, 1, ..., nk) Anchor points: ((xa, j, ya, j, sa, j) j = 0, 1, ..., na) wherein Knots and Anchor points represent the node and anchor point of the divided straight line and circle, m and j represent the number of the corresponding node and trace point, (xk, m, yk, m, sk, m) respectively represent the transverse and longitudinal coordinates of the node and the total length of the divided straight line, (xa, j, ya, j, sa, j) respectively represent the transverse and longitudinal coordinates of the anchor point and the total length of the divided circle; a reference path between every two adjacent nodes is connected by a quintuple polynomial, and then a smooth feasible path is searched near a straight line and a circular path by an optimization method; the advanced planner performs speed planning on all automatic driving vehicles in the range in the ST graph based on each vehicle path planning result, firstly, the interaction between each vehicle and the conflict area is projected into the ST graph, then the state space discretization is performed and the automatic driving vehicle passing sequence is determined, orderly performing the initial solution search and optimization of the speed; and the speed planning of the high-level planner satisfies the constraint condition that the conflict area is only occupied by the same vehicle at the same time; the automatic driving vehicle enters the buffer coordination area, the coordination node realizes V2I communication with the automatic driving vehicle in the area through the PC5 direct connection communication interface of C-V2X, the road side sensing and vehicle-mounted sensing are connected together by using V2I communication technology, realizing low time delay of data transmission, high reliable requirement, establishing reliable information transmission channel, realizing multi-dimensional, all-aspect sensing information sharing and cooperative scheduling control; when the coordination node judges that there is conflict relation between the automatic driving vehicle track of each intersection and the track of other automatic driving vehicles, the high-level planner determines the planning starting point based on the current mobile state of the automatic driving vehicle, and replans and coordinates all vehicles in the buffer coordination area.
| 9. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 8, wherein the planning result of the high-level planning period replaces the original reference line of the automatic driving vehicle, adopts path and speed decoupling to respectively plan, and iteratively solves the feasible self-vehicle track, The method comprises the following steps: the planning result of the advanced planning period replaces the original reference line of the automatic driving vehicle, each automatic driving vehicle establishes a Frenet coordinate system according to the coordinated reference line, the intersection obstacle information sensed by the vehicle-mounted sensor is projected into the SL image and ST image, the vehicle bottom planner performs replanning, respectively planning by path and speed decoupling, and iteratively solving the feasible self-vehicle track; The time for each automatic driving vehicle output by the advanced planner to enter and exit the conflict area is used as the limit area of the ST diagram in the bottom planning process, so as to ensure that there is no conflict between the output track of the automatic vehicle re-planning and the output track of other automatic driving vehicles: wherein tsl, tel represents the time domain boundary of the passing conflict area of the self-vehicle in the advanced planner speed planning result, tin, tout represents the interaction time of the self-vehicle bottom planner speed planning result and the conflict area; The passing speed of the conflict area should satisfy the speed constraint condition: wherein represents the speed planning result of the bottom planner, and is less than the speed limiting v1 of the conflict area; the high-level planner plans each automatic driving vehicle coordination reference track under the corresponding scene, each automatic driving vehicle bottom planner uses the coordination reference track as input to perform self-vehicle weight planning, so as to avoid various obstacles when passing through the conflict area, the high-level planner and the bottom planner ensure that each vehicle passes through the conflict area safely and harmoniously in turn.
| 10. The coordination planning method of automatic driving vehicle under mine based on vehicle road cooperation according to claim 1, wherein the self-vehicle track of each automatic driving vehicle is input to the control module. the control module executes the transverse and longitudinal control of the automatic driving vehicle to finish the vehicle meeting action through the mine crossing, the self-vehicle track of the automatic driving vehicle is converted by the coordinate system to be input to the control module, wherein the transverse control uses the model prediction control method, the longitudinal control uses the PID control method. | The method involves obtaining a high-precision map of a road in a mine under a mine by an automatic driving vehicle. A global smooth navigation path is planned based on a central line of the road in the high precision map. A planed smooth reference line is realized. A path and speed decision planning process is performed according to the planning track of the bottom layer of the vehicle. A vehicle track of each automatic vehicle is input to a control module. The control module executes transverse and longitudinal control of the automatic vehicle to complete vehicle meeting action through a mine crossing. Coordinated planning method of automatic driving vehicle under mine based on vehicle road cooperation. The collision-free track of multiple automatic driving vehicles is effectively planned and the running efficiency of the automatic driving vehicle under the interactive scene is improved. The drawing shows a flow diagram of a planning method. (Drawing includes non-English language text). | Please summarize the input |
REDUNDANT COMMUNICATION METHOD, APPARATUS AND SYSTEM FOR COOPERATIVE AUTONOMOUS DRIVING PLATOONINGThe present disclosure relates to Internet of Vehicles technology, and provides a method, an apparatus, and system for redundant communication for platooning. The method includes: transmitting application data to be transmitted to at least two V2V devices; and controlling the at least two V2V devices that have received the application data to transmit the application data to a predetermined air interface, such that a receiving apparatus obtains the application data from the air interface. With the redundant configuration of the V2V devices, the problem caused by communication failure of one single V2V device can be avoided, so as to ensure stability of V2V communication and guarantee safe operation for platooning.|1-25. (canceled)
| 26. A transmitting apparatus, comprising a first processing device and at least two V2V devices, wherein
the first processing device is configured to transmit application data to the at least two V2V devices, and
the at least two V2V devices are configured to transmit the application data to a predetermined air interface, such that a receiving apparatus obtains the application data from the air interface.
| 27. The transmitting apparatus of claim 26, wherein the first processing device is further configured to:
convert the application data into an Ethernet message, and
transmit the Ethernet message to the at least two V2V devices.
| 28. The transmitting apparatus of claim 27, wherein the at least two V2V devices are further configured to:
packetize the Ethernet message into a V2X message; and
transmit their respectively packetized V2X messages using different frequency bands to air interfaces corresponding to the different frequency bands.
| 29. The transmitting apparatus of claim 28, wherein each of the at least two V2V devices comprises a plurality of antennas, and the at least two V2V devices are further configured to:
transmit their respectively packetized V2X messages using the different frequency bands to the air interfaces corresponding to the different frequency bands via the plurality of antennas provided at each of the at least two V2V devices, wherein each V2V device occupies one frequency band, and the plurality of antennas of each V2V device occupy a same frequency band.
| 30. A receiving apparatus, comprising a second processing device and at least two V2V devices, wherein
each of the at least two V2V devices is configured to obtain application data from an air interface, and
the second processing device is configured to obtain, from each of the at least two V2V devices, the application data corresponding to the V2X device, and fuse and verify the application data to obtain valid data.
| 31. The receiving apparatus of claim 30, wherein:
the air interfaces correspond to a plurality of frequency bands;
each of the at least two V2V devices occupies different one of the plurality of frequency bands and comprises a plurality of antennas;
the plurality of antennas of each V2V device occupy a same frequency band; and
each of the at least two V2V devices is further configured to:
receive V2X messages from air interfaces corresponding to different frequency bands via a plurality of antennas; and
perform signal fusion on the V2X messages received via the plurality of antennas of the V2V device, to form application data information corresponding to the V2V device.
| 32. The receiving apparatus of claim 31, the second processing device is further configured to:
control each of the at least two V2V devices to decode the application data information corresponding to the V2V device, and packetize the decoded application data information into an Ethernet message; and
receive, from each of the at least two V2V devices, the Ethernet message corresponding to the V2V device.
| 33. The receiving apparatus of claim 32, wherein the second processing device is further configured to:
determine, at an end of a current detection period, one or more V2V devices corresponding to the Ethernet message received in the current detection period, the detection period being a predetermined message communication period;
perform, when only one V2V device corresponds to the Ethernet message received in the current detection period, message identity detection on the Ethernet message corresponding to the only one V2V device as received in the current detection period to form a first detection result;
determine whether the Ethernet message corresponding to the only one V2V device as received in the current detection period is valid data or invalid data based on the first detection result.
| 34. The receiving apparatus of claim 33, wherein the second processing device is further configured to:
perform, when more than one V2V device corresponds to the Ethernet message received in the current detection period, message identity detection on the Ethernet message corresponding to the more than one V2V device as received in the current detection period to form a second detection result;
determine an Ethernet message to be discarded and an Ethernet message to be verified based on the second detection result; and
discard the Ethernet message to be discarded, and verify the Ethernet message to be verified to obtain valid data or invalid data.
| 35. The receiving apparatus of claim 33, wherein the second processing device is further configured to:
determine whether an identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is same as an expected message identity known in advance;
set a status flag corresponding to the only one V2V device to a first flag indicating same identity when the identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is same as the expected message identity known in advance; and
maintain a flag corresponding to the only one V2V device as an initial flag to indicate different identity when the identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is different from the expected message identity known in advance.
| 36. The receiving apparatus of claim 35, wherein the second processing device is further configured to:
determine whether the flag corresponding to the only one V2V device is the initial flag or the first flag;
determine that the Ethernet message corresponding to the only one V2V device as received in the current detection period is invalid data when the flag corresponding to the only one V2V device is the initial flag; and
determine that the Ethernet message corresponding to the only one V2V device as received in the current detection period is valid data when the flag corresponding to the only one V2V device is the first flag.
| 37. The receiving apparatus of claim 33, wherein the second processing device is further configured to:
determine whether an identity of the Ethernet message corresponding to each V2V device as received in the current detection period is same as an expected message identity known in advance;
set a status flag corresponding to each V2V device to a first flag indicating same identity when the identity of the Ethernet message corresponding to the V2V device as received in the current detection period is same as the expected message identity known in advance; and
maintain a flag corresponding to each V2V device as an initial flag to indicate different identity when the identity of the Ethernet message corresponding to the V2V device as received in the current detection period is different from the expected message identity known in advance.
| 38. The receiving apparatus of claim 37, the second processing device is further configured to:
determine whether the flag corresponding to each V2V device is the initial flag or the first flag;
determine that the Ethernet message corresponding to each V2V device as received in the current detection period is an Ethernet message to be discarded when the flag corresponding to the V2V device is the initial flag; and
determine that the Ethernet message corresponding to each V2V device as received in the current detection period is an Ethernet message to be verified when the flag corresponding to the V2V device is the first flag.
| 39. The receiving apparatus of claim 37, the second processing device is further configured to:
calculate data bits in the Ethernet message to be verified corresponding to each V2V device in accordance with a predetermined algorithm to obtain a calculation result corresponding to the V2V device, the predetermined algorithm comprising addition, multiplication, MD5 message digest algorithm;
compare the calculation results;
determine the Ethernet message to be verified corresponding to each V2V device to be same, and determine the same Ethernet messages to be verified corresponding to the V2V device as valid data, when the calculation results are same; and
determine the Ethernet message to be verified corresponding to each V2V device as invalid data, when different calculation results exist in the calculation results.
| 40. A method for redundant communication for platooning, comprising:
controlling at least two V2V devices to obtain application data from an air interface; and
obtaining, from the at least two V2V devices, the application data corresponding to the V2X device; and
fusing and verifying the application data to obtain valid data.
| 41. The method of claim 40, wherein:
the air interfaces correspond to a plurality of frequency bands;
each of the at least two V2V devices occupies different one of the plurality of frequency bands and comprises a plurality of antennas;
the plurality of antennas of each V2V device occupy a same frequency band; and
said controlling the at least two V2V devices to obtain the application data from the air interface comprises:
controlling the at least two V2V devices to receive V2X messages from air interfaces corresponding to different frequency bands via a plurality of antennas of each V2V device; and
controlling each of the at least two V2V devices to perform signal fusion on the V2X messages received via the plurality of antennas of the V2V device, to form application data information corresponding to the V2V device.
| 42. The method of claim 41, wherein said obtaining, from the at least two V2V devices, the application data corresponding to the V2X device comprises:
controlling each of the at least two V2V devices to decode the application data information corresponding to the V2V device, and packetize the decoded application data information into an Ethernet message; and
receiving, from each of the at least two V2V devices, the Ethernet message corresponding to the V2V device via a router or a switch.
| 43. The method of claim 42, wherein said fusing and verifying the application data to obtain the valid data comprises:
determining, at an end of a current detection period, one or more V2V devices corresponding to the Ethernet message received in the current detection period, the detection period being a predetermined message communication period;
performing, when only one V2V device corresponds to the Ethernet message received in the current detection period, message identity detection on the Ethernet message corresponding to the only one V2V device as received in the current detection period to form a first detection result;
determining whether the Ethernet message corresponding to the only one V2V device as received in the current detection period is valid data or invalid data based on the first detection result;
performing, when more than one V2V device corresponds to the Ethernet message received in the current detection period, message identity detection on the Ethernet message corresponding to the more than one V2V device as received in the current detection period to form a second detection result;
determining an Ethernet message to be discarded and an Ethernet message to be verified based on the second detection result; and
discarding the Ethernet message to be discarded, and verifying the Ethernet message to be verified to obtain valid data or invalid data.
| 44. The method of claim 43, wherein
said performing the message identity detection on the Ethernet message corresponding to the only one V2V device as received in the current detection period to form the first detection result comprises:
determining whether an identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is same as an expected message identity known in advance;
setting a status flag corresponding to the only one V2V device to a first flag indicating same identity when the identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is same as the expected message identity known in advance; and
maintaining a flag corresponding to the only one V2V device as an initial flag to indicate different identity when the identity of the Ethernet message corresponding to the only one V2V device as received in the current detection period is different from the expected message identity known in advance, and
said determining whether the Ethernet message corresponding to the only one V2V device as received in the current detection period is valid data or invalid data based on the first detection result comprises:
determining whether the flag corresponding to the only one V2V device is the initial flag or the first flag;
determining that the Ethernet message corresponding to the only one V2V device as received in the current detection period is invalid data when the flag corresponding to the only one V2V device is the initial flag; and
determining that the Ethernet message corresponding to the only one V2V device as received in the current detection period is valid data when the flag corresponding to the only one V2V device is the first flag.
| 45. A non-transitory computer readable storage medium, having a computer program stored thereon, the program comprising code configured to perform a method for redundant communication for platooning of claim 40. | The method, involves sending application data to at least two Vehicle-to-vehicle devices (101). The at least two Vehicle-to-vehicle devices is controlled and received the application data to apply the application (102). The data is send to the preset air interface, so that the received device obtains the application data from the air interface. The application data to be send to at least two Vehicle-to-vehicle (V2V) devices has converted the application data to be send into application data Ethernet packets and passed the router or the switch sends the application data Ethernet message to at least two V2V devices. An INDEPENDENT CLAIM is included for the following:a sending end device;a receiving terminal device;a computer readable storage medium on which a computer program is stored; anda synergy automatic driving vehicles of the redundant communication system. Redundant communication method for collaborative autonomous driving fleet. The method ensures stability of V2V communication. The drawing shows the flow chart of the method. 101Involves sending application data to at least two Vehicle-to-vehicle devices102At least two Vehicle-to-vehicle devices is controlled and received the application data to apply the application | Please summarize the input |
Road cloud cooperative automatic driving control method of road end main controlThe invention claims a road cloud cooperative automatic driving control method and method for road end main control, comprising the following steps: S1, building a cloud platform database: S1.3, an ID is allocated for each vehicle in the static road environment model and a corresponding file library is established according to the ID, the vehicle comprises an automatic driving vehicle and a non-automatic driving vehicle, the automatic driving vehicle is allocated with a permanent ID, the non-automatic driving vehicle is allocated with a temporary ID, when the non-automatic driving vehicle drives out of the whole control area for 1 week, the cloud platform automatically deletes the temporary ID of the non-automatic driving vehicle and the corresponding file base; S2, determining the local path planning of the controlled vehicle; The invention endows the automatic driving vehicle and the non-automatic driving vehicle with the ID, and establishes a matched file library, and the data of the non-automatic driving vehicle is periodically deleted according to the rule, which ensures that the database will not store excessive and unused information, which is convenient for the rapid management and application between the edge computing centre and the cloud platform, The transmission is updated.|1. A vehicle road cloud cooperative automatic driving control method for road end main control, wherein The control architecture comprises: S1, constructing a cloud platform database: S1.1, the cloud platform collects the high-precision map through the road side terminal, extracts the content related to the driving and removes the unrelated information; S1.2, establishing a dimension-reducing static road environment model according to the content related to the driving; S1.3, distributing ID for each vehicle in the static road environment model and establishing corresponding file library according to the ID, the vehicle comprises an automatic driving vehicle and a non-automatic driving vehicle, the automatic driving vehicle is distributed with permanent ID, the non-automatic driving vehicle is distributed with temporary ID, when the non-automatic driving vehicle drives out of the whole control area for 1 week, the cloud platform automatically deletes the temporary ID of the non-automatic driving vehicle and the corresponding file base; S1.4, the information of the static road environment model updated by S1.3 is recorded to the database in real time and shared to each edge computing centre according to the area fragment, and returned to S1.2 for updating by timing or event trigger; S2, determining the local path planning of the controlled vehicle: S2.1, the cloud platform determines the controlled vehicle according to the application requirement, and determines the vehicle scheduling instruction and the overall path planning according to the static road environment model; S2.2, the edge computing centre receives the road environment model, the vehicle scheduling instruction and the overall path planning; establishing a real-time dynamic traffic environment model according to the road environment model and the real-time collected dynamic data; S2.3, the edge computing centre according to the real-time dynamic traffic environment model, according to the vehicle scheduling instruction and total path planning for each automatic driving vehicle for local path planning; S3, the controlled vehicle executes the local path planning.
| 2. The road cloud cooperative automatic driving control method for road end main control according to claim 1, wherein In S1.3, the archive of the auto-driving vehicle includes basic information and dynamic information of the vehicle, and the archive of the non-auto-driving vehicle includes vehicle information sensed by the roadside device.
| 3. The road cloud cooperative automatic driving control method for road end main control according to claim 2, wherein the basic information comprises vehicle type, size, power parameter, braking parameter, steering ability, real-time electric quantity, fault state, history state and maintenance record; the dynamic information is the self state data uploaded by the vehicle in real time.
| 4. The road cloud cooperative automatic driving control method for road end main control according to claim 1, wherein In S1.4, the event is an important road event reported by an edge computing centre, a vehicle or a person.
| 5. The road cloud cooperative automatic driving control method for road end main control according to claim 1, wherein In S2.3, the local path planning is updated according to the frequency of 50 HZ, the local path planning comprises ten planning path points, each planning path point carries a coordinate point and a time to reach the path point, and the adjacent distance of the ten planning path points is inversely proportional to the vehicle speed.
| 6. The road cloud cooperative automatic driving control method for road end main control according to claim 1, wherein in S1-S3, the information with high time delay requirement of the controlled vehicle realizes V2I direct connection communication through the PC5 interface of the 5G-OBU module and the edge computing centre, the information with low time delay requirement of the controlled vehicle realizes V2N communication through the Uu interface of the 5G-OBU and the cloud platform; each edge computing centre communicates with the cloud platform through the optical fibre Ethernet. | The vehicle road cloud cooperative automatic driving control method involves constructing a cloud platform database. A cloud platform collects a high-precision map through a road side terminal. A real-time dynamic traffic environment model is established according to a road environment model and real time collected dynamic data. A local path planning of a controlled vehicle is determined. The cloud platform determines the controlled vehicle according to an application requirement. A vehicle scheduling instruction and an overall path planning are determined according to the static road environmental model. The edge computing center receives the road environment models, the vehicle scheduling instructions and the overall path plans. Vehicle road cloud cooperative automatic driving control method for road end main control of vehicle, such as automobile. The data of the non-automatic driving vehicle is periodically deleted according to the rule, which ensures that the database will not store excessive and unused information, which is convenient for the rapid management and application between the edge computing center and the cloud platform. The drawing shows a flow chart of the vehicle road cloud cooperative automatic driving control method. (Drawing includes non-English language text). | Please summarize the input |
Method of using a multi-input and multi-output antenna (MIMO) array for high-resolution radar imaging and wireless communication for advanced driver assistance systems (ADAS) and autonomous drivingA method of using a multi-input multi-output (MIMO) antenna array for high-resolution radar imaging and wireless communication for advanced driver assistance systems (ADAS) utilizes a MIMO radar and at least one base station. The MIMO radar establishes wireless communication with the base station via an uplink signal. Likewise, the base station sends a downlink signal to the MIMO radar. Further, unlike conventional vehicle-to-everything (V2X) systems that filter the reflected uplink signal, the MIMO radar uses the reflected uplink signal to detect a plurality of targets. Accordingly, the MIMO radar derives spatial positioning data for each target from the reflected uplink signal.What is claimed is:
| 1. A method of using a multi-input and multi-output (MIMO) antenna array for radar imaging and wireless communication for advanced driver assistance systems (ADAS) and autonomous driving, the method comprises the steps of:
(A) providing a multi-input and multi-output (MIMO) radar and at least one base station;
(B) transmitting an uplink signal from the MIMO radar to the at least one base station;
(C) receiving a downlink signal from the at least one base station with the MIMO radar;
(D) receiving a reflected uplink signal with the MIMO radar, wherein the reflected signal is reflected off objects surrounding the MIMO radar;
providing a plurality of transmitters, a plurality of receivers, and a RF controller for the MIMO radar;
providing a PN-code regulator managed by the MIMO radar, wherein the reflected uplink signal is encoded with a spread spectrum coding scheme;
receiving an ambient signal with the MIMO radar;
cancelling a cross-talk portion of the ambient signal with the RF controller during step (D), wherein the cross-talk portion is generated from direct communication between the plurality of transmitters and the plurality of receivers;
filtering the reflected uplink signal from the ambient signal with the RF controller during step (D);
dispreading the reflected uplink signal through the PN-code regulator with the RF controller;
estimating a detection time delay for the spatial positioning data for each target with the RF controller;
(E) processing communication data from the downlink signal with the MIMO radar;
(F) detecting a plurality of targets within the reflected uplink signal with the MIMO radar; and
(G) deriving spatial positioning data for each target from the reflected uplink signal with the MIMO radar.
| 2. The method as claimed in claim 1 further comprises the steps of:
providing a pseudo-noise (PN) generator managed by the MIMO radar; and
encoding the uplink signal through the PN generator with the MIMO radar during step (B), wherein a spread spectrum coding scheme is applied to the uplink signal by the PN generator.
| 3. The method as claimed in claim 1 comprises:
providing a RF controller for the MIMO radar;
receiving an ambient signal with the MIMO radar; and
filtering the downlink signal from the ambient signal with the RF controller during step (C).
| 4. The method as claimed in claim 1 further comprises the steps of:
providing an adaptive noise canceller for the MIMO radar; and
capturing the cross-talk portion of the ambient signal with the adaptive noise canceller.
| 5. The method as claimed in claim 1 further comprises the steps of:
executing a plurality of iterations for steps (B) through (G);
transmitting an omni-directional uplink signal during step (B) of an initial iteration, wherein the initial iteration is from the plurality of iterations;
receiving a reflected omni-directional uplink signal during step (D) of the initial iteration; and
detecting a plurality of targets during step (F) of the initial iteration.
| 6. The method as claimed in claim 1 further comprises the steps of:
executing a plurality of iterations for steps (B) through (G);
beamforming a uni-directional uplink signal towards each target detected in a previous iteration during step (B) of an arbitrary iteration, wherein the arbitrary iteration is any iteration from the plurality of iterations, and wherein the previous iteration precedes the arbitrary iteration in the plurality of iterations;
receiving a uni-directional reflected uplink signal for each target detected in the previous iteration during step (D) of the arbitrary iteration; and
detecting a plurality of targets during step (F) of the arbitrary iteration, wherein each target corresponds to the uni-directional reflected uplink signal for each target detected in the previous iteration. | The method involves transmitting an uplink signal from a Multi-Input Multi-Output (MIMO) radar to at least one base station, and receiving a downlink signal from the at least one base station with the MIMO radar. A reflected uplink signal reflected off objects surrounding the MIMO radar is received with the MIMO radar. Communication data from the downlink signal is processed with the MIMO radar. Multiple targets within the reflected uplink signal are detected with the MIMO radar. Spatial positioning data for each target is derived from the reflected uplink signal with the MIMO radar. Method of using a Multi-Input Multi-Output (MIMO) antenna array for high-resolution radar imaging and communication for Advanced Driver Assistance Systems (ADAS) and autonomous driving of vehicle. By encoding the reflected uplink signal with a spread spectrum coding scheme, the bandwidth of the uplink signal is spread and the uplink signal is made more resistant to jamming and noise. Beamforming is made possible by transmitting in-phase signals through each antenna in the antenna array which allows the transmittance of the high-energy uni-directional uplink signal towards each target. The drawing is a schematic diagram of a vehicle communication and radar sensing system. | Please summarize the input |
AUTONOMOUS VEHICLE ACTIVE INTERACTION WITH SURROUNDING ENVIRONMENTAn automated vehicle (AV) which automatically interacts with objects in a surrounding environment based on the objects determined intention and predicted actions determined based on their intention. Data is collected from an external environment by cameras, sensors, and optionally other devices on an AV. The data is processed to identify objects and a state for each object, and an interaction scenario is identified. For objects within the interaction scenario, an intention for each object is determined, and the action of the object is predicted. The AV generates a decision to perform an action to communicate the AV's action to one or more objects. Commands are generated to execute the decision, and the intention of the AV is implemented by executing the commands using one or more output mechanisms (horn, turn signal, display, and/or other mechanisms) for the
AV.|1. An autonomous vehicle system for automatically interacting with a surrounding environment, the system comprising:
a data processing system comprising one or more processors, a memory, a planning module, and a control module, the data processing system to:
detect, from received sensor data, an object in an interaction scenario in an external environment;
monitor the object in response to the detecting the interaction scenario;
determine an intention for the object within the external environment based on the monitoring, wherein the intention is determined based on detected object gestures and a detected object state;
generating an object prediction based on the determined object intention and the detected object state; and
generate one or more commands to indicate an intention of the autonomous vehicle in response to the generated prediction of the object.
| 2. The system of claim 1, the data processing system further to:
predict an action of the object based on the determined intention of the object; and
determine an action to indicate the intention of the autonomous vehicle to the object, the one or more commands generated to implement the action.
| 3. The system of claim 1, the data processing system further to:
detect the interaction scenario based on the received sensor data; and
monitor an activity of the object within the interaction scenario.
| 4. The system of claim 1, wherein the object includes a pedestrian or a vehicle.
| 5. The system of claim 1, wherein the intention of the object is determined at least in part based on gestures performed by the object and detected by the data processing system.
| 6. The system of claim 1, wherein the received sensor data include semantic information to describe the object.
| 7. The system of claim 1, wherein the received sensor data includes one or more of the following: a vehicle location, a vehicle action, a pedestrian location, and a pedestrian action.
| 8. The system of claim 1, wherein the data processing system is further configured to select the intention of the autonomous vehicle based on the current object state.
| 9. The system of claim 1, wherein the autonomous vehicle is configured to signal the intention via at least one of the following: a visual indication, an audio indication, and a Vehicle-to-everything (V2X) communication.
| 10. The system of claim 1, wherein the interaction of the autonomous vehicle is determined at least in part on policies associated with traffic rules.
| 11. The system of claim 1, wherein the intention of the object is determined at least in part based on gestures performed by the object and detected by the data processing system.
| 12. A method for automatically interacting with a surrounding environment by an autonomous vehicle, the method comprising:
detecting, by a data processing system from received sensor data, an object in an interaction scenario in an external environment;
monitoring the object in response to the detecting the interaction scenario;
determine an intention for the object within the external environment based on the monitoring, wherein the intention is determined based on detected object gestures and a detected object state;
generating an object prediction based on the determined object intention and the detected object state; and
generate one or more commands to indicate an intention of the autonomous vehicle in response to the generated prediction of the object.
| 13. The method of claim 12, the data processing system further to:
predict an action of the object based on the determined intention of the object; and
determine an action to indicate the intention of the autonomous vehicle to the object, the one or more commands generated to implement the action.
| 14. The method of claim 12, the data processing system further to:
detect the interaction scenario based on the received sensor data; and
monitor an activity of the object within the interaction scenario.
| 15. The method of claim 12, wherein the object includes a pedestrian or a vehicle.
| 16. The method of claim 12, wherein the intention of the object is determined at least in part based on gestures performed by the object and detected by the data processing system.
| 17. The method of claim 12, wherein the received sensor data include semantic information to describe the object.
| 18. The method of claim 12, wherein the received sensor data includes one or more of the following: a vehicle location, a vehicle action, a pedestrian location, and a pedestrian action.
| 20. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for automatically interacting with a surrounding environment by an autonomous vehicle, the method comprising:
detecting, by a data processing system from received sensor data, an object in an interaction scenario in an external environment;
monitoring the object in response to the detecting the interaction scenario;
determine an intention for the object within the external environment based on the monitoring, wherein the intention is determined based on detected object gestures and a detected object state;
generating an object prediction based on the determined object intention and the detected object state; and
generate one or more commands to indicate an intention of the autonomous vehicle in response to the generated prediction of the object. | The autonomous vehicle system comprises a data processing system having multiple processors, a memory, a planning module (412), and a control module (414). The data processing system detects an object in an interaction scenario in an external environment (510) from received sensor data. The object is monitored in response to the detecting the interaction scenario. An intention is determined for the object within the external environment based on the monitoring. An object prediction is generated based on the determined object intention and the detected object state. The commands are generated to indicate an intention of the autonomous vehicle in response to the generated prediction of the object. INDEPENDENT CLAIMS are included for the following:a method for automatically interacting with a surrounding environment by an autonomous vehicle; anda non-transitory computer readable storage medium. Autonomous vehicle system for automatically interacting with a surrounding environment. Enhances the safety and efficiency of the automated vehicle at intersections. The drawing shows a block representation of a system for automatically interacting with a surrounding environment by an autonomous vehicle.412Planning module414Control module420Perception Module510External environment530Monitoring Module | Please summarize the input |
Surface Detection Via a Directed Autonomous VehicleA number of illustrative variations may include the steps of providing a first vehicle including at least one sensor, a controller configured to process sensor data, and a vehicle communication system; providing a driving surface having an actual coefficient of friction; determining at least one estimated driving surface coefficient of friction; communicating the at least one estimated driving surface coefficient from the first vehicle to the vehicle communication system; and communicating the at least one estimated driving surface coefficient from the vehicle communication system to at least one other vehicle directly or indirectly.What is claimed is:
| 1. A method comprising:
providing a first vehicle comprising at least one sensor, a controller configured to process sensor data, and a vehicle communication system;
driving the first vehicle on a driving surface having an actual coefficient of friction;
determining at least one estimated driving surface coefficient of friction;
communicating the at least one estimated driving surface coefficient from the first vehicle to the vehicle communication system;
communicating the at least one estimated driving surface coefficient from the vehicle communication system to at least one other vehicle.
| 2. A method as set forth in claim 1 wherein the first vehicle further comprises a braking system configured to manipulate a brake set, a steering system configured to adjust a roadwheel direction, and a propulsion system configured to deliver driving power to the road wheels; and
wherein determining at least one estimated driving surface coefficient of friction comprises manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle.
| 3. A method as set forth in claim 1 wherein determining at least one estimated driving surface coefficient of friction is accomplished via the at least one sensor.
| 4. A method as set forth in claim 1 wherein the vehicle communication system is a cloud-based vehicle-to-vehicle communication system.
| 5. A method as set forth in claim 1 wherein the vehicle communication system is a vehicle-to-everything communication system.
| 6. A method as set forth in claim 1 wherein the first vehicle is an unmanned ground vehicle.
| 7. A method as set forth in claim 1 wherein the first vehicle is an unmanned aerial vehicle.
| 8. A method as set forth in claim 1, further comprising using the at least one estimated driving surface coefficient of friction to manipulate at least one of a braking system, a steering system, or a propulsion system of the at least one other vehicle.
| 9. A method comprising:
providing an unmanned ground vehicle comprising at least one sensor, a controller configured to process sensor data, and a vehicle communication system;
driving the first vehicle on a driving surface having an actual coefficient of friction;
determining at least one estimated driving surface coefficient of friction via the at least one sensor; and
communicating the at least one estimated driving surface coefficient from the unmanned ground vehicle to the vehicle communication system.
| 10. A method as set forth in claim 9, further comprising communicating the at least one estimated driving surface coefficient from the vehicle communication system to at least one other vehicle.
| 11. A method as set forth in claim 10 wherein the at least one other vehicle comprises a braking system configured to manipulate a brake set, a steering system configured to adjust a roadwheel direction, and a propulsion system configured to deliver driving power to the road wheels; and
further comprising using the at least one estimated driving surface coefficient of friction to manipulate at least one of the braking system, steering system, and propulsion system of the other vehicle.
| 12. A method as set forth in claim 9 wherein the vehicle communication system is a cloud-based vehicle-to-vehicle communication system.
| 13. A method as set forth in claim 9 wherein the vehicle communication system is a vehicle-to-everything communication system
| 14. A method as set forth in claim 9 wherein determining at least one estimated driving surface coefficient of friction via the at least one sensor additionally comprises performing unmanned ground vehicle maneuvers comprising manipulating at least one of vehicle speed, acceleration, direction, or braking.
| 15. A method comprising:
providing an unmanned aerial vehicle comprising at least one sensor, a controller configured to process sensor data, and a vehicle communication system;
determining at least one estimated driving surface coefficient of friction of a driving surface via the at least one sensor; and
communicating the at least one estimated driving surface coefficient from the unmanned aerial vehicle to the vehicle communication system.
| 16. A method as set forth in claim 15, further comprising communicating the at least one estimated driving surface coefficient from the vehicle communication system to at least one other vehicle.
| 17. A method as set forth in claim 16 wherein the at least one other vehicle comprises a braking system configured to manipulate a brake set, a steering system configured to adjust a roadwheel direction, and a propulsion system configured to deliver driving power to the road wheels; and
further comprising using the at least one estimated driving surface coefficient of friction to manipulate at least one of the braking system, steering system, or propulsion system of the at least one other vehicle.
| 18. A method as set forth in claim 16 wherein the vehicle communication system is a cloud-based vehicle-to-vehicle communication system.
| 19. A method as set forth in claim 16 wherein the vehicle communication system is a vehicle-to-everything communication system.
| 20. A method as set forth in claim 2 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed at the maximum capability of the first vehicle.
| 21. A method as set forth in claim 2 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed without a passenger in the vehicle at the a capability of the first vehicle that would otherwise result in injury to a passenger in the vehicle.
| 22. A method as set forth in claim 2 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed without cargo in the vehicle at the a capability of the first vehicle that would otherwise result in damage to a cargo in the vehicle.
| 23. A method as set forth in claim 11 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed at the maximum capability of the first vehicle.
| 24. A method as set forth in claim 11 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed without a passenger in the vehicle at the a capability of the first vehicle that would otherwise result in injury to a passenger in the vehicle.
| 25. A method as set forth in claim 11 wherein the manipulating at least one of the braking system, steering system, or propulsion system of the first vehicle is performed without cargo in the vehicle at the a capability of the first vehicle that would otherwise result in damage to a cargo in the vehicle. | The method involves providing a vehicle (14) with a sensor (16), where a control unit (18) is set up to process a sensor data (20). The vehicle communication system (22) is provided, where the vehicle is driven (24) on a roadway (26) with an actual coefficient of friction (28). The estimated road surface friction coefficient (32) is determined (30), where the estimated road surface coefficient is communicated (34) from the vehicle to the vehicle communication system. The estimated road surface coefficient is communicated (36) from the vehicle communication system to another vehicle (38). The vehicle is provided with a braking system (40) to actuate a set of brakes (42). Method for performing the surface detection by a guided autonomous vehicle. The control unit is set up to process a sensor data, where the vehicle is driven on a roadway with an actual coefficient of friction, and hence enables preventing the unintended imbalances in the driving force transferred from each wheel to a vehicle and performs the surface detection by a guided autonomous vehicle effectively. The drawing shows a flowchart of a method for performing the surface detection by a guided autonomous vehicle. 12Providing a vehicle with a sensor14,38Vehicles16Sensor18Control unit20Sensor data22Vehicle communication system24Driving a vehicle on a roadway with an actual coefficient of friction26Roadway28Actual coefficient of friction30Determining an estimated road surface friction coefficient32Estimated road surface friction coefficient34Communicating an estimated road surface coefficient from a vehicle to a vehicle communication system36Communicating a road surface coefficient from a vehicle communication system to another vehicle40Braking system42Brakes | Please summarize the input |
Safety method for a modular autonomous vehicle and a control device thereforA safety method, performed by a control device for a vehicle assembled from a set of modules, the vehicle including at least two modules, including at least one drive module and at least one functional module. The control device is in any of the at least two modules. The at least one drive module has a pair of wheels and is configured to be autonomously operated. The method includes detecting (s 101) an emergency situation in any of the at least two modules of the assembled vehicle, transmitting (s102) information about the detected emergency situation to a control center and controlling (s103) the module associated with the emergency situation to physically disconnect from the assembled vehicle. Also to a computer program, a computer-readable medium, a control device and a vehicle are included.The invention claimed is:
| 1. A safety method, performed by a control device for a vehicle assembled from a set of modules, the vehicle comprising one or more of at least two modules, including:
at least one drive module; and
at least one functional module;
wherein the control device is comprised in any one or more of the at least two modules and wherein the at least one drive module comprises a pair of wheels and is configured to be autonomously operated;
the method comprising:
detecting, by a first sensor element, an emergency situation in any one or more of the at least two modules of the assembled vehicle;
transmitting, by a transmitter, information about the detected emergency situation to a control center; and
controlling the module associated with the emergency situation to physically disconnect from the assembled vehicle.
| 2. The method according to claim 1, further comprising:
controlling the disconnected module to move away from the at least one remaining module of the assembled vehicle and/or controlling the at least one remaining module of the assembled vehicle to move away from the disconnected module.
| 3. The method according to claim 1, wherein, after transmitting information about the detected emergency situation to a control center, and before controlling the module associated with the emergency situation, physically disconnecting the module from the assembled vehicle;
the method further comprising:
receiving, from the control center, a command to physically disconnect the at least one drive module from the assembled vehicle.
| 4. The method according to claim 1, wherein, before controlling the module associated with the emergency situation to physically disconnect from the assembled vehicle, the method further comprises:
activating an alarm informing about the emergency situation.
| 5. The method according to claim 1, further comprising:
identifying a safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment; and
controlling the assembled vehicle to move to the identified safe space prior to physically disconnecting the module.
| 6. The method according to claim 5, wherein identifying the safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment is performed by means of a second sensor element comprising a radar, a lidar or a camera.
| 7. The method according to claim 5, wherein identifying the safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment is based on information from the control center via 4G, 5G, V2I, Wi-Fi or any other wireless communication means.
| 8. The method according to claim 5, wherein identifying the safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment is based on a type of the at least one functional module.
| 9. The method according to claim 5, wherein identifying the safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment is based on a type of load in the at least one functional module.
| 10. The method according to claim 5, wherein identifying the safe space where the emergency situation in the assembled vehicle will have a reduced impact on the environment is based on a type of emergency situation in the assembled vehicle.
| 11. The method according to claim 1, wherein controlling the module associated with the emergency situation to physically disconnect from the assembled vehicle also comprises controlling the module to electrically disconnect from the assembled vehicle.
| 12. The method according to claim 1, wherein the assembled vehicle comprises two drive modules and the at least one functional module, and wherein one of the drive modules is configured to operate as a master and the other drive module is configured to operate as a slave;
the method further comprises, when an emergency situation is detected in the master drive module:
controlling the drive module configured to operate as a slave to operate as master.
| 13. The method according to claim 12, further comprising, when an emergency situation is detected in the at least one functional module:
controlling both drive modules to physically disconnect from the assembled vehicle.
| 14. The method according to claim 1, further comprising detecting an emergency situation by means of the first sensor element including a temperature sensor, a pressure sensor, a smoke sensor, a particle sensor, a gas sensor and/or a camera arranged on the assembled vehicle.
| 15. A computer memory storing program instructions which, when the program instructions are executed by a computer, causes the computer to carry out a method performed by the computer for a vehicle assembled from a set of modules, wherein the vehicle comprises one or more of at least two modules including: at least one drive module and at least one functional module, wherein the computer is comprised in any one or more of the at least two modules and wherein the at least one drive module comprises a pair of wheels and is configured to be autonomously operated, wherein the method comprises:
detecting, by a first sensor element, an emergency situation in any one or more of the at least two modules of the assembled vehicle;
transmitting, by a transmitter, information about the detected emergency situation to a control center; and
controlling the module associated with the emergency situation to physically disconnect from the assembled vehicle.
| 16. A control device of a vehicle assembled from a set of modules, the vehicle comprising one or more of at least two modules, including:
at least one drive module; and
at least one functional module;
wherein the control device is comprised in any one or more of the at least two modules, and wherein the at least one drive module comprises a pair of wheels and is configured to be autonomously operated;
the control device being configured to:
detect, by a first sensor element, an emergency situation in any one or more of the at least two modules of the assembled vehicle;
transmit, by a transmitter, information about the detected emergency situation to a control center; and
control the module associated with the detected emergency situation to physically disconnect from the assembled vehicle.
| 17. A vehicle assembled from a set of modules, wherein the vehicle comprises at least one control device, wherein the set of modules comprises one or more of at least two modules including: at least one drive module, and at least one functional module; and wherein the control device is comprised in any one or more of the at least two modules; and wherein the at least one drive module comprises a pair of wheels and is configured to be autonomously operated; and the control device being configured to:
detect, by a first sensor element, an emergency situation in any one or more of the at least two modules of the assembled vehicle;
transmit, by a transmitter, information about the detected emergency situation to a control center; and
control the module associated with the detected emergency situation to physically disconnect from the assembled vehicle. | The method involves detecting (s101) an emergency situation in any of two modules of an assembled vehicle. The information about the detected emergency situation is transmitted (s102) to a control centre. The module associated with the emergency situation is controlled (s103) to physically disconnect from the assembled vehicle. The disconnected module is controlled to move away from the remaining module of the assembled vehicle, and/or the remaining module of the assembled vehicle is controlled to move away from the disconnected module. The command is received to physically disconnect the drive module from the assembled vehicle. INDEPENDENT CLAIMS are included for the following:a computer program for vehicle assembled from set of modules;a control device of vehicle assembled from set of modules; anda vehicle assembled from set of modules. Safety method for vehicle such as bus and truck assembled from set of modules. The assembled vehicle is quickly and easily disassembled without manual work. The safe distance to the surrounding objects is maintained, and the accidents are avoided. The assembled vehicle is controlled to move to the identified safe space prior to physically disconnecting the module. The drawing shows a flowchart illustrating the safety process. s101Step for involves detecting emergency situation in any of two modules of assembled vehicles102Step for transmitting information about detected emergency situation to control centres103Step for controlling module associated with the emergency situation to physically disconnect from assembled vehicle | Please summarize the input |
FRICTION MONITORING SYSTEM FOR A VEHICLE AND A METHOD PERTAINING TO SUCH A SYSTEMA friction monitoring system (2) for vehicles (4, 16) which comprises a slipperiness detection device (6) suited to making measurements of at least one parameter related to slipperiness of a roadway close to a first vehicle (4), to determining at least one friction value on the basis of the measurement and to generating a friction signal (8) comprising said friction value determined. Also provided is a processing device (10) adapted to receiving said friction signal (8) and to generating a slipperiness information signal (12) comprising said friction value. The friction monitoring system (2) comprises also a first communication device (14) situated in the first vehicle (4) and adapted to receiving said slipperiness information signal (12) and to transmitting a processed slipperiness information signal (15) wirelessly in a format such that one or more other vehicles (16) can receive the processed signal (15), process it and, where necessary, activate at least one skid protection system (17) in said other vehicle on the basis of the information contained in the processed signal (15), said slipperiness information signal (12) being arranged to be passed on and, where necessary, to activate at least one skid protection system (22) of the first vehicle (4) in accordance with a set of dynamic activation rules.|1. A friction monitoring system (2) for vehicles (4, 16) which comprises a slipperiness detection device (6) suited to making measurements of at least one parameter related to slipperiness of a roadway close to a first vehicle (4), to determining at least one friction value on the basis of the measurement and to generating a friction signal (8) comprising said friction value determined, a processing device (10) adapted to receiving said friction signal (8) and to generating a slipperiness information signal (12) comprising said friction value, a first communication device (14) situated in said first vehicle (4) and adapted to receiving said slipperiness information signal (12) and to transmitting a processed slipperiness information signal (15) wirelessly in a format such that one or more other vehicles (16) can receive the processed signal (15), process it and, where necessary, activate at least one skid protection system (17) in said other vehicle on the basis of the information in the processed signal received (15), said slipperiness information signal (12) is arranged to be passed on and, where necessary, to activate at least one skid protection system (22) of the first vehicle (4) in accordance with a set of dynamic activation rules characterised in that said set of dynamic activation rules comprises parameters related to nearby vehicles.
| 2. The friction monitoring system (2) according to claim 1, in which said nearby vehicles are part of a vehicle train.
| 3. The friction monitoring system (2) according to claim 2, in which said parameters comprise the length of the vehicle train.
| 4. The friction monitoring system (2) according to any one of claims 1-3, which comprises a location determination device (19) adapted to determining the location of the first vehicle, to determining a location value on the basis of the location determined and to generating a location signal (20) on the basis of said location value determined, said processing device (10) being adapted to receiving said location signal (20) and to generating said slipperiness information signal (12) comprising coordinated friction values and location values.
| 5. The friction monitoring system (2) according to claim 4, in which the processing device (10) is adapted to relating each friction value to a location value so that a specific friction value unambiguously indicates how slippery the roadway is at a given location.
| 6. The friction monitoring system (2) according to any one of claims 1-5, in which at least one of said first and second vehicles (4,16) is part of a vehicle train.
| 7. The friction monitoring system (2) according to any one of claims 1-6, in which at least one of said first and second vehicles (4,16) is an autonomous vehicle in a vehicle train.
| 8. The friction monitoring system (2) according to any one of the foregoing claims, in which said format for the processed slipperiness information signal (15) is suited to vehicle-to-vehicle transmission.
| 9. The friction monitoring system (2) according to any one of the foregoing claims, in which said format for the processed slipperiness information signal (15) is suited to vehicle-to-infrastructure transmission.
| 10. A method for a skid protection system for vehicles, which method comprises
* - making measurements of at least one parameter related to slipperiness of a roadway close to a first vehicle,
* - determining at least one friction value on the basis of the measurement,
* - generating a friction signal comprising said friction value determined,
* - receiving said friction signal in a processing device and generating a slipperiness information signal comprising said friction value,
* - receiving said slipperiness information signal in a communication device in said first vehicle,
* - sending a processed slipperiness information signal out wirelessly in a format such that one or more other vehicles can receive the signal,
* - processing the processed slipperiness information signal received and, where necessary, activating at least one skid protection system in said other vehicle on the basis of the information in the slipperiness information signal received, and
* - acting upon and, where necessary, activating at least one skid protection system of the first vehicle in accordance with a set of dynamic activation rules and characterised in that said set of dynamic activation rules comprises parameters related to nearby vehicles.
| 11. The method according to claim 10, in which said nearby vehicles are part of a vehicle train.
| 12. The method according to claim 11, in which said parameters comprise the length of the vehicle train.
| 13. The method according to any one of claims 10-12, comprises
* - determining the location of the first vehicle in a location measuring device,
* - determining a location value on the basis of the location determined,
* - generating a location signal on the basis of said location value,
* - receiving said location signal in said processing device,
* - generating said slipperiness information signal comprising coordinated friction values and location values.
| 14. The method according to claim 13, in which the processing device is adapted to relating each friction value to a location value so that a specific friction value unambiguously indicates how slippery the roadway is at a given location.
| 15. The method according to any one of claims 10-14, in which at least one of said first and second vehicles is part of a vehicle train.
| 16. The method according to any one of claims 10-15, in which at least one of said first and second vehicles is an autonomous vehicle in a vehicle train.
| 17. The method according to any one of claims 10-16, in which said format for the processed slipperiness information signal is suited to vehicle-to-vehicle transmission.
| 18. The method according to any one of claims 10-17, in which said format for the processed slipperiness information signal is suited to vehicle-to-infrastructure transmission.
| 19. A computer programme (P) for vehicles, which programme (D) comprises programme code for causing a processing device (10; 500) or another computer (500) connected to the processing device (10; 500) to perform steps of the method according to any one of claims 10-18.
| 20. A computer programme product comprising a programme code stored on a computer-readable medium for performing method steps according to any one of claims 10-18 when said programme code is run on a processing device (10; 500) or another computer (500) connected to the processing device (10; 500). | The friction monitoring system has a communication device (14) in a host vehicle (4) to receive a slipperiness information signal (12) and transmit wirelessly a processed slipperiness information signal (15) to other vehicles (16), activating the skid protection system (17) of the other vehicle. The slipperiness information signal is also used to active the skid protection system (22) of the host vehicle in accordance with set of dynamic activation rules. INDEPENDENT CLAIMS are included for the following:a method for a skid protection system for vehicles;a computer program for vehicles; anda computer program product. Friction monitoring system for vehicles, such as autonomous vehicles. Slipperiness information can be disseminated to other vehicles included in a vehicle train, improving traffic safety as well as safety of the vehicle. The drawing shows the block diagram of a friction monitoring system. 4Host vehicle12Slipperiness information signal14Communication device15Processed slipperiness information signal16Other vehicles17Skid protection system of other vehicles22Skid protection system of host vehicle | Please summarize the input |
Positioning quality filter for the V2X technologiesThis provides methods and systems for V2X applications, such as forward collision warning, electronic emergency brake light, left turn assist, work zone warning, signal phase timing, and others, mainly relying on a GNSS positioning solution transmitted via the Dedicated Short-Range Communications (DSRC) to/from the roadside units and onboard units in other V2X-enabled vehicles. However, the positioning solution from a GNSS may be deteriorated by noise and/or bias due to various error sources, e.g., time delay, atmospheric effect, ephemeris effect, and multipath effect. This offers a novel quality filter that can detect noise and the onset of drift in GNSS signals by evaluating up to four metrics that compare the qualities of kinematic variables, speed, heading angle change, curvature, and lateral displacement, obtained directly or derived from GNSS and onboard vehicle sensors. This is used for autonomous cars and vehicle safety, with various examples/variations.The invention claimed is:
| 1. A method for positioning quality filter for a global navigation system for a vehicle, said method comprising:
a central computer receiving global positioning system location data;
said central computer receiving sensors data from vehicle sensors;
said sensors data from said vehicle sensors comprises data from a vehicle speed sensor, a vehicle direction sensor, and a vehicle yaw rate sensor on said vehicle;
said central computer calculating a first metric value based on said sensors data from said vehicle sensors, based on said vehicle speed, said vehicle direction, and said vehicle yaw rate;
a processor receiving a first threshold;
said processor comparing said first metric value with said first threshold;
after determining that said first metric value is larger than or equal to said first threshold, said processor receiving a second threshold;
a) said central computer calculating a second metric value;
b) said processor comparing said second metric value with said second threshold;
c) after determining that said second metric value is smaller than said second threshold, said processor receiving a third threshold;
a. said central computer calculating a third metric value;
b. said processor comparing said third metric value with said third threshold;
c. after determining that said third metric value is larger than or equal to said third threshold, said processor receiving a fourth threshold;
a. said central computer calculating a fourth metric value;
b. said processor comparing said fourth metric value with said fourth threshold;
c. after determining that said fourth metric value is smaller than said fourth threshold, said processor setting said global navigation system value as valid;
said central computer validating said global positioning system location data 9 using said global navigation system value, for safety, operation, or navigation of said vehicle;
said central computer sending a notice to a vehicle warning device;
said central computer correcting a navigation of said vehicle; said central computer adjusting direction of said vehicle.
| 2. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, wherein said first threshold is not greater than 1.
| 3. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning an operator.
| 4. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning an driver.
| 5. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning headquarters.
| 6. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning a central server.
| 7. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning another driver.
| 8. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: warning another car.
| 9. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: communicating with pedestrians.
| 10. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: communicating with cloud.
| 11. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: communicating with server farms.
| 12. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: communicating with police.
| 13. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: communicating with a grid, a secured network, or outside car sensors.
| 14. The method for positioning quality filter for a global navigation system for a vehicle, as recited in claim 1, said method comprises: resolving conflict between sensors and/or received data. | The positioning method involves use of processor for comparing primary metric value with primary threshold. The processor compares fourth metric value with fourth threshold. The processor sets the global navigation system value as valid in case fourth metric value is smaller than fourth threshold. The processor sets the global navigation system value as invalid when fourth metric value is larger than or equal to fourth threshold. A central computer validates global positioning system location data using global navigation system value, for safety, operation, or navigation of vehicle. An INDEPENDENT CLAIM is also included for a method for positioning quality filter for a positioning system for an automated or autonomous vehicle. Positioning method for quality filter of global navigation system for vehicle e.g. automated or autonomous vehicle. A weighted-averaging process based on the redundancies between coverage of different units, to weighted-average of the data for more accurate results, with more weights for the more reliable units or sources, or higher weights for the results that are closer to the center of curve representing the distribution of values, eliminating or reducing the fringe results or erroneous data. Such estimates and statistics for patterns or behaviors for people are very valuable for marketing and sales people who want to predict and plan ahead. The drawing shows a representation of development of fully automated vehicles, in stages. | Please summarize the input |
Methods and systems for V2X congestion control using directional antennas, and determining OBU transmission power based on the weather data received from vehicle CANSelf-driving and autonomous vehicles are very popular these days for scientific, technological, social, and economical reasons. In one aspect of this technology, one of the main concerns for an implementation of any V2X technology on a large scale is the issue of congestion control. In large cities and crowded highways during rush hours, each host vehicle can get messages from over 200 other vehicles and several road side units, all working on the same channel and trying to send and receive messages at the same time. With respect to the weather effect on signal, the signal path loss occurs whenever there is moderate (or moderate plus) rain, and because of that, the OBU communications packets are prone to get lost, or communication coverage region gets diminished, depending upon the intensity, speed, angle and temperature of the rainfall/snowfall droplets. We have provided the solutions for these 2 problems, with variations.The invention claimed is:
| 1. A method for an autonomous or automated vehicle operation, said method comprising:
a central computer receiving a vehicle state for said vehicle;
a communication channel for said vehicle transmitting in an omni-directional radiation pattern;
said communication channel for said vehicle receiving messages from all directions;
in case said vehicle state indicating a congestion on said communication channel for said vehicle, said communication channel for said vehicle changing to transmission in a specific directional pattern, and said communication channel for said vehicle continuing receiving messages from all directions.
| 2. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
in case channel congestion is detected, said communication channel for said vehicle changing to transmission in a directional pattern toward front and back of said vehicle, and said communication channel for said vehicle continuing receiving messages from all directions.
| 3. The method for an autonomous or automated vehicle operation, as recited in claim 2, said method comprises:
said communication channel for said vehicle switching between transmitting in an omni-directional radiation pattern and transmitting in a directional pattern toward said front and back of said vehicle, based on channel congestion.
| 4. The method for an autonomous or automated vehicle operation, as recited in claim 2, said method comprises:
said communication channel for said vehicle switching between transmitting in an omni-directional radiation pattern and transmitting in a directional pattern toward said front and back of said vehicle, based on driving speed.
| 5. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
monitoring said state for said vehicle, or
monitoring said congestion on said communication channel for said vehicle.
| 6. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
said communication channel for said vehicle switching to transmitting in an omni-directional radiation pattern.
| 7. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
monitoring a threshold value for said congestion on said communication channel for said vehicle.
| 8. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
monitoring a threshold value based on number of messages, or rate of messages for said congestion on said communication channel for said vehicle, or
monitoring a threshold value based on number of cars for said congestion on said communication channel for said vehicle.
| 9. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
monitoring a threshold value based on bandwidth capacity of said communication channel for said congestion on said communication channel for said vehicle.
| 10. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using multiple of directional transmission schemes or patterns.
| 11. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using map of a road for switching on transmission methods.
| 12. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using map and elevation data of a road for optimization of switching on transmission methods.
| 13. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using a combination of directional transmission and non-directional transmission simultaneously.
| 14. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using 2 of directional transmission schemes or patterns.
| 15. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using antenna arrays, a group of antennas, Cassegrain antenna, or parabolic antenna.
| 16. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using map and intersections data as potential danger points for a road for optimization of switching on transmission methods.
| 17. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
using a transition mode of transmission between and for optimization of switching on transmission methods.
| 18. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
independently optimizing transmission and listening modes or methods.
| 19. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
combining optimization of transmission and listening modes or methods.
| 20. The method for an autonomous or automated vehicle operation, as recited in claim 1, said method comprises:
optimizing of transmission based on multiple thresholds, conditions, or triggers. | The method involves comprising a central computer receiving a vehicle state for the vehicle. A communication channel is for the vehicle transmitting in an omni-directional radiation pattern. The communication channel for the vehicle receives messages from all directions. The communication channel for the vehicle changing to transmission in a specific directional pattern, and the communication channel for the vehicle continues receiving messages from all directions in case the vehicle state indicates congestion on the communication channel for the vehicle. Method for operation of autonomous or automated vehicle such as car, sedan, truck, bus, pickup truck, sport utility vehicle (SUV), tractor, agricultural machinery, entertainment vehicles, motorcycle, bike, bicycle, and hybrid. The overall number of messages each target gets is reduced and the channel congestion is avoided. The drawing shows a block diagram for a system with monitoring congestion with multiple modes of transmission. | Please summarize the input |
Automatic driving method and systemThe embodiment of the invention claims an automatic driving system, comprising: obtaining the image data of the peripheral vehicle of the automatic driving vehicle by an image processing unit, and processing the image data to obtain the first driving state information of the peripheral vehicle; communicating with the peripheral vehicle in the V2X communication distance by the V2X communication unit to obtain the second driving state information of the peripheral vehicle; through the decision unit, according to the first driving state information of the peripheral vehicle, or the first driving state information and the second driving state information, determining the driving action of the automatic driving vehicle, The invention solves the problem that the traffic safety is influenced by the wrong judgement of the surrounding vehicle caused by the special condition of the automatic driving vehicle in the related technology, and improves the automatic driving safety.|1. An automatic driving system, wherein it is set on the automatic driving vehicle, comprising: an image processing unit for obtaining the image data of the peripheral vehicle of the automatic driving vehicle and processing the image data to obtain the first driving state information of the peripheral vehicle; a vehicle network V2X communication unit for obtaining the second driving state information of the peripheral vehicle by communicating with the peripheral vehicle in the V2X communication distance; a decision unit for determining the driving action of the automatic driving vehicle according to the first driving state information of the peripheral vehicle or the first driving state information and the second driving state information.
| 2. The system according to claim 1, wherein the first driving state information of the peripheral vehicle comprises at least one of the following: vehicle profile information; vehicle orientation information.
| 3. The system according to claim 1, wherein the second driving state information of the peripheral vehicle comprises at least one of: vehicle identification information; vehicle latitude and longitude information; vehicle driving speed information; vehicle driving direction information.
| 4. The system according to claim 1, wherein the driving action of the automatic driving vehicle comprises at least one of the following: running at uniform speed; accelerating to drive; reducing speed to drive; Road change.
| 5. The system according to claim 1, wherein the decision unit further comprises: a distance outer decision sub-unit for calculating the driving speed and driving direction of the peripheral vehicle according to the first driving state information outside the V2X communication distance, and determining the driving action of the automatic driving vehicle according to the driving speed and driving direction of the peripheral vehicle.
| 6. The system according to claim 1, wherein the decision unit further comprises: a distance inner decision sub-unit, for in the V2X communication distance, judging whether the first driving state information and the second driving state information are the information of the same peripheral vehicle, if so, then according to the first driving state information and the second driving state information, determining the driving speed and driving direction of the peripheral vehicle, if not, then according to the second driving state information, determining the driving speed and driving direction of the peripheral vehicle, and determining the driving action of the automatic driving vehicle according to the driving speed and the driving direction of the peripheral vehicle.
| 7. An automatic driving method, wherein it is used for automatic driving vehicle, comprising: obtaining the image data of the peripheral vehicle of the automatic driving vehicle, and processing the image data to obtain the first driving state information of the peripheral vehicle; obtaining second driving state information of the peripheral vehicle by communicating with the peripheral vehicle within a V2X communication distance; determining the driving action of the automatic driving vehicle according to the first driving state information of the peripheral vehicle, or the first driving state information and the second driving state information.
| 8. The method according to claim 7, wherein the step of determining the driving action of the automatic driving vehicle according to the first driving state information comprises: out of the V2X communication distance, calculating the driving speed and driving direction of the peripheral vehicle according to the first driving state information; and determining the driving action of the automatic driving vehicle according to the driving speed and the driving direction of the peripheral vehicle.
| 9. The method according to claim 7, wherein the step of determining the driving action of the automatic driving vehicle according to the first driving state information and the second driving state information comprises: in the V2X communication distance, judging whether the first driving state information and the second driving state information are the information of the same peripheral vehicle, if so, determining the driving speed and driving direction of the peripheral vehicle according to the first driving state information and the second driving state information, if not, determining the driving speed and driving direction of the peripheral vehicle according to the second driving state information; and determining the driving action of the automatic driving vehicle according to the driving speed and the driving direction of the peripheral vehicle.
| 10. A computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, wherein the computer program is executed by a processor to realize the method according to any one of claims.
| 11. An electronic device, comprising a memory, a processor and a computer program stored on the memory and capable of being operated on the processor, wherein the method according to any one of claims is realized when the computer program is executed by the processor. | The system (20) has an image processing unit (210) that is provided for obtaining the image data of the peripheral vehicle of the automatic driving vehicle and processes the image data to obtain the first driving state information of the peripheral vehicle. A vehicle network vehicle to vehicle (V2V) communication unit (220) is provided for obtaining the second driving state information of the peripheral vehicle by communicating with the peripheral vehicle in the communication distance. A decision unit (230) is provided for determining the driving action of the automatic driving vehicle according to the first driving state information of the peripheral vehicle or the first driving state information and the second driving state information. INDEPENDENT CLAIMS are included for the following:an automatic driving method applied to an automatic driving vehicle;a computer readable storage medium storing program for providing the automatic driving of vehicle; andan electronic device. Automatic driving system for automatic driving vehicle. The system solves the defect that the peripheral vehicle state information in the existing technology is not accurate, and the different system modules are used at different vehicle distances to improve the precision of the obtained peripheral vehicle state information, so as to make the automatic driving vehicle to perform better reaction action, so as to achieve safer driving target. The drawing shows a block diagram of the automatic driving system. (Drawing includes non-English language text) 20Automatic driving system210Image processing unit220Vehicle to vehicle communication unit230Decision unit | Please summarize the input |
WAFER TRANFERING AUTOMATION SYSTEM AND OPEPRATING METHOD THEREOFThe wafer transport automation system according to the present invention loads at least one airtight container including a plurality of Front Opening Unified Pods (FOUPs) and performs vehicle-to-vehicle communication. An autonomous robot that transfers or loads at least one airtight container and moves the loaded airtight container so as to closely dock with a partition wall dividing a clean room and a non-clean room, and the at least one airtight container loaded on the autonomous robot An interface device that transfers each of a plurality of FOUPs between a container and a conveyor in the clean room, and a transport that performs wireless communication with the autonomous vehicles, the autonomous robot, and the interface device to perform wafer transport automation May contain automation servers.|1. Autonomous vehicles that load at least one airtight container including a plurality of Front Opening Unified Pods (FOUPs) and perform vehicle-to-vehicle communication;
an autonomous robot that transfers or loads the at least one airtight container from each of the autonomous vehicles and moves the loaded airtight container so that the airtight container is closely docked with a bulkhead dividing a clean room and a non-clean room;
an interface device for transferring each of a plurality of FOUPs between the at least one airtight container loaded in the autonomous robot and the conveyor of the clean room; and a transfer automation server that performs wireless communication with the self-driving vehicles, the self-driving robot, and the interface device to perform wafer transfer automation.
| 2. The wafer transport automation system according to claim 1, wherein the at least one sealing container includes a sealing unit performing an internal/external blocking function.
| 3. The wafer transport automation system of claim 1, wherein the at least one sealed container includes at least one damped vibration unit performing a low/no vibration function.
| 4. The wafer transport automation system according to claim 1, wherein the at least one airtight container inputs and outputs nitrogen gas to maintain constant temperature/humidity/low moisture.
| 5. The wafer transport automation system according to claim 1, wherein the at least one sealed container includes a Radio Frequency Identification (RFID) tag for computerized lot management.
| 6. The method of claim 1, wherein each of the self-driving vehicles performs a function of automatically opening and closing a loading box door for inputting/dispensing of the airtight container, maintaining a constant temperature/humidity inside the vehicle body, or performing a function of maintaining a constant temperature/humidity inside the vehicle body while driving. A wafer transfer automation system characterized in that it performs a low-vibration / no-vibration maintenance function.
| 7. The wafer transport automation system according to claim 1, wherein each of the autonomous vehicles performs parallel input output (PIO) communication with an autonomous robot or wireless communication with the transport automation server.
| 8. The method of claim 1, wherein the self-driving robot supplies nitrogen gas to the at least one airtight container, performs PIO (Parallel Input Output) communication with each of the self-driving vehicles, or communicates with the transfer automation server and the wireless A wafer transfer automation system characterized in that it performs communication.
| 9. The method of claim 1, wherein the interface device opens and closes a door of the bulkhead, opens and closes a door of the at least one airtight container, performs PIO (Parallel Input Output) communication with the autonomous robot, or the transfer automation server. And the wafer transfer automation system, characterized in that performing the wireless communication.
| 10. A method of operating a wafer transport automation system, comprising: moving an airtight container including a plurality of Front Opening Unified Pods (FOUPs) from a first factory using an autonomous vehicle;
transferring the airtight container of the self-driving vehicle to the self-driving robot;
closely docking the airtight container to a partition wall between a clean room and a non-clean room of a second factory;
opening the airtight container by an interface device and dispensing each of the plurality of FOUPs to a conveyor of the clean room; and transferring the ejected FOUP to the conveyor. | The system has an airtight container (130) including multiple front opening unified pods (FOUPs) for performing vehicle-to-vehicle communication. An autonomous robot (200) transfers or loads the container from each of autonomous vehicles (100) and moves the loaded container such that the container is closely docked with a bulkhead dividing a clean room and a non-clean room. An interface device (300) transfers each of the FOUPs between the container loaded in the autonomous robot and a conveyor of the clean room. A transfer automation server (400) performs wireless communication with the autonomous vehicles. An INDEPENDENT CLAIM is also included for a method for operating an automation wafer transfer system for use during semiconductor manufacturing process. Automation wafer transfer system for use during semiconductor manufacturing process. The system achieves complete automation of wafer transport by transferring an airtight container having wafer between factories through an autonomous vehicle between factories, reduces the transport waiting time by eliminating manual work outside logistics, and minimizes the safety hazards to workers. The drawing shows a schematic view of an automation wafer transfer system for use during semiconductor manufacturing process.(Drawing includes non-English language text).100Autonomous vehicle130Airtight container200Autonomous robot300Interface device400Transfer automation server | Please summarize the input |
Electronic device for supporting wireless mobile communication for vehicle and operation method of the sameProvided are an electronic device for supporting vehicle-to-everything (V2X) communication on which an autonomous driving vehicle technology, a cooperative-intelligent transport systems (C-ITS) technology, etc. are based and an operation method of the electric device. The electronic device mounted to a vehicle to support wireless mobile communication for the vehicle includes: a dedicated short range communication (DSRC) module configured to perform wireless communication by using DSRC technology; a cellular V2X (C-V2X) module configured to perform wireless communication by using C-V2X technology; an antenna; and a processor configured to control a switch to connect one of the DSRC module and the C-V2X module to the antenna.What is claimed is:
| 1. An electronic device mounted to a vehicle to support wireless mobile communication for the vehicle, the electronic device comprising:
a dedicated short range communication (DSRC) module configured to perform wireless communication by using DSRC technology;
a cellular vehicle-to-everything (C-V2X) module configured to perform wireless communication by using C-V2X technology;
an antenna;
a switch; and
a processor configured to control the switch to connect the DSRC module or the C-V2X module to the antenna,
wherein the antenna includes a pair of sub-antennas for diversity transmission and diversity reception, and
wherein the switch is configured to receive two output signals from the DSRC module for the pair of sub-antennas or two output signals from the C-V2X module for the pair of sub-antennas and output two signals to the pair of sub-antennas.
| 2. The electronic device of claim 1, further comprising a telematics control unit (TCU).
| 3. The electronic device of claim 1, wherein each of the DSRC module and the C-V2X module includes a V2X modem and a radio frequency (RF) transceiver.
| 4. The electronic device of claim 1, wherein the processor is further configured to:
select a module from the DSRC module and the C-V2X module based on location information of the vehicle; and
control the switch to connect the module selected from the DSRC module and the C-V2X module to the antenna.
| 5. The electronic device of claim 4, wherein the location information of the vehicle includes a global positioning system (GPS) signal for the vehicle,
wherein the module selected from the DSRC module and the C-V2X module is selected based on information about a V2X communication technology corresponding to a location of the vehicle and the location information of the vehicle, and
wherein the module selected from the DSRC module and the C-V2X module supports a V2X communication technology corresponding to a current location of the vehicle.
| 6. The electronic device of claim 1, wherein the processor is further configured to:
select a module from the DSRC module and the C-V2X module based on information about a base station that is performing cellular communication with the electronic device; and
control the switch to connect the module selected from the DSRC module and the C-V2X module to the antenna.
| 7. The electronic device of claim 6, wherein the module selected from the DSRC module and the C-V2X module is selected based on information about a V2X communication technology corresponding to the base station and the information about the base station, and
wherein the module selected from the DSRC module and the C-V2X module supports the V2X communication technology corresponding to the base station.
| 8. The electronic device of claim 1, wherein the processor is further configured to:
select a module from the DSRC module and the C-V2X module by periodically comparing a DSRC signal received via the DSRC module with a C-V2X signal received via the C-V2X module; and
control the switch to connect the module selected from the DSRC module and the C-V2X module to the antenna.
| 9. The electronic device of claim 8, wherein the module selected from the DSRC module and the C-V2X module is selected based on comparing packet error rate (PER), packet reception rate (PRR), latency, and/or strength of the DSRC signal and the C-V2X signal to each other.
| 10. The electronic device of claim 9, wherein the processor is further configured to:
control the switch to perform diversity communication by using one of the DSRC module and the C-V2X module and the pair of sub-antennas included in the antenna;
control the switch to receive the DSRC signal via a first sub-antenna of the pair of sub-antennas and the C-V2X signal via a second sub-antenna at preset time periods; and
determine whether to change V2X communication technology based on a result of the comparing the DSRC signal with the C-V2X signal.
| 11. The electronic device of claim 1, wherein the processor is further configured to:
obtain surrounding environment information and select a module from the DSRC module and the C-V2X module based on the obtained surrounding environment information; and
control the switch to connect the module selected from the DSRC module and the C-V2X module to the antenna.
| 12. The electronic device of claim 1, wherein the processor is further configured to:
obtain a captured image of the vehicle's surrounding environment as surrounding environment information; and
identify, in the captured image, an entity supporting vehicle-to-infrastructure (V2I) communication with the vehicle and select a module from the DSRC module and the C-V2X module as a module supporting a V2X communication technology corresponding to the identified entity.
| 13. An operation method of an electronic device mounted to a vehicle to support wireless mobile communication for the vehicle, the operation method comprising:
selecting a module from a dedicated short range communication (DSRC) module configured to perform wireless communication by using DSRC technology and a cellular vehicle-to-everything (C-V2X) module configured to perform wireless communication by using C-V2X technology;
controlling a switch to connect the selected module to an antenna; and
performing V2X communication via the selected module,
wherein the antenna includes a pair of sub-antennas for diversity transmission and diversity reception, and
wherein the method further comprises:
receiving two output signals from the DSRC module for the pair of sub-antennas or two output signals from the C-V2X module for the pair of sub-antennas; and
outputting two signals to the pair of sub-antennas.
| 14. The operation method of claim 13, wherein location information of the vehicle includes a global positioning system (GPS) signal for the vehicle,
wherein the selecting of the module further comprises:
obtaining the location information of the vehicle; and
selecting the module from the DSRC module and the C-V2X module based on information about a V2X communication technology corresponding to a location of the vehicle and the location information of the vehicle, and
where the module selected from the DSRC module and the C-V2X module supports a V2X communication technology corresponding to a current location of the vehicle.
| 15. The operation method of claim 13, wherein the selecting of the module further comprises:
obtaining information about a base station that is performing cellular communication with the electronic device; and
selecting the module from the DSRC module and the C-V2X module based on information about a V2X communication technology corresponding to the base station and the information about the base station,
wherein the module selected from the DSRC module and the C-V2X module supports the V2X communication technology corresponding to the base station.
| 16. The operation method of claim 13, wherein the module selected from the DSRC module and the C-V2X module is selected based on periodically comparing a DSRC signal received via the DSRC module with a C-V2X signal received via the C-V2X module.
| 17. The operation method of claim 13, wherein the selecting of the module further comprises:
obtaining surrounding environment information of the vehicle; and
selecting the module from the DSRC module and the C-V2X module based on the obtained surrounding environment information.
| 18. A non-transitory computer-readable recording medium having stored therein a program for performing an operation method of an electronic device mounted to a vehicle to support wireless mobile communication for the vehicle, the operation method comprising:
selecting a module from a dedicated short range communication (DSRC) module configured to perform wireless communication by using DSRC technology and a cellular vehicle-to-everything (C-V2X) module configured to perform wireless communication by using C-V2X technology;
controlling a switch to connect the selected module to an antenna; and
performing V2X communication via the selected module,
wherein the antenna includes a pair of sub-antennas for diversity transmission and diversity reception, and
wherein the operation method further comprises:
receiving two output signals from the DSRC module for the pair of sub-antennas or two output signals from the C-V2X module for the pair of sub-antennas; and
outputting two signals to the pair of sub-antennas. | The electronic device comprises a dedicated short range communication (DSRC) module configured to perform wireless communication by using DSRC technology. A cellular vehicle-to-everything (C-V2X) module is configured to perform wireless communication by using C-V2X technology. A processor is provided to control a switch to connect the DSRC module or the C-V2X module to the antenna. The DSRC module and the C-V2X module include a V2X modem and a radio frequency (RF) transceiver. The antenna includes a pair of sub-antennas for diversity transmission and diversity reception. A module is selected from the DSRC module and the C-V2X module based on location information of the vehicle (30,100). INDEPENDENT CLAIMS are included for the following:a method for an electronic device mounted for supporting wireless mobile communication for vehicle through wired or wireless networks; anda computer-readable recording medium having stored instructions for implementing the method for supporting wireless mobile communication for vehicle through wired or wireless networks. Electronic device for supporting wireless mobile communication for vehicle, such as car through wired or wireless networks, such as third generation , fourth generation and fifth generation networks. Electronic device can select the V2X module, which achieves better performance by comparing packet error rate (PER), packet reception rate (PRR), latency, or the strength of the DSRC signal and the C-V2X signal. Multiple weight values can be modified to reduce or minimize a loss or cost value obtained by the AI model during a training process. The drawing shows a perspective view of electronic device for supporting wireless mobile communication for vehicle. 10Network20Infrastructure30,100Vehicle40Pedestrian | Please summarize the input |
Method and apparatus for operating autonomous driving controller of vehicleProvided is a method and apparatus for operating an autonomous driving controller, the method including generating route information for the vehicle based on a rule, transitioning from an autonomous driving mode to an autonomous driving disable mode, in response to the driving route information not being generated for an amount of time greater than or equal to a threshold, tracking at least one neighboring vehicle based on data sensed by a sensor, and generating temporary driving route information based on a movement of the at least one neighboring vehicle.What is claimed is:
| 1. A method of driving a vehicle, the method comprising:
generating driving route information for the vehicle based on a rule; and
in response to the driving route information not being generated based on the rule for an amount of time greater than or equal to a threshold:
disabling an autonomous driving mode using the driving route information;
tracking at least one neighboring vehicle based on data sensed by a sensor;
generating temporary driving route information based on a movement of the at least one neighboring vehicle; and
driving the vehicle based on the temporary driving route information.
| 2. The method of claim 1, wherein the generating of the driving route information comprises:
recognizing a surrounding environment of the vehicle based on the data sensed by the sensor; and
generating the driving route information based on the recognized surrounding environment and the rule.
| 3. The method of claim 1, wherein the tracking of the movement of the at least one neighboring vehicle comprises:
periodically determining a location of the at least one neighboring vehicle; and
tracking the movement of the at least one neighboring vehicle based on a change of the location of the at least one neighboring vehicle.
| 4. The method of claim 1, wherein the generating of the temporary driving route information comprises:
determining a difference in movement between a first neighboring vehicle and a second neighboring vehicle, in response to movements of two neighboring vehicles being tracked; and
generating the temporary driving route information based on the difference in movement between the first neighboring vehicle and the second neighboring vehicle.
| 5. The method of claim 4, wherein the determining of the difference comprises determining the difference by comparing a first surrounding environment in which the first neighboring vehicle moves to a second surrounding environment in which the second neighboring vehicle does not move, and
the generating of the temporary driving route information comprises generating the temporary driving route information based on the first surrounding environment including the difference.
| 6. The method of claim 1, wherein the generating of the temporary driving route information comprises generating the temporary driving route information to move the vehicle based on a change of a location of the at least one neighboring vehicle.
| 7. The method of claim 1, wherein the at least one neighboring vehicle comprises a vehicle moving in a direction identical to a direction of the vehicle.
| 8. The method of claim 1, wherein the at least one sensor comprises any one or any combination of a camera, a lidar, and a radar.
| 9. The method of claim 1, wherein the generating of the temporary driving route information comprises generating the temporary driving route information in response 25 to an absence of a movement of a vehicle in a direction different from a direction of the vehicle.
| 10. The method of claim 1, further comprising, in response to the driving route information not being generated based on the rule for the amount of time greater than or equal to the threshold, updating log information stored in a memory.
| 11. The method of claim 10, further comprising:
searching the memory for the log information corresponding to a current circumstance, in response to a route generating mode being transitioned to an autonomous driving disable mode; and
generating the temporary driving route information based on the log information corresponding to the current circumstance.
| 12. The method of claim 11, wherein the current circumstance comprises any one or any combination of a type of an obstacle, a size of the obstacle, weather conditions, type of a road, road conditions, a size of a lane, and a number of lanes, and
the searching comprises searching the memory for the log information having greatest similarity with the current circumstance.
| 13. The method of claim 1, further comprising:
receiving the temporary driving route information through wireless communication or vehicle to vehicle (V2V) communication with the at least one neighboring vehicle, in
response to a route generating mode being transitioned to an autonomous driving disable mode.
| 14. A non-transitory computer-readable medium storing instructions that, when executed by a processor, causes the processor to perform the method of claim 1.
| 15. An autonomous driving controller comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to generate route information for a vehicle based on a rule, and in response to the driving route information not being generated based on the rule for an amount of time greater than or equal to a threshold, configured to transition from an autonomous driving mode to an autonomous driving disable mode, track at least one neighboring vehicle based on data sensed by a sensor, generate temporary driving route information based on a movement of the at least one neighboring vehicle and control the vehicle based on the temporary driving route information.
| 16. A method of controlling autonomous driving controller, the method comprising:
generating route information for a vehicle based on a rule;
in response to the driving route information not being generated based on the rule for an amount of time greater than or equal to a preset time:
transitioning from an autonomous driving mode to an autonomous driving disable mode;
searching a memory for log information having greatest similarity with a current circumstance, in response to the transitioning to the autonomous driving disable mode;
generating temporary driving route information based on the log information; and
controlling the vehicle based on the temporary driving route information.
| 17. A method of driving a vehicle, the method comprising:
generating route information for the vehicle being driven in an autonomous driving mode; and
in response to the route information violating a rule for a time period greater than a threshold:
disabling the autonomous driving mode;
tracking a change of a location of at least one neighboring vehicle based on data sensed by a sensor;
generating temporary driving route information based on the change of the location of the at least one neighboring vehicle; and
driving the vehicle based on the temporary driving route information.
| 18. The method of claim 17, wherein the generating of the temporary driving route information comprises generating a temporary driving route based on the change of the location of the at least one neighboring vehicle and a surrounding environment in which the at least one neighboring vehicle moves.
| 19. The method of claim 17, further comprising:
updating log data, in a memory, in response to the generating of the temporary driving route information.
| 20. The method of claim 19, wherein the generating of the temporary driving route information comprises generating a temporary driving route based on the change of the location of the at least one neighboring vehicle and log entry, stored in the memory, corresponding to a surrounding environment of the vehicle. | The method involves generating (710) the route information for the vehicle based on a rule. A neighboring vehicle is tracked based on the data sensed by a sensor. The temporary driving route information is generated (760) based on a movement of the neighboring vehicle. A surrounding environment of the vehicle is recognized based on the data sensed by the sensor. The sensor is combination of a camera, light detection and ranging and a radar. A memory is searched for the log information corresponding to a current circumstance. INDEPENDENT CLAIMS are included for the following:a non-transitory computer-readable medium for storing instructions; andan autonomous driving controller has a memory. Method for driving a vehicle. The method involves generating the route information for the vehicle based on a rule, where neighboring vehicle is tracked based on the data sensed by a sensor and the temporary driving route information is generated based on a movement of the neighboring vehicle, and thus enables to recognize surrounding objects and generates a driving route that meets traffic regulations and avoids contact with the surrounding objects. The drawing shows a flowchart of a method for operating an autonomous driving controller. 710Generating the route information for the vehicle720Verifying whether the driving route information is generated based on the rule730Transitioning a route generating mode from an autonomous driving mode to an autonomous driving disable mode740Tracking the movement of a neighboring vehicle based on the data sensed by the sensor760Generating the temporary driving route information | Please summarize the input |
Non-invasive handling of sleep apnea, snoring and emergency situationsA monitoring non-invasive device for handling of sleep apnea, snoring and emergency situations operates for breathing assistance by means of transdermal stimulation of muscle groups including the pectoralis majoris, the serratus anterior, and the abdominal muscles. A wrist mounted version may alarm drivers or others requiring focus or concentration when they fall asleep and may alert a medical center. The invention may have a pulse oximeter on a person's wrist/finger to monitor their breathing while asleep, and in the event of a serious snoring or sleep apnea episode, activate the breathing assistance pulses.What is claimed is:
| 1. A sleep apnea, snoring, emergency situations and breath assistance device configured for use by a person having a body, skin, a mouth, airways, first, second, third, and fourth pairs of abdominal muscles, and four chest muscles including first and second pectoral muscles and first and second serratus anterior muscles, the sleep apnea, snoring, emergency situations and breath assistance device comprising:
a control module having operative electrical connections to a plurality of dermal electrodes configured to be attached to such skin of such person, whereby the control module is in communication with the dermal electrodes, the control module configured so as to be worn on such person's body;
a first one of the plurality of dermal electrodes configured to be disposed on such skin of such person at one such chest muscle;
a second one of the plurality of dermal electrodes configured to be disposed on such skin of such person at one such abdominal muscle;
each of the dermal electrodes configured to deliver a plurality of pulse trains to one such respective muscle;
the control module having a stimulation module operative to send a first pulse train to such chest muscle and a second pulse train to such abdominal muscle;
the first pulse train operative to stimulate such chest muscle so as to cause a first contraction of such chest muscle;
the second pulse train operative to stimulate such abdominal muscle so as to cause a second contraction of such abdominal muscle;
whereby at least one breath is stimulated.
| 2. The sleep apnea, snoring, emergency situations and breath assistance device of claim 1, configured for use with agarment worn on such body by such person, wherein:
the control module, the dermal electrodes and the operative electrical connections are configured so as to be worn on such body concealed within such garment.
| 3. The sleep apnea, snoring, emergency situations and breath assistance device of claim 2, further comprising:
a third one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a second such abdominal muscle;
a fourth one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a third such abdominal muscle;
a fifth one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a fourth such abdominal muscle;
the control module further operative to send the second pulse train to such second, third and fourth abdominal muscles.
| 4. The sleep apnea, snoring, emergency situations and breath assistance device of claim 3, further comprising:
a sixth one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a second such chest muscle;
a seventh one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a third such chest muscle;
an eighth one of the plurality of dermal electrodes configured to be disposed on such skin of such person at a fourth such chest muscle;
the control module further operative to send the first pulse train to such second, third and fourth chest muscles.
| 5. The sleep apnea, snoring, emergency situations and breath assistance device of claim 4, the pulse train further comprising:
a group of pulses consisting of a plurality of individual pulses increasing in amplitude with time, the group of pulses having a duration of 500 ms to 900 ms;
a second time out period of 2 to 3 seconds during which no pulses are sent;
repetitions of the group of pulses and the second time out period for a breath assist time period defined to last either until an autonomic breath occurs or for a period of time of no more than 3 seconds.
| 6. The sleep apnea, snoring, emergency situations and breath assistance device of claim 5, further comprising:
at least one pulse oximeter configured to be attached to such user;
the pulse oximeter sensor in operative communication with the control module;
the control module further comprising an analysis module operative to receive a data from the pulse oximeter sensor and analyze the data to determine if such person is exhibiting an autonomic breath and if such person is not exhibiting an autonomic breath for a period of 3 seconds, the control module further operative to send the pulse trains.
| 7. The sleep apnea, snoring, emergency situations and breath assistance device of claim 6, wherein the pulse oximeter sensor is further operative to alert such person by means of a signal when it sends such pulse trains.
| 8. The sleep apnea, snoring, emergency situations and breath assistance device of claim 5, further comprising:
at least one breath sensor in operative communication with the control module, the breath sensor configured to be disposed on such skin of such person;
the control module further comprising an analysis module operative to receive a data from the breath sensor and analyze the data to determine if such person is exhibiting an autonomic breath and if such person is not exhibiting an autonomic breath, the control module further operative to send the pulse trains.
| 9. The sleep apnea, snoring, emergency situations and breath assistance device of claim 5, further comprising:
at least one blood oxygen level sensor, the blood oxygen level sensor in operative communication with the control module, the blood oxygen level sensor configured to be disposed on such skin of such person;
the control module further comprising an analysis module operative to receive a data from the blood oxygen level sensor and analyze the data to determine if such person is exhibiting an oxygen level indicative of a normal breathing pattern and if such person is not, the control module further operative to send the pulse trains.
| 10. The sleep apnea, snoring, emergency situations and breath assistance device of claim 9, further comprising:
an RF communication module;
the control module having a non-volatile memory and a central processor unit, the analysis module stored in the non-volatile memory, the control module having a start button operative to activate the sleep apnea, snoring, emergency situations and breath assistance device to begin an operating cycle, using a first set of preset operating parameters also stored in the non-volatile memory;
a mobile device having an operative RF connection to the RF communication module of the control module and further having a touch screen operative to display a set of data collected by the device and enable control of the secretion clearance and cough assistance device;
the start button further operative to establish the operative RF connection to the mobile device;
the mobile device having a module operative to provide wireless control of the operation of the control module;
the mobile device operative to collect data, provide for wireless setup and wireless maintenance of the breath assistance device.
| 11. The sleep apnea, snoring, emergency situations and breath assistance device of claim 10, wherein the mobile device is operative to provide control of the control module by one mode selected from the group consisting of: manual control input to the mobile device and the control module, manual control input to the mobile device and from the mobile device to the control module, adaptive heuristic control by an artificial intelligence module loaded in the mobile device and the control module, adaptive heuristic control by an artificial intelligence module loaded in the mobile device and from the mobile device to the control module, remote control from a remote location via communication with the mobile device and from the mobile device to the control module, and combinations thereof.
| 12. The sleep apnea, snoring, emergency situations and breath assistance device of claim 10, wherein the control module is further operative to alert such person by means of a signal from such mobile device when it sends such pulse trains.
| 13. The sleep apnea, snoring, emergency situations and breath assistance device of claim 10, configured for use with a vehicle being driven by such person, such vehicle having autonomous driving capability, wherein the control module further comprises:
a communication protocol allowing the control module to control such vehicle;
the control module operative to assume control of such vehicle when it sends such pulse trains.
| 14. The sleep apnea, snoring, emergency situations and breath assistance device of claim 13, wherein the communication protocol further comprises one member selected from the group consisting of: V2X, Bluetooth, WiFi, and combinations thereof.
| 15. The sleep apnea, snoring, emergency situations and breath assistance device of claim 10, configured for use by such person in a job requiring attention and focus.
| 16. The sleep apnea, snoring, emergency situations and breath assistance device of claim 5, further comprising:
at least one blood pressure sensor, the blood pressure sensor in operative communication with the control module, the blood pressure sensor configured to be disposed on such skin of such person;
the control module further comprising an analysis module operative to receive a data from the blood pressure sensor and analyze the data to determine if such person is exhibiting normal autonomic breathing and if sch person is not exhibiting normal autonomic breathing, the control module further operative to send the pulse trains.
| 17. The sleep apnea, snoring, emergency situations and breath assistance device of claim 5, further comprising:
at least one sensor of at least one heart rate sensor, the heart rate sensor in operative communication with the control module, the heart rate sensor configured to be disposed on such skin of such person;
the control module further comprising an analysis module operative to receive a data from the heart rate sensor and analyze the data to determine if such person is exhibiting normal autonomic breathing and if such person is not exhibiting normal autonomic breathing, the control module further operative to send the pulse trains. | The device has a main portion having a shape dimensioned and configured to be worn on such arm. A control module (1514) includes a CPU within the device main portion, and has operative electrical connections to a first electrode. The first electrode is in contact with arm. A control module comprises an analysis module operative to receive data from the sensor and analyze the data to determine if such a person (1500) exhibits autonomic breath and if such person is not exhibiting such autonomic breath. The control module is operative to carry out task related to sending of a first pulse train to the first electrode, making an alert noise, alert vibration, and communicating with a vehicle and a first preferred remote terminal through a RF communication module (1516). An INDEPENDENT CLAIM is included for a method of breath assistance for use by a person. Sleep apnea, snoring, emergency situations and breath assistance device for use by person in autonomic breath. The control module is operative to alert person by audible/vibration/transmitted signal when it sends such pulse trains. The device is efficient to have the stimulation happen concurrently with the breathing or perhaps even after, or there are multiple rounds of stimulation for each breath, and so on. The device analyzes the monitored data, and examines the stimulation history, and then actually optimizes the parameters of the stimulation, thus providing a unique and optimized stimulation from moment to moment or from breath to breath. The communication protocol is selected from group consisting of Wireless Fidelity (Wi-Fi) , Bluetooth standards. The drawing shows a front view of the sleep apnea, snoring, emergency situations and breath assistance device. 1500Person1514Control module1516RF communication module1518Control device1520Sensor | Please summarize the input |
SYSTEMS AND METHODS FOR IMPROVED OPERATION OF A WORKING VEHICLEVarious apparatus and procedures for improved operation of a working vehicle are provided. One embodiment provides for vehicle-to-vehicle communications using cellular modems to provide information from one vehicle to another vehicle that has lost internet connectivity. Another embodiment provides a method for improving safety of a work area where an autonomous or remotely controlled vehicle is operating by scanning for unknown Bluetooth modules in the vicinity of a working vehicle. Another embodiment provides for intercepting and modifying signals from vehicle controls and passing the modified signals to a control unit of the vehicle.|1. A system for performing a work operation in a work area comprising:
a plurality of vehicles wherein each vehicle is equipped with a GNSS unit and a modem, and wherein the modem of each vehicle is configured to receive location corrections from an RTK network;
a processor connected to each vehicle, wherein each processor is configured to receive location information from its respective GNSS unit and location corrections from its respective modem;
wherein each processor is further configured to run mission plan software for controlling operation of its respective vehicle; and
wherein each processor is further configured to detect a loss of connection to the RTK network, connect to a local wireless network, and query any other vehicle of the plurality of vehicles for location corrections.
| 2. The system of claim 1 wherein real-time collision avoidance information is communicated in addition to the location corrections.
| 3. The system of claim 1 wherein dynamic job optimization information is communicated in addition to the location corrections.
| 4. The system of claim 1 wherein the plurality of vehicles comprise autonomous vehicles.
| 5. A system for improving safety in a work area comprising:
one or more vehicles wherein each vehicle is equipped with a Bluetooth module configured to send and receive signals from other Bluetooth modules;
a processor connected to each vehicle, wherein each processor is configured to communicate with its respective Bluetooth module; and
wherein each processor is further configured to shut down its respective vehicle if a signal transmitted by an unknown Bluetooth module is detected by the vehicle's Bluetooth module.
| 6. The method of claim 5 wherein the one or more vehicles is autonomous.
| 7. The method of claim 5 wherein the one or more vehicles is remotely controlled.
| 8. A method for improving safety in a work area comprising:
operating one or more vehicles in the work area, wherein each vehicle is equipped with a vehicle Bluetooth module configured to send and receive signals from other Bluetooth modules, and each vehicle is equipped with a processor configured to communicate with its respective vehicle Bluetooth module;
scanning for Bluetooth signals transmitted by one or more other Bluetooth modules; and
shutting down the one or more vehicles if its associated vehicle Bluetooth module receives a signal transmitted by an unknown Bluetooth module.
| 9. The method of claim 5 wherein the plurality of vehicles is autonomous.
| 10. The method of claim 5 wherein the plurality of vehicles is remotely controlled.
| 11. A method for autonomously controlling a vehicle comprising:
providing an interceptor configured to intercept one or more messages communicated by one or more armrest controls of the vehicle to an engine control unit of the vehicle;
inserting autonomous control instructions into the one or more intercepted messages to create a modified message; and
communicating the modified message to the engine control unit of the vehicle.
| 12. The method of claim 11 wherein the one or messages communicated by one or more armrest controls of the vehicle to the engine control unit of the vehicle and the modified message are communicated on a CAN bus of the vehicle. | The system has multiple vehicles where each vehicle (10) is connected with a global navigation satellite system (GNSS) unit (40) and a modem (710), where the modem of each vehicle is configured to receive location corrections from an RTK network. A processor is connected to each vehicle, where each processor is configured to receive location information from the respective GNSS unit and location corrections from its respective modem. Each processor is configured to run mission plan software for controlling operation of its respective vehicle. Each processor is configured to detect a loss of connection to the RTK network, connect to a local wireless network, and query any other vehicle of the plurality of vehicles for location corrections. INDEPENDENT CLAIMS are included for: (1) a system for improving safety in a work area; (2) the method for improving safety in a work area; (3) a method for autonomously controlling a vehicle. System for performing work operation in work area performed using a manned vehicle or by an autonomous or remotely controlled vehicle such as agricultural vehicle, a mower. The operator of the vehicle makes judgment calls about selecting a safe evacuation location and steering the vehicle quickly toward the evacuation location while avoiding injury or damage to the vehicle or to objects or people in the vehicle path. The speed of vehicle is reduced to avoid damage to vehicle, when readings captured by GNSS unit indicate that the vehicle is approaching or within the slow zone. The drawing shows a schematic view of system for performing work operation in work area .performed using a manned vehicle.10Vehicle 20Control implement 30Computer 35Microprocessor 40GNSS unit 700Base station 710Modem | Please summarize the input |
Mobile payment system for traffic prioritization in self-driving vehiclesA self-driving or autonomous vehicle transmits a vehicle-to-vehicle offer message from a user of a vehicle-connected mobile communication device riding in the self-driving vehicle to a second user of a second mobile communication device riding in a second vehicle to pay for a traffic prioritization relative to the second vehicle. The first mobile communication device receives a reply message and sends a payment to the second mobile communication device or an account associated with the second mobile communication device to obtain the traffic prioritization relative to the other vehicle. For example, the traffic prioritization may enable one vehicle to pass the other vehicle, to take precedence at an intersection or to be given priority to take a parking place or any other traffic-related advantage.The invention claimed is:
| 1. A non-transitory computer-readable medium storing computer-readable instructions in code which when executed by a processor of a mobile communication device cause the mobile communication device to:
communicatively connect to a mobile communication interface of a self-driving vehicle in which the mobile communication device is located;
receive user input defining an offer to pay for a traffic prioritization that prioritizes the self-driving vehicle relative to a second vehicle;
automatically generate an offer message in response to the user input;
automatically transmit the offer message to the mobile communication interface of the self-driving vehicle for communicating the offer message to a second mobile communication device in the second vehicle;
automatically receive a reply message from the second mobile communication device;
automatically determine if the reply message constitutes an acceptance or rejection of the offer; and
in response to determining that the reply message indicates the acceptance of the offer, send a payment to the second mobile communication device or to an account associated with the second mobile communication device to pay for the traffic prioritization.
| 2. The non-transitory computer-readable medium of claim 1 further comprising code that causes the mobile communication device to receive a confirmation message to confirm receipt of the payment.
| 3. The non-transitory computer-readable medium of claim 2 further comprising code that causes the mobile communication device to receive an acknowledgement message that the second vehicle will maneuver as soon as traffic regulations and traffic conditions permit to grant priority to the self-driving vehicle.
| 4. The non-transitory computer-readable medium of claim 1 further comprising code that causes the mobile communication device to output an alert that an estimated time of arrival at a destination will be later than originally predicted and presenting a user interface element to pay to prioritize the self-driving vehicle in traffic.
| 5. The non-transitory computer-readable medium of claim 1 further comprising code that causes the mobile communication device to receive a third-party request to expedite travel, the third-party request including a third-party payment to prioritize the self-driving vehicle in traffic, wherein the code is configured to automatically generate and transmit a third-party offer message using the third-party payment to the second mobile communication device.
| 6. A non-transitory computer-readable medium storing computer-readable instructions in code which when executed by a processor of a mobile communication device cause the mobile communication device to:
display on a user interface of the mobile communication device a fee-for-transport interface of a fee-for-transport application executing on the mobile communication device to enable a user to summon a self-driving vehicle to transport the user from a starting point to a destination for a fee;
receive user input from the user to define the destination, wherein the starting point is either a current location of the mobile communication device or a user-specified pickup location;
display pricing options based on a plurality of different levels of traffic prioritization for transport to the destination from the starting point to the destination;
receive a user-selected traffic prioritization; and
communicate a pickup request to the self-driving vehicle, the pickup request including the user-selected traffic prioritization to enable the self-driving vehicle to automatically offer one or more payments to one or more other vehicles to obtain the user-selected traffic prioritization along the route to the destination.
| 7. The non-transitory computer-readable medium of claim 6 wherein the code causes the mobile communication device to display a trip report upon arrival at the destination that indicates that the self-driving vehicle has determined that a portion of the fee allocated for prioritization payments has been unused, and the portion of the fee that was unused has been refunded to an account associated with a user of the mobile communication device.
| 8. The non-transitory computer-readable medium of claim 6 comprising code that causes the mobile communication device to display on the user interface of the mobile communication device an amount payable to arrive at the destination at a user-specified time, to present a user interface element to pay the amount, and to communicate this amount and the user-specified time to the self-driving vehicle.
| 9. The non-transitory computer-readable medium of claim 6 comprising code that causes the mobile communication device to receive real-time traffic data, to detect a traffic jam based on the real-time traffic data by determining that the self-driving vehicle is moving below a speed limit, and to send a plurality of offer messages to a plurality of vehicles to pay for prioritization.
| 10. The non-transitory computer-readable medium of claim 9 wherein the offer messages are conditional offers that are conditional on acceptance by all of the plurality of vehicles.
| 11. The non-transitory computer-readable medium of claim 6 wherein the code to display the pricing options includes code to display travel times for the pricing options.
| 12. The non-transitory computer-readable medium of claim 6 comprising code to cause the mobile communication device to use an event stored in a calendar application on the mobile communication device to determine the travel time to the event, and then automatically recommend a prioritization level to arrive at the event on time.
| 13. The non-transitory computer-readable medium of claim 6 wherein the pricing options are based on historical prioritization data that include the probabilities of offers being accepted at various price points.
| 14. The non-transitory computer-readable medium of claim 6 comprising code that causes the mobile communication device to receive a third-party request to expedite travel, the third-party request including a third-party payment to prioritize the self-driving vehicle in traffic.
| 15. A non-transitory computer-readable medium storing computer-readable instructions in code which when executed by a processor of a mobile communication device cause the mobile communication device to:
generate an emergency request for an emergency, the emergency request requesting that a self-driving vehicle be prioritized in traffic due the emergency;
transmit the emergency request to a governmental authority emergency server to request emergency prioritization; and
receive an emergency prioritization authorization from the governmental authority emergency server, the emergency prioritization authorization comprising a first cryptographic token to be broadcast by the self-driving vehicle to other vehicles to obtain priority in traffic and a second cryptographic token that is recognizable by law enforcement entities permitting the self-driving vehicle to exceed a speed limit due to the emergency.
| 16. The non-transitory computer-readable medium of claim 15 wherein the emergency request is generated in response to detecting a 911 call being made by the mobile communication device.
| 17. The non-transitory computer-readable medium of claim 15 wherein the emergency request is generated in response to a biometric sensor detecting the emergency, the biometric sensor being in the mobile communication device or in communication with the mobile communication device.
| 18. The non-transitory computer-readable medium of claim 15 comprising code that causes the mobile communication device to:
determine an emergency destination to replace a destination originally specified by the user; and
re-route the self-driving vehicle to the emergency destination.
| 19. The non-transitory computer-readable medium of claim 18 comprising code that causes the mobile communication device to:
constrain the cryptographic token to be valid only for a new route to the emergency destination.
| 20. The non-transitory computer-readable medium of claim 16 comprising code that causes the mobile communication device to:
determine an emergency destination to replace a destination originally specified by the user; and
re-route the self-driving vehicle to the emergency destination. | The medium has set of instructions for communicatively connecting to a first mobile communication interface (1000) of a self-driving vehicle (10) in which the first mobile communication device is located. User input defining offer to pay for traffic prioritization that prioritizes the self-driving vehicle relative to a primary vehicle is received. Offer message is automatically generated in response to the user input. The offer message is automatically transmitted to the first mobile communication interface of the self-driving vehicle for communicating the offer message to a second mobile communication device (1100) in the primary vehicle. Reply message is automatically received from the second mobile communication device. Judgment is made to check whether the reply message constitutes acceptance or rejection of the offer. Payment is transmitted to the second mobile communication device or to an account associated with the second mobile communication device to pay for the traffic prioritization in response to determining that the reply message indicates the acceptance of the offer. Non-transitory computer readable storage medium for realizing traffic prioritization in a self-driving vehicle i.e. car (from drawings) by a mobile payment system through a mobile communication device e.g. smartphone, cell phone, tablet, smartwatch, wearable smart device and laptop. The medium enables mutually sensing self-driving vehicles in a preset area of a road by utilizing various sensors for collision avoidance and communication through vehicle-to-vehicle messaging protocols. The drawing shows a schematic diagram of a mobile payment system.10Self-driving vehicle 11Vehicle-to-vehicle messages 1000First mobile communication interface 1100Second mobile communication interface 1101First user 1101aSecond user 1105Processor 1105aCPU 1110Mobile device memory 1115Mobile device display screen 1120Mobile device global navigation satellite system chip 1130Cellular transceiver 1140Mobile device data interface 1150User interface element 1200First vehicle-to-vehicle data transceiver 1200aSecond vehicle-to-vehicle data transceiver | Please summarize the input |
Vehicle-to-vehicle payment system for traffic prioritization in self-driving vehiclesA self-driving or autonomous vehicle has a traffic-prioritization processor to send or receive a payment to or from a central server to obtain a traffic prioritization for a route or to accept a traffic de-prioritization for the route. The central server receives and distributes payments to other vehicles traveling the route. The vehicle communicates with the central server to receive a plurality of levels of prioritization which range from a highest prioritization to a lowest prioritization, and the costs or payouts associated with each of the levels.The invention claimed is:
| 1. A self-driving vehicle comprising:
a vehicle chassis;
a motor supported by the chassis for providing propulsive power for the vehicle;
a braking system;
a steering system;
a plurality of sensors;
a self-driving processor configured to receive signals from the sensors and to generate steering, acceleration and braking control signals for controlling the steering system, the motor and the braking system of the vehicle;
a Global Navigation Satellite System (GNSS) receiver for receiving satellite signals and for determining a current location of the self-driving vehicle;
a radiofrequency data transceiver; and
a traffic-prioritization processor configured to cooperate with the radiofrequency data transceiver to:
receive from a central server a price to obtain a traffic prioritization for a route or to accept a traffic de-prioritization for the route, wherein the central server determines the price based on offers and requests to be prioritized or deprioritized from other vehicles traveling the route and wherein the central server receives payments from prioritized vehicles traveling the route and distributes payments to de-prioritized vehicles traveling the route; and
send or receive a payment to or from the central server to obtain the traffic prioritization for the route or to accept the traffic de-prioritization for the route.
| 2. The self-driving vehicle of claim 1 wherein the traffic-prioritization processor is configured to cooperate with the radiofrequency data transceiver to receive, from the central server a plurality of levels of prioritization which range from a highest prioritization to a lowest prioritization, and the costs or payouts associated with each of the levels.
| 3. The self-driving vehicle of claim 2 wherein the traffic-prioritization processor is configured to cooperate with the radiofrequency data transceiver to receive, from the central server, travel times for the levels of prioritization.
| 4. The self-driving vehicle of claim 3 comprising a user interface to display the costs or payouts for the levels of prioritization and the travel times for each of the levels of prioritization to enable a user to select the level of prioritization for the route.
| 5. The self-driving vehicle of claim 1 wherein the user interface provides an alert indicating that an estimated time of arrival at a destination will be later than originally predicted and providing a user interface element to enable a user to pay to expedite travel to the destination.
| 6. The self-driving vehicle of claim 4 wherein the user interface displays the cost to pay to obtain the traffic prioritization to the destination.
| 7. A self-driving vehicle comprising:
a vehicle chassis;
a motor supported by the chassis for providing propulsive power for the vehicle;
a braking system;
a steering system;
a plurality of sensors;
a self-driving processor configured to receive signals from the sensors and to generate steering, acceleration and braking control signals for controlling the steering system, the motor and the braking system of the vehicle;
a Global Navigation Satellite System (GNSS) receiver for receiving satellite signals and for determining a current location of the self-driving vehicle;
a radiofrequency data transceiver; and
a traffic-prioritization processor that cooperates with the radiofrequency data transceiver to:
receive, from a central server, pricing for different levels of traffic prioritization for a route, the pricing including a cost to obtain a higher traffic prioritization for the route and a payout to accept a lower traffic prioritization for the route.
| 8. The self-driving vehicle of claim 7 wherein the traffic-prioritization processor is configured to cooperate with the radiofrequency data transceiver to send to the central server a payment equal to the cost of obtaining the higher traffic prioritization for the route.
| 9. The self-driving vehicle of claim 7 wherein the traffic-prioritization processor is configured to cooperate with the radiofrequency data transceiver to receive from the central server a payment equal to the payout for accepting the lower traffic prioritization for the route.
| 10. The self-driving vehicle of claim 7 further comprising a user interface presenting costs and payouts for three or more different levels of traffic prioritization.
| 11. The self-driving vehicle of claim 10 wherein the user interface also presents costs and payouts based on times of day.
| 12. The self-driving vehicle of claim 10 wherein the user interface also presents costs and payouts based on segments of the route.
| 13. The self-driving vehicle of claim 10 wherein the user interface also presents travel times for the different levels of traffic prioritization.
| 14. An autonomous vehicle comprising:
a self-driving processor configured to receive signals from sensors to generate steering, acceleration and braking control signals for controlling a steering system, a motor and a braking system of the vehicle;
a Global Navigation Satellite System (GNSS) receiver for receiving satellite signals and for determining a current location of the vehicle;
a radiofrequency data transceiver; and
a traffic-prioritization processor cooperating with the radiofrequency data transceiver to:
communicate with a central server to receive pricing for a traffic prioritization or de-prioritization for a route; and
send or receive a payment to or from the central server for the traffic prioritization or de-prioritization for the route.
| 15. The autonomous vehicle of claim 14 wherein the pricing includes costs and payouts for different segments of the route.
| 16. The autonomous vehicle of claim 15 wherein the costs and payouts for the different segments depend on a time of day.
| 17. The autonomous vehicle of claim 14 comprising a user interface to present the costs and payouts to enable selection of a level of prioritization.
| 18. The autonomous vehicle of claim 17 wherein the user interface indicates whether the costs and payouts are above normal market prices for that particular time and place.
| 19. The autonomous vehicle of claim 14 wherein the pricing includes a bid and an ask for each segment of the route and for each level of prioritization, the bid defining a price being offered for the prioritization and the ask defining a price that is being asked to accept the prioritization.
| 20. The autonomous vehicle of claim 14 wherein the self-driving processor and the traffic-prioritization processor are integrated in a vehicle computing device. | The vehicle (10) has a vehicle chassis (12) for supporting a motor for providing propulsive power for the vehicle. A self-driving processor (100) receives signals from sensors and for generating steering, acceleration and braking control signals. A Global Navigation Satellite System (GNSS) receiver (260) receives satellite signals and determines a current location of the self-driving vehicle. A traffic-prioritization processor (200) cooperates with a radio frequency data transceiver (220) for sending or receiving a payment to or from a central server to obtain traffic prioritization for a route or to accept traffic de-prioritization for the route. Autonomous or self-driving vehicles such as car, van, minivan, sports utility vehicle (SUV), crossover-type vehicle, bus, minibus, truck, tractor-trailer, semi-trailer, construction vehicle, work vehicle, tracked vehicle, semi-tracked vehicle, offroad vehicle, electric cart and a dune buggy for utilizing sensors such as RADAR, LIDAR and/or cameras to provide signals to a processor or controller that generates and outputs steering, acceleration and braking signals to the vehicle. The vehicle allows self-driving vehicles in a given area of a road to mutually sense presence of each other using various sensors for collision avoidance through vehicle-to-vehicle messaging protocols. The vehicle can automatically perform an adjustment to own routing on the benefit of the prioritization, e.g., to pass the second vehicle, upon transfer of the payment. The drawing shows a side view of an autonomous or self-driving vehicle.10Vehicle 12Vehicle chassis 100Self-driving processor 200Traffic-prioritization processor 220Radio frequency data transceiver 260GNSS receiver | Please summarize the input |
Vehicle-to-vehicle payment system for traffic prioritization in self-driving vehiclesA self-driving or autonomous vehicle comprises a processor to transmit an offer message to another vehicle and to receive a reply message from the other vehicle, and to transfer a payment to the other vehicle to obtain a traffic prioritization relative to the other vehicle. For example, the traffic prioritization may enable one vehicle to pass the other vehicle, to take precedence at an intersection or to be given priority to take a parking place or any other traffic-related advantage.The invention claimed is:
| 1. A self-driving vehicle comprising:
a vehicle chassis;
a motor supported by the chassis for providing propulsive power for the vehicle;
a braking system;
a steering system;
a plurality of sensors;
a processor configured to receive signals from the sensors and to generate steering, acceleration and braking control signals for controlling the steering system, the motor and the braking system of the vehicle;
a Global Navigation Satellite System (GNSS) receiver for receiving satellite signals and for determining a current location of the self-driving vehicle;
a radiofrequency data transceiver; and
wherein the processor is configured to:
transmit an offer message to a second vehicle;
receive a reply message from the second vehicle; and
transfer a payment to the second vehicle to obtain a traffic prioritization relative to the second vehicle.
| 2. The self-driving vehicle of claim 1 wherein the processor is configured to receive a counteroffer from the second vehicle and is configured to accept or reject the counteroffer.
| 3. The self-driving vehicle of claim 1 wherein the processor cooperates with the radiofrequency data transceiver to communicate with a first payment server to transfer payment to a second payment server associated with the second vehicle.
| 4. The self-driving vehicle of claim 3 wherein the second payment server requests that the first payment server verify that funds are available, wherein the first payment server confirms to the second payment server that the funds are available, and wherein the second payment server confirms to the second vehicle that the funds are available.
| 5. The self-driving vehicle of claim 4 wherein the processor requests that the first payment server transfer the funds in response to receiving an acknowledgement from the second vehicle that the availability of the funds has been verified.
| 6. The self-driving vehicle of claim 5 wherein the processor receives a confirmation from the second vehicle that the second vehicle has initiated a manoeuver to reprioritize the self-driving vehicle in traffic relative to the second vehicle.
| 7. The self-driving vehicle of claim 1 wherein the processor cooperates with the radiofrequency data transceiver to communicate two parallel offer messages to the second vehicle and to a third vehicle.
| 8. The self-driving vehicle of claim 7 wherein each of the offer messages contains bits in a data field indicating that the offer is conditional on which of the second and third vehicles is first to reply.
| 9. The self-driving vehicle of claim 1 wherein the processor cooperates with the radiofrequency data transceiver to send two conditional offer messages to the second vehicle and to a third vehicle ahead of the second vehicle.
| 10. The self-driving vehicle of claim 9 wherein the conditional offer messages each contains bits in a data field indicating that the offer is conditional on both the second and third vehicles accepting.
| 11. The self-driving vehicle of claim 1 further comprising a user interface presenting pricing and timing data for two routes to enable a user of the self-driving vehicle to select one of the two routes based on both pricing and timing.
| 12. The self-driving vehicle of claim 1 further comprising a user interface presenting costs and payouts for different traffic prioritizations.
| 13. The self-driving vehicle of claim 1 further comprising a user interface presenting bid-ask pricing for different levels of traffic prioritization for different road segments, wherein bid prices are prices being offered by the self-driving vehicle to the second vehicle for the traffic prioritization and ask prices are prices the second vehicle is asking from the self-driving vehicle to grant the traffic prioritization.
| 14. The self-driving vehicle of claim 1 wherein the processor is configured to receive user-configurable multipliers for setting prices for various types of traffic manoeuvers.
| 15. The self-driving vehicle of claim 1 wherein the traffic prioritization is precedence for a parking space.
| 16. The self-driving vehicle of claim 1 wherein the traffic prioritization is precedence at an intersection.
| 17. The self-driving vehicle of claim 1 wherein the vehicle is a truck and wherein the traffic prioritization is precedence at a loading dock of a warehouse or store.
| 18. The self-driving vehicle of claim 1 wherein the processor is configured to grant precedence to an emergency vehicle upon wirelessly receiving a special code.
| 19. The self-driving vehicle of claim 1 wherein the processor automatically generates the offer message based on predetermined user settings representing priority levels set by a user wherein the priority levels are set based on time and location.
| 20. The self-driving vehicle of claim 1 wherein the payment comprises a transfer to the second vehicle of redeemable points that are stored in a database and are redeemable for a subsequent traffic prioritization in favor of the second vehicle. | The self-driving vehicle has a vehicle chassis, a motor supported by the chassis for providing propulsive power for the vehicle, a braking system, a steering system and several sensors. A processor is configured to receive signals from the sensors and to generate steering, acceleration and braking control signals for controlling the steering system, the motor and the braking system of the vehicle. A global navigation satellite system (GNSS) receiver configured for receiving satellite signals and for determining a current location of the self-driving vehicle and a radiofrequency data transceiver. The processor is configured to transmit an offer message to a second vehicle. A reply message is received from the second vehicle. A payment is transferred to the second vehicle to obtain a traffic prioritization relative to the second vehicle. The self-driving vehicles use sensors such as radio detection and ranging, light detection and ranging or cameras to provide signals to the processor or controller that generates and outputs steering, acceleration and braking signals to the vehicle. Uses included but are not limited to encompass any vehicle such as a car, van, minivan, sports utility vehicle, crossover-type vehicle, bus, minibus, truck, tractor-trailer, semi-trailer, construction vehicle, work vehicle, tracked vehicle, semi-tracked vehicle, offroad vehicle, electric cart, dune buggy. The receiving vehicle have a rule defining a monetary threshold to automatically accept an offer from a requesting vehicle. The emergency vehicle makes the request without offering any payment because the vehicle is an emergency vehicle in a first paradigm. The drawing shows a schematic view of the system for V2V payments for traffic reprioritization.10Autonomous vehicle 222Base station transceiver 250Internet 300First payment server 302Payment processing server | Please summarize the input |
Mobile payment system for traffic prioritization in self-driving vehiclesA self-driving or autonomous vehicle transmits a vehicle-to-vehicle offer message from a user of a vehicle-connected mobile communication device riding in the self-driving vehicle to a second user of a second mobile communication device riding in a second vehicle to pay for a traffic prioritization relative to the second vehicle. The first mobile communication device receives a reply message and sends a payment to the second mobile communication device or an account associated with the second mobile communication device to obtain the traffic prioritization relative to the other vehicle. For example, the traffic prioritization may enable one vehicle to pass the other vehicle, to take precedence at an intersection or to be given priority to take a parking place or any other traffic-related advantage.The invention claimed is:
| 1. A self-driving vehicle comprising:
a vehicle chassis;
a motor supported by the chassis for providing propulsive power for the vehicle;
a braking system;
a steering system;
a plurality of sensors;
a processor configured to receive signals from the sensors and to generate steering, acceleration and braking control signals for controlling the steering system, the motor and the braking system of the vehicle;
a Global Navigation Satellite System (GNSS) receiver for receiving satellite signals and for determining a current location of the self-driving vehicle;
a mobile communication interface communicatively connected to a first mobile communication device of a first user riding in the self-driving vehicle, the mobile communication interface receiving from the first mobile communication device an offer message from the first user to pay for a traffic prioritization relative to a second self-driving vehicle;
a vehicle-to-vehicle data transceiver communicatively connected to the mobile communication interface to transmit the offer message to the second self-driving vehicle to be relayed via a second mobile communication interface to a second mobile communication device of a second user riding in the second self-driving vehicle; and
wherein the mobile communication interface, via the vehicle-to-vehicle data transceiver, receives a reply message from the second mobile communication device and transmits the reply message to the first mobile communication device to cause the first mobile communication device to make a payment from a first account of the first user to a second account of the second user to obtain the traffic prioritization relative to the second self-driving vehicle; and
wherein the mobile communication interface receives a payment message from the first mobile communication device and transmits, via the vehicle-to-vehicle data transceiver, the payment message to the second mobile communication device to confirm that the payment has been being made.
| 2. The self-driving vehicle of claim 1 wherein the mobile communication interface receives a counteroffer from the second mobile communication device and relays the counteroffer to the first mobile communication device to accept or reject the counteroffer, wherein the first mobile communication device is configured to either present the counteroffer and receive user input to accept or reject the counteroffer or automatically accept or reject the counteroffer based on a user setting.
| 3. The self-driving vehicle of claim 1 comprising a fee-for-transport processor that computes a fee to transport the first user from a starting point along a route to a destination, wherein the fee is determined based on distance or travel time and is further based on a user-specified traffic prioritization received from the first mobile communication device.
| 4. The self-driving vehicle of claim 3 wherein the fee-for-transport processor communicates to the first mobile communication device a plurality of pricing options for the route based on different levels of traffic prioritization.
| 5. The self-driving vehicle of claim 4 wherein the fee-for-transport processor computes the travel times for the route for each of the different levels of traffic prioritization, wherein the travel times are computed using real-time traffic data for the route and historical prioritization data for the route for the time of day, the historical prioritization data indicative of probabilities of traffic prioritization requests being accepted for the route at the time of day.
| 6. The self-driving vehicle of claim 5 wherein the fee-for-transport processor receives a user selection of one of the different levels of traffic prioritization from the first mobile communication device, the fee-for-transport processor then automatically offering payments to other vehicles along the route to obtain traffic prioritizations and, when offers are accepted, automatically disbursing payments to the other vehicles.
| 7. The self-driving vehicle of claim 1 wherein the mobile communication interface is a Bluetooth? interface and the vehicle-to-vehicle data transceiver is a dedicated vehicle-to-vehicle short-range communications (DSRC) transceiver operating in a 5.7-5.9 GHz band.
| 8. A non-transitory computer-readable medium storing computer-readable instructions in code which when executed by a processor of a mobile communication device cause the mobile communication device to:
communicatively connect to a mobile communication interface of a self-driving vehicle in which the mobile communication device is located;
receive user input defining an offer to pay for a traffic prioritization that prioritizes the self-driving vehicle relative to a second vehicle;
automatically generate an offer message in response to the user input, the offer message being a datagram in a predetermined data format;
automatically transmit the offer message to the mobile communication interface of the self-driving vehicle for communicating the offer message to a second mobile communication device in the second vehicle, the second mobile communication device being configured to automatically read the datagram;
automatically receive a reply message from the second mobile communication device;
automatically determine if the reply message constitutes an acceptance or rejection of the offer; and
in response to determining that the reply message indicates the acceptance of the offer, send a payment to the second mobile communication device or to an account associated with the second mobile communication device to pay for the traffic prioritization.
| 9. The non-transitory computer-readable medium of claim 8 further comprising code that causes the mobile communication device to receive a confirmation message to confirm receipt of the payment.
| 10. The non-transitory computer-readable medium of claim 9 further comprising code that causes the mobile communication device to receive an acknowledgement message that the second vehicle will maneuver as soon as traffic regulations and traffic conditions permit to grant priority to the self-driving vehicle.
| 11. The non-transitory computer-readable medium of claim 8 further comprising code that causes the mobile communication device to output an alert that an estimated time of arrival at a destination will be later than originally predicted and presenting a user interface element to pay to prioritize the self-driving vehicle in traffic.
| 12. The non-transitory computer-readable medium of claim 8 further comprising code that causes the mobile communication device to receive a third-party request to expedite travel, the third-party request including a third-party payment to prioritize the self-driving vehicle in traffic, wherein the code is configured to automatically generate and transmit a third-party offer message using the third-party payment to the second mobile communication device.
| 13. A non-transitory computer-readable medium storing computer-readable instructions in code which when executed by a processor of a mobile communication device cause the mobile communication device to:
display on a user interface of the mobile communication device a fee-for-transport interface of a fee-for-transport application executing on the mobile communication device to enable a user to summon a self-driving vehicle also executing the fee-for-transport application to transport the user from a starting point to a destination for a fee;
receive user input from the user to define the destination, wherein the starting point is either a current location of the mobile communication device or a user-specified pickup location;
display pricing options based on a plurality of different levels of traffic prioritization for transport to the destination, wherein the pricing options are also based on either distance or travel time to from the starting point to the destination;
receive a user-selected traffic prioritization; and
communicate a pickup request to the self-driving vehicle, the pickup request including the user-selected traffic prioritization to enable the self-driving vehicle to automatically offer one or more payments to one or more other vehicles to obtain the user-selected traffic prioritization along the route to the destination.
| 14. The non-transitory computer-readable medium of claim 13 wherein the code causes the mobile communication device to display a trip report upon arrival at the destination that indicates that the self-driving vehicle has determined that a portion of the fee allocated for prioritization payments has been unused, and the portion of the fee that was unused has been refunded to an account associated with a user of the first mobile communication device.
| 15. The non-transitory computer-readable medium of claim 13 comprising code that causes the mobile communication device to display on the user interface of the mobile communication device an amount payable to arrive at the destination at a user-specified time, to present a user interface element to pay the amount, and to communicate this amount and the user-specified time to the self-driving vehicle.
| 16. The non-transitory computer-readable medium of claim 12 comprising code that causes the mobile communication device to receive real-time traffic data, to detect a traffic jam based on the real-time traffic data by determining that the self-driving vehicle is moving at an average speed less than 20% of a speed limit, and to send a plurality of offer messages to a plurality of vehicles to pay for prioritization.
| 17. The non-transitory computer-readable medium of claim 16 wherein the offer messages are conditional offers that are conditional on acceptance by all of the plurality of vehicles.
| 18. The non-transitory computer-readable medium of claim 13 comprising code that causes the mobile communication device to:
generate an emergency request in response to detecting a 911 call being made by the mobile communication device to signify an emergency, the emergency request requesting that the self-driving vehicle be prioritized in traffic due the emergency;
transmit the emergency request to a governmental authority emergency server to request emergency prioritization; and
receive an emergency prioritization authorization from the governmental authority emergency server, the emergency prioritization authorization comprising a first cryptographic token to be broadcast by the self-driving vehicle to other vehicles to obtain priority in traffic and a second cryptographic token that is recognizable by law enforcement entities permitting the self-driving vehicle to exceed a speed limit due to the emergency.
| 19. The non-transitory computer-readable medium of claim 18 comprising code that causes the mobile communication device to:
determine an emergency destination to replace the destination originally specified by the user;
re-route the self-driving vehicle to the emergency destination; and
constrain the cryptographic token to be valid only for a new route to the emergency destination.
| 20. The non-transitory computer-readable medium of claim 18 comprising code that causes the mobile communication device to:
detect an emergency using a sensor in, or communicatively connected to, the mobile communication device;
generate an emergency request requesting that the self-driving vehicle be prioritized in traffic in response to detecting the emergency;
transmit the emergency request to a governmental authority emergency server to request emergency prioritization; and
receive an emergency prioritization authorization from the governmental authority emergency server, the emergency prioritization authorization comprising one or both of: a first cryptographic token to be broadcast by the self-driving vehicle to other vehicles to obtain priority in traffic and a second cryptographic token recognizable by law enforcement entities permitting the self-driving vehicle to exceed a speed limit due to the emergency. | The vehicle (10) has a mobile communication interface for receiving a reply message from a second mobile communication device through a vehicle-to-vehicle data transceiver, and transmitting the reply message to a first mobile communication device to cause the first mobile communication device to make a payment from a first account of a first user to a second account of a second user to obtain traffic prioritization relative to a second self-driving vehicle. The mobile communication interface receives a payment message from the first mobile communication device and transmits the payment message to the second mobile communication device to confirm that the payment is made through the vehicle-to-vehicle data transceiver. An INDEPENDENT CLAIM is included for a non-transitory computer-readable medium storing computer-readable instructions for operating a self-driving vehicle. Self-driving vehicle i.e car. The vehicle in a given area of a road mutually sense each other's presence using various sensors for collision avoidance and can communicate through vehicle-to-vehicle messaging protocols with each other to avoid collisions. The drawing shows a schematic diagram of a system for V2V payments for traffic reprioritization.10, 10aSelf-driving vehicles 11Exchange v2v messages 222Base stations transceiver 250Internet 300, 302Payment processing servers | Please summarize the input |
SYSTEMS AND METHODS FOR AN AUTONOMOUS CONVOY WITH LEADER VEHICLEA module for a leader vehicle of a convoy can have a suite of sensors, a communication system, and a controller. The sensor suite can have at least one feature sensor that detects features and/or terrain in an environment and at least one location sensor that determines a location of the leader vehicle. Via the sensor suite, the controller can detect features as the leader vehicle travels along a route through the environment as well as the route of the leader vehicle. The controller can build a map for at least part of the environment with the detected route therethrough. Data indicative of the map and the detected route can then be transmitted to one or more follower vehicles. In some embodiments, the leader vehicle is manually driven while the follower vehicles operate autonomously. | The system has a convoy leader module (200) that is used for a leader vehicle of a convoy, and comprises a first suite of sensors (202). The first suite comprises at least one feature sensor operable to detect features or terrain in an environment to be traversed by the leader vehicle and at least one location sensor operable to determine a location of the leader vehicle. A first communication system (204) is operable to transmit one or more signals between the leader vehicle and one or more follower vehicles in the convoy. The route of the leader vehicle is detected through the environment via the at least one location sensor. A map for at least portion of the environment with the detected route is built based at least in portion on the detected one or more features and the detected route. The first data indicative of the map and the detected route are transmitted to the one or more follower vehicles in the convoy via the first communication system. An INDEPENDENT CLAIM is included for a convoy. Convoy system for autonomous vehicles with leader vehicle e.g. manned leader vehicle. The method allows the leader vehicle and the autonomous follower vehicles in the convoy to share a common map, thus improving the efficiency of the convoy. The method enables the convoy leader module to be mounted on and/or integrated with a leader vehicle, so that the leader module can use the detected features and route to construct a map, which can be shared with the follower vehicles. The follower vehicles can have their own sensors that detect the features within the environment and can use detected features to improve the route following. The shared map can include information regarding an environmental aspect such as a slip condition, roadway features, area susceptible to dust generation, and the follower vehicle can implement remedial measures at or in advance of a location of that environmental aspect. The drawing shows a simplified schematic diagram of the manned vehicle with convoy leader module. 200Convoy leader module202Sensor suite204Communication system206Control system208Data storage system | Please summarize the input |
Vehicle-to-vehicle sensor verification using sensor fusion networkA vehicle-to-vehicle sensor authentication using a sensor fusion network is used. The invention claims a system and method for sensor verification using a sensor fusion network. The sensor fusion network may include a plurality of sensors associated with one or more vehicles having autonomous or partial autonomous driving functions.|1. A vehicle sensor verification system, comprising: a coordination processor; the coordination processor is operable for performing data communication with a plurality of vehicles; a sensor fusion network; the sensor fusion network comprises a plurality of sensors; each sensor is in data communication with the coordination processor; the sensor fusion network comprises at least a first sensor operable to generate first data and a second sensor operable to generate second data; wherein the first sensor is associated with a first vehicle of the plurality of vehicles; the first data indicates a first detection state of the object; and the second data indicates a second detection state of the object.
| 2. The system according to claim 1, wherein the second sensor is associated with a second vehicle of the plurality of vehicles.
| 3. The system according to claim 1, wherein the plurality of sensors further comprises a third sensor operable to generate third data indicative of a detected state of the object, and wherein the coordinating processor is operable to be based on the first data; the second data and the third data generating coordination data, the coordination data comprises a weighted detection state of the object.
| 4. The system according to claim 3, wherein based on the first data, the second data and the third data use multiple voting algorithms to generate the coordinated data.
| 5. The system according to claim 3, wherein the second sensor is associated with the first vehicle and the third sensor is associated with the second vehicle.
| 6. The system according to claim 1, wherein the coordinating processor comprises a neural network operable to identify whether a potential trajectory of the first vehicle is free of obstacles.
| 7. The system according to claim 1, wherein the coordination processor is operable to dynamically define the sensor fusion network as the subset of the plurality of sensors according to the proximity of each of the plurality of sensors to the first vehicle.
| 8. The system according to claim 1, wherein the coordination processor is operable to detect a potential fault condition in the first sensor.
| 9. The system according to claim 1, wherein each sensor of the plurality of sensors in the sensor fusion network comprises a specified accuracy; and the coordinating processor is operable to dynamically define the sensor fusion network as subset of sensors based on the specified accuracy of each sensor.
| 10. The system according to claim 9, wherein the coordination processor is operable to select a sensor included in the sensor fusion network based on a minimum specified accuracy.
| 11. The system according to claim 1, wherein the first sensor comprises a sensor type selected from a group of sensor types; the group of sensor types comprises a radar sensor, a laser radar sensor, a proximity sensor, a camera sensor, an infrared sensor and an ultraviolet sensor; an ultrasonic sensor or a sound wave sensor.
| 12. The system according to claim 11, wherein the second sensor comprises a sensor type different from the first sensor.
| 13. The system according to claim 1, wherein the first data further comprises a first confidence value, and the second data further comprises a second confidence value.
| 14. A method for object verification by using sensor fusion network, wherein the sensor fusion network comprises a plurality of sensors, wherein at least the first sensor is associated with the first vehicle, the method comprises: based on the gap measurement of the first sensor to generate a first object data, the first object data comprises a first confidence value of the first object state and the first object state; generating a second object data based on the gap measurement of the second sensor of the plurality of sensors; the second object data comprises a second confidence value of the second object state and the second obstacle state; and generating coordination verification data, the coordination verification data indicates the coordination object state generated by using the first obstacle data and the second obstacle data.
| 15. The method according to claim 14, wherein the second sensor is associated with the second vehicle.
| 16. The method according to claim 15, further comprising generating a third obstacle data based on a gap measurement of a third sensor to a potential trajectory, the third obstacle data including a third confidence value of a third object state and a third object state; and using the first object data, the second object data and the third object data to generate the coordination verification data, wherein the third sensor is associated with the third vehicle.
| 17. The method according to claim 14, wherein the coordination verification data is generated in response to a majority vote algorithm, the majority vote algorithm utilizing at least a first obstacle data, a second obstacle data and a third obstacle data as input.
| 18. The method according to claim 17, wherein the majority of voting algorithms use weighting factors to generate track feasibility data, the weighting factors being based on a first confidence value, a second confidence value, and a third confidence value.
| 19. The method according to claim 14, wherein the coordinated verification data further indicates a coordinated confidence value associated with the coordinated object state. | The system has a coordination processor for performing data communication with multiple vehicles. A sensor fusion network is provided with multiple sensors. Each sensor is in data communication with the coordination processor. The sensor fusion network is provided with two sensors operable to generate two data. A neural network is operable to identify whether potential trajectory of the vehicles is free of obstacles. An INDEPENDENT CLAIM is included for method for performing object verification by using a sensor fusion network. Vehicle-to-vehicle sensor verification system. The system realizes autonomous or partial autonomous driving functions of the vehicles. The drawing shows a top view of a vehicle-to-vehicle sensor verification system. | Please summarize the input |
Method, device, and computer program for controlling stop of autonomous vehicle using speed profileProvided are a method, a device, and a computer program for controlling stop of an autonomous vehicle using a speed profile. The method of controlling, by a computing device, stop of an autonomous vehicle using a speed profile includes obtaining surrounding information of an autonomous vehicle, determining candidate routes for controlling stop of the autonomous vehicle on the basis of the surrounding information, calculating scores for candidate driving plans for the autonomous vehicle to travel the determined candidate routes according to a preset speed profile, and finalizing a driving plan for the autonomous vehicle on the basis of the calculated scores.What is claimed is:
| 1. A method of controlling, by a computing device, stop of an autonomous vehicle using a speed profile, the method comprising:
obtaining surrounding information of an autonomous vehicle;
determining candidate routes for controlling stop of the autonomous vehicle on the basis of the surrounding information;
calculating scores for candidate driving plans for the autonomous vehicle to travel the determined candidate routes according to a preset speed profile; and
finalizing a driving plan for the autonomous vehicle on the basis of the calculated scores.
| 2. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and then stop at the determined candidate stop location according to a first speed profile by applying the first speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, a current acceleration is a0, and a distance to the determined candidate stop location is starget, the first speed profile increases or reduces a speed of the autonomous vehicle from v0 to a preset target speed of vtarget using the current acceleration of a0 and a preset sectional acceleration profile, maintains the speed of the autonomous vehicle at vtarget for a certain period from a time point at which the speed of the autonomous vehicle becomes vtarget, and reduces the speed of the autonomous vehicle from vtarget to zero using the preset sectional acceleration profile and stops the autonomous vehicle at the determined candidate stop location after the certain period, and
wherein the certain period is set such that a distance traveled by the autonomous vehicle according to the first speed profile becomes s target.
| 3. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and then stop at the determined candidate stop location according to a second speed profile by applying the second speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, a current acceleration is a0, and a distance to the determined candidate stop location is starget, the second speed profile increases or reduces a speed of the autonomous vehicle from v0 to a preset target speed of vtarget using the current acceleration of a0 and a preset sectional acceleration profile, maintains the speed of the autonomous vehicle at vtarget for a first period from a time point at which the speed of the autonomous vehicle becomes vtarget, reduces the speed of the autonomous vehicle from vtarget to vtail using the preset sectional acceleration profile after the first period, maintains the speed of the autonomous vehicle at vtail for a second period from a time at which the speed of the autonomous vehicle becomes vtail, and reduces the speed of the autonomous vehicle from vtail to zero using the preset sectional acceleration profile and stops the autonomous vehicle at the determined candidate stop location after the second period,
wherein the first period is set such that a distance traveled by the autonomous vehicle according to the second speed profile becomes a difference between s target and stail, and
the second period is set such that a distance traveled by the autonomous vehicle according to the second speed profile becomes s tail.
| 4. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and then stop at the determined candidate stop location according to a third speed profile by applying the third speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, a current acceleration is a0, a distance to the determined candidate stop location is starget, the third speed profile reduces a speed of the autonomous vehicle from v0 to zero using the current acceleration of a0, a target acceleration of adecel of the autonomous vehicle, and a preset sectional acceleration profile and stops the autonomous vehicle at the determined candidate stop location, and
wherein a decel is set to a value such that a distance traveled by the autonomous vehicle according to the third speed profile becomes starget.
| 5. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and then stop at the determined candidate stop location according to a fourth speed profile by applying the fourth speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, a current acceleration is a0, and a distance to the determined candidate stop location is starget, the fourth speed profile reduces a speed of the autonomous vehicle from v0 to vtail using the current acceleration of a0, a target acceleration of adecel of the autonomous vehicle, and a preset sectional acceleration profile, maintains the speed of the autonomous vehicle at vtail for a certain period from a time point at which the speed of the autonomous vehicle becomes vtail, and reduces the speed of the autonomous vehicle from vtail to zero using the preset sectional acceleration profile and stops the autonomous vehicle at the determined candidate stop location after the certain period, and
wherein the certain period is set such that a distance traveled by the autonomous vehicle from the time point at which the speed of the autonomous vehicle becomes v tail becomes a difference value stail between starget and a distance stravel,ramp traveled by the autonomous vehicle until the speed of the autonomous vehicle reaches vtail.
| 6. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route according to a fifth speed profile by applying the fifth speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0 and a current acceleration is a0, the fifth speed profile increases or reduces a speed of the autonomous vehicle from v0 to vtarget using the current acceleration of a0 and a preset sectional acceleration profile and causes the autonomous vehicle to travel while maintaining the speed of the autonomous vehicle at vtarget.
| 7. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route according to a sixth speed profile by applying the sixth speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, a current acceleration is a0, and a distance to a location at which a preset target speed of vtarget of the autonomous vehicle will be achieved is starget, the sixth speed profile increases or reduces a speed of the autonomous vehicle from v0 to vtarget using the current acceleration of a0, a target acceleration of aadjust of the autonomous vehicle, and a preset sectional acceleration profile and causes the autonomous vehicle to travel while maintaining the speed of the autonomous vehicle at vtarget, and
a adjust is set such that a distance traveled by the autonomous vehicle until the speed of the autonomous vehicle reaches vtarget becomes starget.
| 8. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and stop according to a seventh speed profile by applying the seventh speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0 and a current acceleration is a0, the seventh speed profile reduces a speed of the autonomous vehicle from v0 to zero and stops the autonomous vehicle using the current acceleration of a0, a target acceleration of atarget of the autonomous vehicle, and a preset sectional acceleration profile.
| 9. The method of claim 1, wherein the calculating of the scores comprises calculating the score for the candidate driving plan for the autonomous vehicle to travel the determined candidate route and stop according to an eighth speed profile by applying the eighth speed profile to the autonomous vehicle,
wherein, when a current speed of the autonomous vehicle is v 0, the eighth speed profile reduces a speed of the autonomous vehicle from v0 to zero and stops the autonomous vehicle using a preset acceleration of aemergency, and
a emergency is a value preset without considering a current acceleration of a0 of the autonomous vehicle and a preset sectional acceleration profile.
| 10. The method of claim 1, wherein the calculating of the scores comprises determining whether the determined candidate stop locations correspond to a preset no-stopping zone and correcting the scores calculated for the determined candidate stop locations according to a result of determining whether the determined candidate stop locations correspond to the preset no-stopping zone.
| 11. The method of claim 1, wherein the calculating of the scores comprises calculating the scores for the candidate driving plans for traveling the determined candidate routes using a processor included in the computing device,
when there are a plurality of candidate driving plans for which scores will be calculated because there are the plurality of determined candidate routes or the plurality of candidate stop locations are determined on the determined candidate routes, the calculating of the scores comprises calculating the scores for the plurality of candidate driving plans for traveling the determined candidate routes using a plurality of different processors included in the computing device, the plurality of candidate driving plans including continuously traveling the plurality of candidate routes without stopping or traveling the plurality of candidate routes and then stopping at any one of the plurality of candidate stop locations determined on the plurality of candidate routes, and
the finalizing of the driving plan comprises collecting the scores calculated by the plurality of different processors and finalizing the candidate driving plan having the highest score as the driving plan for the autonomous vehicle.
| 12. The method of claim 1, further comprising:
receiving a target stop location for the autonomous vehicle from a user;
transmitting information on the received target stop location to a server and receiving a control command, which is determined according to scores calculated for the target stop location and a driving plan including a driving method to the target stop location on the basis of the preset speed profile, from the server; and
controlling the autonomous vehicle to stop at the target stop location according to the control command.
| 13. The method of claim 1, further comprising providing guide information of the finalized stop location,
wherein the providing of the guide information comprises providing information on the finalized route, the finalized stop location, and the finalized driving plan through a display provided in the autonomous vehicle, providing the information on the finalized route, the finalized stop location, and the finalized driving plan to another vehicle adjacent to the autonomous vehicle through vehicle-to-vehicle communication, or displaying the finalized stop location on a road on which the autonomous vehicle is traveling through a location display module provided in the autonomous vehicle.
| 14. A device for controlling stop of an autonomous vehicle using a speed profile, the device comprising:
a processor;
a network interface;
a memory; and
a computer program which is loaded into the memory and executed by the processor,
wherein the computer program comprises;
an instruction of obtaining surrounding information of an autonomous vehicle;
an instruction of determining candidate routes for controlling stop of the autonomous vehicle on the basis of the surrounding information;
an instruction of calculating scores for candidate driving plans for the autonomous vehicle to travel the determined candidate routes according to a preset speed profile;
an instruction of finalizing a driving plan for the autonomous vehicle on the basis of the calculated scores; and
an instruction of determining candidate stop locations on the determined candidate routes,
wherein the determining of the candidate stop locations comprises determining, as a candidate stop location, at least one of a location which is spaced a certain distance from a stop line on the determined candidate route, a location which is spaced a certain distance from a location at which an object present on the determined candidate route has stopped or is predicted to stop, and a location input by a driver or a passenger of the autonomous vehicle,
the calculating of the scores comprises calculating scores for the determined candidate stop locations and the candidate driving plans including driving methods to the determined candidate stop locations, and
the finalizing of the driving plan comprises finalizing a route, a stop location, and a driving plan including a driving method to the stop location for the autonomous vehicle on the basis of the calculated score.
| 15. A non-transitory computer-readable recording medium storing a computer program, and configured to be coupled to a computer hardware, the program includes instructions to execute operations of:
obtaining surrounding information of an autonomous vehicle;
determining candidate routes for controlling stop of the autonomous vehicle on the basis of the surrounding information;
calculating scores for candidate driving plans for the autonomous vehicle to travel the determined candidate routes according to a preset speed profile;
finalizing a driving plan for the autonomous vehicle on the basis of the calculated scores; and
determining candidate stop locations on the determined candidate routes,
wherein the determining of the candidate stop locations comprises determining, as a candidate stop location, at least one of a location which is spaced a certain distance from a stop line on the determined candidate route, a location which is spaced a certain distance from a location at which an object present on the determined candidate route has stopped or is predicted to stop, and a location input by a driver or a passenger of the autonomous vehicle,
the calculating of the scores comprises calculating scores for the determined candidate stop locations and the candidate driving plans including driving methods to the determined candidate stop locations, and
the finalizing of the driving plan comprises finalizing a route, a stop location, and a driving plan including a driving method to the stop location for the autonomous vehicle on the basis of the calculated score. | The method involves obtaining surrounding information of an autonomous vehicle (10). Candidate routes (31) are determined for controlling stop of the autonomous vehicle on the basis of the surrounding information. Scores are calculated for candidate driving plans for the vehicle (21) to travel the determined candidate routes according to a preset speed profile. A driving plan is finalized for the vehicles based on the calculated scores. Candidate stop locations (41,42) are determined on the candidate routes. A route, a stop location, and a driving plan including a driving method to the stop location are finalized based on a calculated score by a computing device e.g. personal computer. INDEPENDENT CLAIMS are included for the following:a device for controlling stop of autonomous vehicle using speed profile; anda computer program. Method for controlling stop of autonomous vehicle using speed profile. The method enables preventing the autonomous vehicle from stopping at an inappropriate location e.g. on a crosswalk, in a no-stopping or parking zone, at a crossroad, and close to a fire hydrant. The drawing shows the diagram exemplifying candidate routes and candidate stop locations.10Autonomous vehicle 22Vehicle 31First candidate route 41First candidate stop location 42Second candidate stop location | Please summarize the input |
METHOD, APPARATUS AND COMPUTER PROGRAM FOR GENERATING SURROUNDING ENVIRONMENT INFORMATION FOR AUTOMATIC DRIVING CONTROL OF VEHICLEProvided are a method, device, and computer program for generating surrounding environment information for autonomous driving control of a vehicle. According to various embodiments of the present disclosure, a method for generating surrounding environment information for autonomous driving control of a vehicle is a method performed by a computing device, comprising the steps of collecting first sensor data about the surrounding environment of a first vehicle; Generating environmental information about the first vehicle by using first sensor data obtained from the first sensor; and correcting the generated surrounding environment information by using.|1. A method performed by a computing device, comprising: collecting first sensor data relating to a surrounding environment of a first vehicle;
generating surrounding environment information about the first vehicle by using the collected first sensor data; and correcting the generated surrounding environment information using second sensor data related to a surrounding environment of the second vehicle collected from a second vehicle located adjacent to the first vehicle, wherein the generated surrounding environment information includes: Correcting the information may include setting a reference object using the collected first sensor data and the collected second sensor data;
calculating an error for the set reference object by comparing information on the set reference object included in the collected first sensor data with information on the set reference object included in the collected second sensor data; and correcting the collected second sensor data using the calculated error, and correcting the generated surrounding environment information using the corrected second sensor data. A method for generating surrounding environment information.
| 2. The method of claim 1, wherein the correcting of the generated ambient environment information using the corrected second sensor data comprises converting the generated ambient environment information to the first sensor using the collected first sensor data. dividing a shaded area including a location where data is not collected into a non-shaded area including a location where the first sensor data is collected; and converting the shaded area into the non-shaded area by correcting the shaded area using the corrected second sensor data.
| 3. The method of claim 2, wherein the converting of the shaded area into the non-shaded area comprises: correcting surrounding environment information of the second vehicle generated according to the collected second sensor data using the calculated error; and correcting the shaded area using the corrected surrounding environment information of the second vehicle.
| 4. delete
| 5. The method of claim 1, wherein the calculating of the error with respect to the set reference object comprises time information included in the collected first sensor data and the collected second sensor data using a time protocol. synchronizing the received time information; and comparing the information on the set reference object included in the first sensor data with which the time information is synchronized with the information on the set reference object included in the second sensor data with which the time information is synchronized to determine the set reference object. A method for generating surrounding environment information for autonomous driving control of a vehicle, comprising calculating an error for
| 6. The method of claim 1, wherein the calculating of the error with respect to the set reference object comprises, when there is a history of occurrence of an event for the second vehicle, the event from the second vehicle based on a history of occurrence of the event. collecting information about a first point in time when
calculating a time error between the collected first sensor data and the collected second sensor data by comparing the first time point with a second time point when the first vehicle detects an event generated from the second vehicle; and correcting time information included in the collected first sensor data and time information included in the collected second sensor data by using the calculated time error, and correcting the time information included in the corrected first sensor data. Comparing information on the reference object with information on the set reference object included in the calibrated second sensor data to calculate an error for the set reference object, How to generate information.
| 7. The method of claim 1, wherein the calculating of the error for the set reference object comprises, when two or more reference objects are set, information on the set two or more reference objects included in the collected first sensor data and the collected calculating two or more position errors for each of the two or more set reference objects by comparing information on the set two or more reference objects included in the second sensor data; and determining a position error between the collected first sensor data and the collected second sensor data by optimizing the calculated sum of the two or more position errors to have a minimum value. A method for generating surrounding environment information for
| 3. The method of claim 2, wherein the converting of the shaded area to the non-shaded area comprises: the calculated error—the calculated error when the surrounding environment information of the second vehicle includes dynamic object information; Time information, location information, and direction information of the dynamic object included in the dynamic object information are corrected using - including time error, position error, and direction error for the object, and the corrected dynamic object information is used to A method of generating surrounding environment information for autonomous driving control of a vehicle, comprising correcting a shaded area.
| 9. The method of claim 1, wherein the setting of the reference object comprises position information about the set reference object included in the collected first sensor data and information about the set reference object included in the collected first sensor data. Comparing location information to calculate a location difference value; and determining whether a reference object set using the collected first sensor data and a reference object set using the second sensor data are the same object according to whether the calculated position difference value is within a predetermined value. A method for generating surrounding environment information for autonomous driving control of a vehicle, comprising:
| 3. The method of claim 2, wherein the converting the shaded area into the non-shaded area comprises, when two or more second sensor data are collected from two or more second vehicles adjacent to the first vehicle, the collected two or more second sensor data. Correcting the shaded area using each of the second sensor data, but calculating the importance of the two or more second vehicles when the collected two or more second sensor data collected at the location corresponding to the shaded area are different and correcting the shadow area using only the second sensor data collected from the second vehicle having the highest calculated importance.
| 11. The method of claim 1, wherein second sensor data about the surrounding environment of the second vehicle is collected from the second vehicle by being directly connected to the second vehicle through V2V communication (Vehicle-to-Vehicle Communication), or a plurality of Connecting to a control server that collects sensor data on the surrounding environment of the vehicle and receiving second sensor data on the surrounding environment of the second vehicle from the control server, further comprising: How to generate environmental information.
| 12. The method of claim 1, wherein the generating of the surrounding environment information comprises: generating a grid map for a predetermined range based on the first vehicle, wherein the grid map includes a plurality of grids; And by recording the collected first sensor data on a grid corresponding to a location where the collected first sensor data was collected, a non-shaded area including a grid on which the collected first sensor data was recorded and the collected first sensor data Generating ambient environment information including a shaded area including a grid in which first sensor data is not recorded, and correcting the generated ambient environment information using the corrected second sensor data, A method of generating ambient environment information for controlling autonomous driving of a vehicle, comprising correcting second sensor data collected at a location corresponding to a grid included in the shaded area and recording the corrected second sensor data in a grid included in the shaded area.
| 13. Processor;
Network interface;
Memory; and a computer program loaded into the memory and executed by the processor, wherein the computer program includes instructions for collecting first sensor data related to a surrounding environment of the first vehicle;
instructions for generating surrounding environment information about the first vehicle by using the collected first sensor data; and instructions for correcting the generated surrounding environment information using second sensor data related to the surrounding environment of the second vehicle collected from a second vehicle located adjacent to the first vehicle, wherein the generated surrounding environment information includes: The instructions for correcting information may include instructions for setting a reference object using the collected first sensor data and the collected second sensor data;
instructions for calculating an error for the set reference object by comparing information on the set reference object included in the collected first sensor data with information on the set reference object included in the collected second sensor data; and an instruction for correcting the collected second sensor data using the calculated error and correcting the generated surrounding environment information using the corrected second sensor data. A computing device that performs a method for generating surrounding environment information.
| 14. coupled with the computing device, collecting first sensor data relating to the surrounding environment of the first vehicle;
generating surrounding environment information about the first vehicle by using the collected first sensor data; and correcting the generated surrounding environment information using second sensor data related to a surrounding environment of the second vehicle collected from a second vehicle located adjacent to the first vehicle, wherein the generated surrounding environment information includes: Correcting the information may include setting a reference object using the collected first sensor data and the collected second sensor data;
calculating an error for the set reference object by comparing information on the set reference object included in the collected first sensor data with information on the set reference object included in the collected second sensor data; and correcting the collected second sensor data using the calculated error, and correcting the generated surrounding environment information using the corrected second sensor data. A computer program stored in a recording medium readable by a computing device in order to execute an environmental information generating method. | The method involves setting a reference object using the collected first sensor data and the collected second sensor data. An error for the set reference object is calculated by comparing information on the set reference object included in the collected first sensor data with information on the set reference object included in the collected second sensor data. The collected second sensor data is corrected using the calculated error, and the generated surrounding environment information is corrected using the corrected second sensor data. INDEPENDENT CLAIMS are included for the following:a computing device for generating surrounding environment information for autonomous driving control of vehicle; anda computing program for generating surrounding environment information for autonomous driving control of vehicle. Method for generating surrounding environment information for autonomous driving control of vehicle. The safer self-driving control of the host vehicle is enabled by removing an area in which the host vehicles cannot perceive it as a result of the correction. The drawing shows a flowchart illustrating the process for generating surrounding environment information for autonomous driving control of vehicle. (Drawing includes non-English language text) S110Step for collecting first sensor data about the surrounding environment of the first vehicleS120Step for generating surrounding environment information about the first vehicleS130Step for using second vehicle data collected from the second vehicle adjacent to the first vehicle | Please summarize the input |
Method for automatically driving vehicle in group queue, device and electronic deviceThe invention claims a method for automatically driving vehicle in group team, device and electronic device; Wherein, the method comprises: the RSU side obtains the information sent by the target automatic driving vehicle, and periodically broadcasts the group request information to confirm at least one group automatic driving vehicle of the application group in the rest automatic driving vehicle; obtaining the team vehicle attribute information of the team automatic driving vehicle, and according to the target vehicle attribute information and team vehicle attribute information for team planning, obtaining the target team strategy; the target group strategy respectively sent to the target automatic driving vehicle and team automatic driving vehicle, so that the target automatic driving vehicle and team automatic driving vehicle group according to the target group strategy; so as to realize the remote distance (namely exceeds the vehicle communication distance) automatic driving vehicle information interaction through the RSU side and the way group, improves the high efficiency and safety of the automatic driving vehicle in the group.|1. A method for automatically driving vehicle in group queue, wherein it is applied to the RSU side; the RSU side is in communication connection with the vehicle side; wherein the vehicle side comprises: a plurality of automatic driving vehicle in the same driving direction, and the distance between any two of the automatic driving vehicle is not less than the vehicle communication distance, the method comprises: obtaining the information sent by the target automatic driving vehicle; Wherein, the information includes: target vehicle attribute information and team request information; periodically broadcasting the group request information to confirm at least one group automatic driving vehicle of the application group in the rest of the automatic driving vehicle; obtaining the team vehicle attribute information of the team automatic driving vehicle, and according to the target vehicle attribute information and the team vehicle attribute information for team planning, obtaining the target team strategy; sending the target group strategy to respectively target automatic driving vehicle and the group automatic driving vehicle, so that the target automatic driving vehicle and the group automatic driving vehicle group according to the target group strategy.
| 2. The method according to claim 1, wherein the vehicle attribute information of each said automatic driving vehicle comprises: static information and dynamic information; Wherein, the static information includes: vehicle body parameter and vehicle engine power; the dynamic information comprises: real time position and real time speed; according to the target vehicle attribute information and the team vehicle attribute information of the team planning step, comprising: according to the target dynamic information of the target automatic driving vehicle and the team dynamic information of the team automatic driving vehicle, performing team planning according to the oil consumption as the target, obtaining the target team strategy; Wherein, the target group policy comprises: in the formation process, the target automatic driving vehicle corresponding to the first group speed, the group automatic driving vehicle corresponding to the second group speed, and the group finishing time.
| 3. The method according to claim 2, wherein the step of sending the target team strategy to respectively target automatic driving vehicle and the team automatic driving vehicle comprises the following steps: sending the first group team speed and the group team finishing time to the target automatic driving vehicle, and sending the second group team speed and the group team finishing time to the group team automatic driving vehicle, so that the target automatic driving vehicle according to the first group team speed; the group automatic driving vehicle group according to the second group speed.
| 4. The method according to claim 1, wherein the method further comprises: obtaining the team information sent by the target automatic driving vehicle; Wherein, the team information comprises at least one of the following: group ID information, the ID information of the pilot vehicle, the driving direction of the team, the current position of the team, each vehicle information in the team, the cruising speed of the team, the train space of the team, the member number of the team, the ID list of the team member, the length of the team and the driving route of the team; sending the team information to the team automatic driving vehicle.
| 5. The method according to claim 1, wherein each of the automatic driving vehicle is further configured with a free cruise mode and a group cruise mode; the method further comprises: when monitoring the group is finished, generating mode switching instruction; the mode switching instruction respectively sent to the target automatic driving vehicle and the group automatic driving vehicle, so that the target automatic driving vehicle and the group automatic driving vehicle are switched from the free cruise mode to the group cruise mode according to the mode switching instruction.
| 6. The method according to claim 1, wherein the group request information further carries with priority information; the method further comprises: obtaining the group request information set; wherein the group request information set comprises a plurality of the group request information of the same time, each of the group request information corresponding to different of the automatic driving vehicle; based on the priority information carried by the group request information, determining the target group request information.
| 7. The method according to claim 1, wherein the step of confirming at least one group automatic driving vehicle of the application group in the rest of the automatic driving vehicles comprises: when monitoring the confirmation application team information, the other said automatic driving vehicle, the confirmation application group information corresponding to the automatic driving vehicle is confirmed as the team automatic driving vehicle.
| 8. An automatic driving vehicle in the group device, wherein it is applied to the RSU side; the RSU side is connected with the vehicle side communication; wherein the vehicle side comprises: a plurality of automatic driving vehicle in the same driving direction, and the distance between any two of the automatic driving vehicle is not less than the vehicle communication distance, the device comprises: an obtaining module for obtaining the information sent by the target automatic driving vehicle; Wherein, the information includes: target vehicle attribute information and team request information; a broadcast module, for periodically broadcasting the team request information to confirm at least one group automatic driving vehicle of the application group in the rest of the automatic driving vehicle; planning module, for obtaining the team vehicle attribute information of the team automatic driving vehicle, and according to the target vehicle attribute information and the team vehicle attribute information for team planning, obtaining the target team strategy; a sending module, used for sending the target team strategy to respectively target automatic driving vehicle and the team automatic driving vehicle, so that the target automatic driving vehicle and the team automatic driving vehicle group according to the target team strategy.
| 9. An electronic device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the steps of the method according to any one of claims 1-7 when the computer program is executed.
| 10. A computer readable storage medium, wherein the computer readable storage medium is stored with a computer program; when the computer program is run by the processor, executing the steps of the method according to any one of the preceding claims 1-7 | The method involves obtaining information sent by a target automatic driving vehicle, where the information includes target vehicle attribute information and team request information. The group request information is periodically broadcasted. The target vehicle attribute information of a team is obtained. A target team strategy is obtained according to target vehicle information and team attribute information. Team planning process is performed on the target team strategy. Each target group strategy is sent to each target automatic drive vehicle and a group automatic drive vehicles. The target automatic driving vehicle and a group automatic driving vehicle group are determined according to the target group strategy. INDEPENDENT CLAIMS are included for: (1) a device for automatically driving vehicle in group queue in RSU side;(2) an electronic device comprising a processor and a memory to execute a set of instructions for performing a method for automatically driving vehicle in group queue in RSU side;(3) a computer readable storage medium for storing a set of instructions for performing a method for automatically driving vehicle in group queue in RSU side. Method for automatically driving vehicle in group queue in restricted stock unit (RSU) side. The method enables realizing long distance automatic driving vehicle information interaction through the RSU side and the way group so as to improve high efficiency and safety of the automatic driving vehicles in the group. The method enables allowing the target vehicle to perform team planning according to the target dynamic information and the team dynamic information, so that the target driving vehicle and team driving vehicle team is formed according to a target team strategy, and improving the high efficiency of the vehicle in the vehicle group. The drawing shows a flow diagram a method for automatically driving vehicle in group queue in RSU side. (Drawing includes non-English language text). | Please summarize the input |
AUTONOMOUS VEHICLE COMMUNICATION FRAMEWORK FOR MULTI-NETWORK SCENARIOSApproaches for Multi-Access Edge Computing (MEC) Vehicle-to-Everything (V2X), Vehicle-To-Vehicle (V2V), and Autonomous Vehicles Distributed Networks (AVDN) functions in a MEC infrastructure are discussed. In various examples, operations and network configurations are described that use a service in an AVDN, including: identifying a service condition (e.g., based on a state of a service and connectivity to an instance of the service); establishing a connection in the AVDN in response to the service condition (e.g., using vehicle-to-vehicle (V2V) or Vehicle-to-Everything (V2X) network communications to the AVDN); and performing a service operation with the service via the AVDN.What is claimed is:
| 1. A user equipment (UE) of a first autonomous vehicle (AV), comprising:
a network interface configured to perform vehicle-to-vehicle (V2V) or Vehicle-to-Everything (V2X) network communications with an autonomous vehicle distributed network (AVDN); and
at least one processor configured to:
identify a service condition based on a state of a service and connectivity to an instance of the service in an infrastructure network;
establish a connection in the AVDN to a second UE at a second AV in response to the service condition, using the V2V or V2X network communications, the AVDN further to provide connectivity between the UE and the second UE for use of the service; and
perform a service operation with the service via the AVDN, using the connection to the second UE. cm 2. The UE of claim 1, wherein the AVDN is formed among a plurality of AVs, the AVDN connecting at least the UE of the first AV and the second UE of the second AV.
| 3. The UE of claim 1, wherein the service operation performed by the UE includes providing a service request to the AVDN, wherein the first AV operates as a service requestor, and wherein the second AV operates as a service provider.
| 4. The UE of claim 1, wherein the service operation performed by the UE includes fulfillment of a service request from the AVDN, wherein the first AV operates as a service provider, and wherein the second AV operates as a service requestor.
| 5. The UE of claim 1, wherein the service condition is identified in response to a change of the state of the service, and wherein the change of the state of service is associated with one or more of:
availability of data from the service;
availability of a resource used by the service;
unavailability of the instance of the service in the infrastructure network; or
a safety-related scenario involving the first AV, the second AV, or the service.
| 6. The UE of claim 1, wherein the service condition is identified in response to the UE being located outside a coverage area of the infrastructure network, and wherein the infrastructure network is a wireless network operated from one or more fixed locations and operated in accordance with a standard from a 3rd Generation Partnership Project (3GPP) 5G, Intelligent Transport Systems (ITS)-G5, or Dedicated Short Range Communications (DSRC) family of standards.
| 7. The UE of claim 1, wherein the service operation relates to: data sharing, decision sharing, or task computation sharing; and
wherein the service operation provides fulfillment of an application operating at the first AV or the second AV.
| 8. The UE of claim 1, wherein the service is provided by a Multi-Access Edge Computing (MEC) host,
wherein the MEC host operates according to a standard from an European Telecommunications Standards Institute (ETSI) MEC standards family, and
wherein (i) the UE operates as a MEC client and the second UE operates as the MEC host, or (ii) the UE operates as the MEC host and the second UE operates as a MEC client.
| 9. The UE of claim 1, wherein the service operation is established using an Application Programming Interface (API) for the AVDN, the API for the AVDN providing a standardized interface to invoke the service operation between the UE and the second UE.
| 10. The UE of claim 1, the at least one processor further configured to:
perform authentication of the UE with an authentication server of the AVDN, wherein the connection with the AVDN is established in response to successful authentication.
| 11. The UE of claim 1, wherein the UE is configured by the AVDN to operate as an anchor service provider, wherein the at least one processor is further configured to:
perform a service request with a third UE of a third AV;
obtain service response data, in response to the service request with the third UE; and
provide the service response data to the second UE.
| 12. At least one non-transitory machine readable medium including instructions for coordinating service operations from a first user equipment (UE) of an autonomous vehicle (AV) with an autonomous vehicle distributed network (AVDN), wherein the instructions, when executed by processing circuitry, cause the processing circuitry to perform operations comprising:
identify a service condition, based on a state of a service and connectivity to an instance of the service in an infrastructure network;
establish a connection in the AVDN to a second UE at a second AV in response to the service condition, using vehicle-to-vehicle (V2V) or Vehicle-to-Everything (V2X) network communications to the AVDN, the AVDN further to provide connectivity between the UE and the second UE for use of the service; and
perform a service operation with the service via the AVDN, using the connection to the second UE.
| 13. The non-transitory machine readable medium of claim 12, wherein the AVDN is formed among a plurality of AVs, the AVDN connecting at least the UE of the first AV and the second UE of the second AV.
| 14. The non-transitory machine readable medium of claim 12, wherein the service operation performed by the UE includes providing a service request to the AVDN, wherein the first AV operates as a service requestor, and wherein the second AV operates as a service provider.
| 15. The non-transitory machine readable medium of claim 12, wherein the service operation performed by the UE includes fulfillment of a service request from the AVDN, wherein the first AV operates as a service provider, and wherein the second AV operates as a service requestor.
| 16. The non-transitory machine readable medium of claim 12, wherein the service condition is identified in response to a change of the state of the service, and wherein the change of the state of service is associated with one or more of:
availability of data from the service;
availability of a resource used by the service;
unavailability of the instance of the service in the infrastructure network; or
a safety-related scenario involving the first AV, the second AV, or the service.
| 17. The non-transitory machine readable medium of claim 12, wherein the service condition is identified in response to the UE being located outside a coverage area of the infrastructure network, and wherein the infrastructure network is a wireless network operated from one or more fixed locations and operated in accordance with a standard from a 3rd Generation Partnership Project (3GPP) 5G, Intelligent Transport Systems (ITS)-G5, or Dedicated Short Range Communications (DSRC) family of standards.
| 18. The non-transitory machine readable medium of claim 12, wherein the service operation relates to: data sharing, decision sharing, or task computation sharing; and
wherein the service operation provides fulfillment of an application operating at the first AV or the second AV.
| 19. The non-transitory machine readable medium of claim 12, wherein the service is provided by a Multi-Access Edge Computing (MEC) host,
wherein the MEC host operates according to a standard from an European Telecommunications Standards Institute (ETSI) MEC standards family, and
wherein (i) the UE operates as a MEC client and the second UE operates as the MEC host, or (ii) the UE operates as the MEC host and the second UE operates as a MEC client.
| 20. The non-transitory machine readable medium of claim 12, wherein the service operation is established using an Application Programming Interface (API) for the AVDN, the API for the AVDN providing a defined interface to invoke the service operation between the UE and the second UE.
| 21. The non-transitory machine readable medium of claim 12, the instructions further to perform operations comprising:
performing authentication of the UE with an authentication server of the AVDN, wherein the connection with the AVDN is established in response to successful authentication.
| 22. The non-transitory machine readable medium of claim 12, wherein the UE is configured by the AVDN to operate as an anchor service provider, the instructions further to perform operations comprising:
performing a service request with a third UE of a third AV;
obtaining service response data, in response to the service request with the third UE; and
providing the service response data to the second UE.
| 23. A system, comprising:
at least one network communication device adapted to perform vehicle-to-vehicle (V2V) or Vehicle-to-Everything (V2X) network communications; and
at least one processing device that, when in operation, is configured by instructions to:
operate the at least one network communication device to establish an autonomous vehicle distributed network (AVDN), using the V2V or V2X network communications;
receive a service request via the AVDN, the AVDN further to provide connectivity between the at least one processing device and at least one other device to operate at least one service;
identify a service condition based on the service request; and
perform a service operation with the at least one service, via the AVDN, based on the identified service condition.
| 24. The system of claim 23, wherein the at least one network communication device and the at least one processing device is included in a first autonomous vehicle (AV), wherein the service operation includes providing a service request to at least a second AV accessible via the AVDN, wherein the first AV operates as a service requestor, and wherein the second AV operates as a service provider.
| 25. The system of claim 23, wherein the at least one network communication device and the at least one processing device is included in a first autonomous vehicle (AV), wherein the service operation includes fulfillment of a service request from at least a second AV accessible via the AVDN, wherein the first AV operates as a service provider, and wherein the second AV operates as a service requestor. | The user equipment has a network interface configured to perform vehicle-to-vehicle (V2V) or Vehicle-to-Everything (V2X) network communications with an autonomous vehicle distributed network (AVDN). A processor (1704) identifies a service condition based on a state of a service and connectivity to an instance of the service in an infrastructure network, and establishes a connection in the AVDN to a second UE at a second AV in response to the service condition using the V2V or V2X network communications. The AVDN provides connectivity between the firs UE and the second UE for use of the service. The processor performs a service operation with the service through the AVDN using the connection to the second UE. INDEPENDENT CLAIMS are included for:(1) a non-transitory machine-readable medium including instructions for coordinating service operations from first UE of AV with AVDN;(2) a system for coordinating service operations from first UE of AV with AVDN. User equipment (UE) for a first autonomous vehicle (AV) e.g. car for coordinating service operations from first AV with autonomous vehicle distributed network (AVDN), in network settings such as multi-access edge computing (MEC) infrastructures and in multi-mobile network operator (MNO) scenarios. The method provides reduced latency, increased responsiveness, and more available computing power than offered in traditional cloud network services and wide area network connections. The method allows a cloud consumer to unilaterally provision computing capabilities such as server time and network storage, as needed automatically without requiring human interaction with a service's provider, so that the capabilities can be rapidly and elastically provisioned automatically to quickly scale out and rapidly released with minimal management effort or interaction with the service provider. The drawing shows a block diagram of a compute node system.1700Edge compute node 1702Compute circuitry 1704Processor 1706Memory 1710Data storage | Please summarize the input |
CLIMATE BASED SELF- SPEED CONTROL SYSTEM IN CAR USING ARTIFICIAL INTELLIGENCEThis system introduces a paradigm shift in vehicular autonomy, integrating adaptive artificial intelligence to not only enable autonomous driving but also dynamically adjust vehicle speed based on real-time climate and environmental conditions. These sensors meticulously capture and feed real-time data on the vehicle's surroundings into a robust artificial intelligence framework. Unlike conventional systems, our solution employs cutting-edge machine learning algorithms to process and fuse this data, facilitating precise decision-making. The self-speed control system transcends the traditional boundaries of autonomous driving by actively responding to an array of environmental factors. It continuously monitors and adapts to factors such as traffic conditions, weather dynamics, temperature fluctuations, wind patterns. Air Quality Index (AQl), road infrastructure, and unforeseen obstacles. Crucially, the system operates with a paramount focus on safety, restraining speed until optimal environmental conditions are assured. This dynamic approach ensures not only safe but also smooth and efficient navigation, even in the most challenging and unpredictable environments. Through this innovative self-speed control system, we pave the way for a safer, more efficient transportation landscape.|1. The Climate based self-speed control system in car using Artificial lnteligence comprises: LiDAR sensor(1), Radar sensors(2), ultrasonic sensor(3), Steering system(4). Throttle and brakes(5), Battery(6), Cellular network(7),V2x Communication(8), GPS and inertial navigation systems(10).
| 2. LiDAR (Light Detection and Ranging)(1): This sensor emits laser pulses and measures the reflected light to create a highly accurate 3D map of the surroundings. It's like having superpowered vision, able to see obstacles in darkness, fog, and even behind comers.
| 3. Radar (Radio Detection and Ranging)(2): Similar to LiDAR(1), radar uses radio waves to detect objects and measure their distance and speed. It's a good backup for LiDAR(1), especially in bad weather conditions.
| 4. Ultrasonic sensors(3): These sensors emit high-frequency sound waves to detect nearby objects, providing short-range obstacle detection, especially useful for parking and manoeuvring in tight spaces.
| 5. Steering system(4): The car's steering wheel is controlled by electric motors or hydraulic actuators that turn the wheels based on the decisions made by the computer.
| 6. Throttle and brakes(5): The car's speed is controlled by electronically controlled motors that adjust the throttle and apply the brakes as needed.
| 7. Battery(6): Self-Speed control cars typically use large batteries to power all the on-board electronics and sensors. Some may have hybrid systems with an additional engine for range extension
| 8. Cellular network(7): Self-Speed control cars can connect to the cellular network to download maps, traffic updates, and communicate with other vehicles or infrastructure.
| 9. V2X (Vehicle-to-Everything) communication(8): This technology allows cars to communicate directly with each other and with roadside infrastructure, further enhancing safety and traffic flow.
| 10. GPS and inertial navigation systems(9): These provide precise location and direction information, even in areas with limited cellular coverage. | The system has a light detection and ranging (LiDAR) sensor for emitting laser pulses and measuring reflected light to create a highly accurate three-dimensional (3D) map of surroundings. Ultrasonic sensors emit high-frequency sound waves to detect nearby objects for providing short-range obstacle detection. Electric motors or hydraulic actuators control car's steering wheel and turns the wheels based on the decisions made by a computer. Electronically controlled motors control car's speed and adjust a throttle. A battery powers all on-board electronics and sensors. INDEPENDENT CLAIMS are included for: (1) liDAR Light Detection and Ranging: This sensor emits laser pulses and measures the reflected light to create a highly accurate D map of the surroundings. It's like having superpowered vision; (2) radar Radio Detection and Ranging: Similar to LiDAR; (3) ultrasonic sensors: These sensors emit high-frequency sound waves to detect nearby objects; (4) steering system: The car's steering wheel is controlled by electric motors or hydraulic actuators that turn the wheels based on the decisions made by the computer.; (5) throttle and brakes: The car's speed is controlled by electronically controlled motors that adjust the throttle and apply the brakes as needed.; (6) battery: Self-Speed control cars typically use large batteries to power all the on-board electronics and sensors. Some may have hybrid systems with an additional engine for range extension; (7) cellular network: Self-Speed control cars can connect to the cellular network to download maps; (8) vX Vehicle-to-Everything communication: This technology allows cars to communicate directly with each other and with roadside infrastructure; (9) gPS and inertial navigation systems: These provide precise location and direction information. Climate based self-speed control system for autonomous vehicles i.e. self-driving cars. The system operates in a manner that restricts the driver from increasing the speed until the environment reaches a climate-neutral state, thus ensuring a safer and more efficient mode of transportation. By harnessing cutting-edge technologies and their seamless integration, the system aims to redefine the capabilities of autonomous vehicles, specifically targeting their adaptability to varying climate conditions, ultimately enhancing road safely and optimizing transportation efficiency in diverse environmental settings. The system is capable of empowering vehicles to autonomously regulate their speed, navigate diverse and challenging environments, and dynamically adapt to various driving scenarios. | Please summarize the input |
TRANSMISSION CONTROL IN APPLICATION LAYER BASED ON RADIO BEARER QUALITY METRICS IN VEHICULAR COMMUNICATIONMethods, apparatuses, and computer-readable mediums for wireless communication are disclosed by the present disclosure. In an aspect, an application layer in a user equipment (UE) receives, from an access layer in the UE, a quality of service (QoS) indication comprising a metric that represents a quality of one or more radio bearers used for a vehicular communication with one or more other UEs. The application layer performs a transmission control over the vehicular communication based on the QoS indication.What is claimed is:
| 1. A method of wireless communication, comprising:
receiving, by an application layer in a user equipment (UE), from an access layer in the UE, a quality of service (QoS) indication comprising a metric that represents a quality of one or more radio bearers used for a vehicular communication with one or more other UEs; and
performing, at the application layer, a transmission control over the vehicular communication based on the QoS indication.
| 2. The method of claim 1, wherein the metric is indicative of a message reception performance as affected by a presence or an absence of message interference or collision in the one or more radio bearers.
| 3. The method of claim 1, wherein the performing comprises adjusting a transmission rate of a unicast communication of the UE, according to the QoS indication.
| 4. The method of claim 1, wherein the performing comprises adjusting a transmission range of a groupcast communication of the UE, according to the QoS indication.
| 5. The method of claim 1, wherein the performing comprises adjusting a maneuver of the UE, according to the QoS indication.
| 6. The method of claim 1, wherein the performing comprises adjusting an autonomous driving status of the UE, according to the QoS indication.
| 7. The method of claim 1, further comprising sharing sensor data of the UE with a remote UE via a unicast communication at a first transmission rate.
| 8. The method of claim 7, wherein the performing comprises:
determining, by the application layer, based on the QoS indication, a second transmission rate supportable by the unicast communication; and
adjusting the unicast communication according to the second transmission rate.
| 9. The method of claim 8, wherein the adjusting comprises performing inter-transmission time (ITT) control at the UE.
| 10. The method of claim 8,
wherein the sharing comprises sharing video sensor data of the UE with the remote UE over the unicast communication; and
wherein the adjusting comprises adjusting a video resolution of a video codec of the UE according to the second transmission rate supportable by the unicast communication.
| 11. The method of claim 7, wherein the receiving comprises receiving a packet error rate (PER) related to the unicast communication with the remote UE.
| 12. The method of claim 7, wherein the receiving comprises receiving a negative acknowledgement (NACK) statistic related to the unicast communication with the remote UE.
| 13. The method of claim 1, further comprising:
communicating, by the UE, with a plurality of other UEs via a groupcast communication; and
wherein the receiving comprises receiving at least one of a packet error rate (PER) or a negative acknowledgement (NACK) statistic related to the groupcast communication with the plurality of other UEs.
| 14. The method of claim 13, wherein the performing comprises:
determining, based on the at least one of the PER or the NACK statistic, that a reachable range of the UE fails to comply with a minimum range requirement of a vehicular application configured for controlling a maneuver of the UE.
| 15. The method of claim 14, wherein the performing further comprises cancelling the maneuver of the UE.
| 16. The method of claim 14, wherein the performing further comprises postponing the maneuver of the UE.
| 17. The method of claim 14, wherein the performing further comprises regenerating a driving strategy of the UE to match the reachable range.
| 18. The method of claim 14, wherein the performing further comprises:
modifying a range of the UE according to the reachable range; and
adjusting the maneuver of the UE based on the range.
| 19. The method of claim 18, wherein the modifying comprises adjusting a radiated power of the UE.
| 20. The method of claim 18, wherein adjusting the maneuver comprises slowing down the UE.
| 21. The method of claim 18, wherein adjusting the maneuver comprises following a stop and go operation at the UE.
| 22. The method of claim 18, wherein adjusting the maneuver comprises exiting an autonomous driving mode at the UE.
| 23. The method of claim 14, wherein the maneuver comprises a coordinated intersection crossing.
| 24. The method of claim 1, wherein the QoS indication comprises one or more of a Packet Error Rate (PER), a Packet Received Rate (PRR), an average number of retransmissions, an average PER, an average PRR, or an acknowledgement (ACK)/negative acknowledgement (NACK) statistic.
| 25. The method of claim 1, wherein the QoS indication comprises a range statistic of a groupcast group.
| 26. The method of claim 1, wherein the QoS indication comprises a supported bit rate for a radio bearer.
| 27. The method of claim 1, wherein the vehicular communication comprises a new radio (NR) vehicle-to-everything (V2X) communication.
| 28. A user equipment (UE) for wireless communication, comprising:
a memory storing instructions; and
a processor in communication with the memory, wherein the processor is configured to execute the instructions to:
receive, by an application layer in the UE, from an access layer in the UE, a quality of service (QoS) indication comprising a metric that represents a quality of one or more radio bearers used for a vehicular communication with one or more other UEs; and
perform, at the application layer, a transmission control over the vehicular communication based on the QoS indication.
| 29. The UE of claim 28, wherein the processor is further configured to execute the instructions to adjust a transmission rate of a unicast communication of the UE, according to the QoS indication.
| 30. The UE of claim 28, wherein the processor is further configured to execute the instructions to adjust a transmission range of a groupcast communication of the UE, according to the QoS indication. | The method involves using the application layer (142) in a user equipment (UE) (148) to receive a QoS indication (144) from the access layer (146) in the UE. The QoS indication includes a metric that represents a quality of the radio bearers used for vehicular communication with other UEs (104,149). The application layer then performs transmission control over the vehicular communication based on the QoS indication. An INDEPENDENT CLAIM is also included for a UE used for wireless communication. Wireless communication method for use in vehicular communication systems. Can be used in transmission control in application layer based on radio bearer quality metrics in vehicular communication, including vehicle-to-vehicle (V2V) communication, vehicle-to-pedestrian (V2P) communication, vehicle-to-everything (V2X) communication, enhanced vehicle-to-everything (eV2X) communication, and cellular vehicle-to-everything (C-V2X) communication. Provides a wireless communication method that ensures improved autonomous driving, e.g., in self-driving vehicles operating with reduced or zero human input, and improved driving experience, e.g., improved non-autonomous human driving. The drawing shows a schematic diagram illustrating a wireless communication system and an access network. 104,149Other UEs142Application layer144QoS indication146Access layer148UE | Please summarize the input |
CONGESTION CONTROL FOR NR V2XIn one aspect, a method includes determining, by a user equipment (UE), a channel busy ratio (CBR) window for a CBR measurement for one or more resources; determining, by the UE, a CBR measurement value for the CBR window and for the one or more resources; determining, by the UE, a channel occupancy ratio (CR) window based on a first number of subframes used for a history of past transmissions and based on a second number of subframes used for future planned transmissions and corresponding retransmissions; and determining, by the UE, a CR value for the CR window based on subchannels used for the one or more resources for the first number of subframes and based on subchannels estimated for the one or more resources for the second number of subframes. In another aspect, a method includes determining a CR window based on a CBR measurement value. | The method involves determining (700) a channel busy ratio (CBR) window for a CBR measurement for resources by a user equipment (UE). A CBR measurement value for the CBR window and for the resources is determined (701) by the UE. A channel occupancy ratio (CR) window is determined based on a first number of sub-frames used for a history of past transmissions and based on a second number of sub-frames used for future planned transmissions and corresponding retransmissions by the UE. A CR value for the CR window is determined based on sub-channels used for the resources for the first number of sub-frames and based on sub-channels estimated for the resources for the second number of sub-frames by the UE. INDEPENDENT CLAIMS are included for the following:(1) an apparatus configured for wireless communication for supporting enhanced congestion control for vehicle-to-everything (V2X) in new radio (NR); and(2) a non-transitory computer-readable medium storing program for supporting enhanced congestion control for vehicle-to-everything (V2X) in new radio (NR). Method for supporting enhanced congestion control for vehicle-to-everything (V2X) in new radio (NR) of wireless communication. The method increases reliability and throughput, reduces latency, and enables operation in ultra-reliable low latency communications (URLLC) modes. The base stations take advantage of the higher dimension multiple input, multiple output (MIMO) capabilities to exploit three-dimensional beam-forming in both elevation and azimuth beam-forming to increase coverage and capacity. The enhanced congestion control operations enables more aperiodic communications to be transmitted, and thus increases throughput and reduce latency. The drawing shows a block diagram of the blocks executed by a UE configured. 700Step for determining a CBR window for a CBR measurement for resources 701Step for determining a CBR measurement value for the CBR window and for the resources 702Step for determining a CR window based on the CBR measurement value | Please summarize the input |
Automated control of headlight illumination by onboard vehicle-to-everything (V2X) deviceIn an aspect, a method of wireless communication performed by a vehicle-to-everything (V2X) device onboard a vehicle includes receiving one or more V2X safety messages indicating a potential safety condition related to illumination of an object; determining, in response to the one or more V2X safety messages, that the object is within or approaching a target area in which illumination of the object by headlights of the vehicle can be adjusted; and controlling an illumination intensity and/or an illumination pattern of the headlights of the vehicle in response to determining that the object is within the target area.What is claimed is:
| 1. A method of wireless communication performed by a vehicle-to-everything (V2X) device onboard a vehicle, comprising:
receiving one or more V2X safety messages indicating a potential safety condition related to illumination of an object, wherein the one or more V2X safety messages indicate information relating to a location of the object;
determining, in response to the information relating to the location of the object indicated by the one or more V2X safety messages, that the object is within or approaching a target area in which illumination of the object by headlights of the vehicle can be adjusted;
determining one or more occluded regions of the target area based on topographical features identified by the V2X device;
determining whether the object is within or outside of the one or more occluded regions based on the information relating to the location of the object indicated by the one or more V2X safety messages; and
controlling an illumination intensity and/or an illumination pattern of the headlights of the vehicle in response to determining that the object is within the target area outside of the one or more occluded regions.
| 2. The method of claim 1, further comprising:
determining the target area based on a current illumination intensity and/or a current illumination pattern of the headlights of the vehicle.
| 3. The method of claim 1, further comprising:
determining the target area based on an illumination capability of the headlights of the vehicle.
| 4. The method of claim 1, further comprising:
determining the target area based on a current position of the vehicle and/or a projected future position of the vehicle using one or more vehicular sensors.
| 5. The method of claim 1, wherein:
the one or more occluded regions of the target area are determined based on topographical features identified by the V2X using sensor data received from one or more vehicular sensors.
| 6. The method of claim 5, wherein:
the one or more vehicular sensors include one or more light detection and ranging (LIDAR) sensors.
| 7. The method of claim 5, wherein:
the one or more vehicular sensors include one or more radio detection and ranging (RADAR) sensors.
| 8. The method of claim 5, wherein:
the one or more vehicular sensors include one or more image sensors.
| 9. The method of claim 1, further comprising:
detecting ambient lighting conditions exterior to the vehicle; and
determining the target area based on the ambient lighting conditions.
| 10. The method of claim 1, wherein controlling the illumination intensity and/or the illumination pattern of the headlights comprises: controlling illumination intensities of one or more of a plurality of light-emitting elements of the headlights.
| 11. The method of claim 10, wherein:
the illumination intensities of the one or more of the plurality of light-emitting elements of the headlights are adjusted to provide an illumination pattern that emits light at an increased intensity toward the object.
| 12. The method of claim 11, wherein:
the illumination pattern is asymmetric between a left-side headlight illumination intensity and a right-side headlight illumination intensity.
| 13. The method of claim 1, wherein: the potential safety condition is a condition in which the object should be illuminated to make the object visible to a driver of the vehicle; and the illumination intensity and/or the illumination pattern of the headlights are controlled to increase intensity of light beams emitted toward the object.
| 14. The method of claim 1, wherein: the potential safety condition is a condition in which illumination of the object should be limited to reduce a likelihood of blinding an individual at the object; and the illumination intensity and/or the illumination pattern of the headlights are controlled to decrease intensity of light beams emitted toward the object.
| 15. The method of claim 1, wherein: the potential safety condition is a condition in which the object should be illuminated to make the object visible to one or more image sensors used in an autonomous driving system of the vehicle; and the illumination intensity and/or the illumination pattern of the headlights are controlled to increase intensity of light beams emitted toward the object.
| 16. The method of claim 1, wherein: the potential safety condition is a condition in which illumination of the object should be limited to reduce a likelihood of overexposing one or more image sensors at the object; and the illumination intensity and/or the illumination pattern of the headlights are controlled to decrease intensity of light beams emitted toward the object.
| 17. The method of claim 1, wherein: the one or more V2X safety messages indicating the potential safety condition related to illumination of the object are received from a roadside unit (RSU).
| 18. The method of claim 1, wherein: determining that the object is within the target area comprises determining a current position of the object and/or a projected future position of the object based on the information relating to the location of the object indicated by the one or more V2X safety messages and/or further V2X communications.
| 19. The method of claim 18, wherein:
the current position of the object is based on the information relating to the location of the object indicated by the one or more V2X safety messages.
| 20. The method of claim 19, wherein the projected future position of the object is based on the current position of the object and:
a speed of the object indicated in V2X communications relating to the object,
a heading of the object indicated in V2X communications relating to the object,
a projected path of the object indicated in V2X communications relating to the object, or
any combination thereof.
| 21. The method of claim 1, wherein:
the one or more occluded regions of the target area are determined based on the topographical features identified by the V2X device from map data.
| 22. The method of claim 21, wherein:
the one or more occluded regions of the target area are determined based on road lanes used by the object as determined from the map data.
| 23. The method of claim 21, wherein:
the map data includes V2X map data.
| 24. The method of claim 21, wherein:
the map data includes local map data.
| 25. A vehicle-to-everything (V2X) device onboard a vehicle, comprising:
a memory;
at least one transceiver; and
at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to:
receive, via the at least one transceiver, one or more V2X safety messages indicating a potential safety condition related to illumination of an object, wherein the one or more V2X safety messages indicate information relating to a location of the object;
determine, in response to the information relating to the location of the object indicated by the one or more V2X safety messages, that the object is within or approaching a target area in which illumination of the object by headlights of the vehicle can be adjusted;
determine one or more occluded regions of the target area based on topographical features identified by the V2X device;
determining whether the object is within or outside of the one or more occluded regions based on the V2X safety information indicating the location of the object and control an illumination intensity and/or an illumination pattern of the headlights of the vehicle in response to determining that the object is within the target area.
| 26. The V2X device of claim 25, wherein the at least one processor is further configured to:
determine the target area based on a current illumination intensity and/or a current illumination pattern of the headlights of the vehicle.
| 27. The V2X device of claim 25, wherein the at least one processor is further configured to:
determine the target area based on an illumination capability of the headlights of the vehicle.
| 28. The V2X device of claim 25, wherein the at least one processor is further configured to:
determine the target area based on a current position of the vehicle and/or a projected future position of the vehicle using one or more vehicular sensors.
| 29. The V2X device of claim 25, wherein:
the one or more occluded regions of the target area are determined based on topographical features identified by the V2X using sensor data received from one or more vehicular sensors.
| 30. The V2X device of claim 29, wherein:
the one or more vehicular sensors include one or more light detection and ranging (LIDAR) sensors.
| 31. The V2X device of claim 29, wherein:
the one or more vehicular sensors include one or more radio detection and ranging (RADAR) sensors.
| 32. The V2X device of claim 29, wherein:
the one or more vehicular sensors include one or more image sensors.
| 33. The V2X device of claim 25, wherein the at least one processor configured to control the illumination intensity and/or the illumination pattern of the headlights comprises the at least one processor configured to:
control illumination intensities of one or more of a plurality of light-emitting elements of the headlights.
| 34. The V2X device of claim 33, wherein:
the illumination intensities of the one or more of the plurality of light-emitting elements of the headlights are controlled to provide an illumination pattern that emits light at an increased intensity toward the object.
| 35. The V2X device of claim 33, wherein:
the illumination pattern is asymmetric between a left-side headlight illumination intensity and a right-side headlight illumination intensity.
| 36. The V2X device of claim 25, wherein:
determining that the object is within the target area comprises determining a current position of the object and/or a projected future position of the object based on the information relating to the location of the object indicated by the one or more V2X safety messages and/or further V2X communications.
| 37. The V2X device of claim 36, wherein:
the current position of the object is based on the location information relating to the location of the object indicated by the one or more V2X safety messages.
| 38. The V2X device of claim 25, wherein:
the one or more occluded regions of the target area are determined based on the topographical features identified by the V2X device from map data. | The method involves receiving vehicle-to-everything (V2X) safety messages indicating a potential safety condition related to illumination of an object (702). Occluded regions of a target area are determined based on topographical features identified by a V2X device. Determination is made (704) to check whether the object is within or outside of the regions based on information relating to a location of the object indicated by the safety messages. An illumination intensity and/or an illumination pattern of headlights of the vehicle are controlled (706) in response to determining that the objects are within the target area. An INDEPENDENT CLAIM is included for a V2X device for onboarding a vehicle. Method for performing wireless communication by a V2X device for onboarding of a vehicle. The method enables utilizing a V2X safety module to determine that the object is within or approaching the target area in which illumination of the object by headlights of the vehicle can be adjusted in response to the V2X safety messages, and to control the illumination intensity and/or the illumination pattern of the headlights of the vehicle based on the determined object in an efficient manner. The drawing shows a flow chart illustrating a method for performing wireless communication by a V2X device for onboarding of a vehicle.702Receiving V2X safety messages indicating potential safety condition related to illumination of object 704Determining whether object is within or outside of regions based on information relating to location of object indicated by safety messages 706Controlling illumination intensity and/or illumination pattern of headlights of vehicle in response to determining that objects are within target area | Please summarize the input |
LOW LATENCY ENHANCEMENTS TO CV2X AUTONOMOUS RESOURCE SELECTION AND RE-SELECTION PROCEDURE FOR VEHICLE-TO-VEHICLE COMMUNICATIONSLow latency enhancements for communication systems, including autonomous driving and/or selection scenarios, are provided. A method for communication includes monitoring communication resources in a communication system, determining a set of candidate resources to use for subsequent transmission of information within a time window such that the time window is minimized based on a desired communication latency parameter that considers at least one or more of communication channel congestion and a priority of transmission, determining a set of lowest energy resources from the set of candidate resources, selecting a low energy resource from the set of lowest energy resources, and transmitting data on the selected low energy resource. Other aspects, embodiments, and features are also claimed and described. | The method involves monitoring communication resources (922-946) in a communication system. A set of candidate resources (910) to use for subsequent transmission of information within a time window such that the time window is minimized based on a desired communication latency parameter that considers communication channel congestion and a priority of the intended transmission. A set of lowest energy resources is determined from the set of candidate resources. A low energy resource is selected from the set of lowest energy resources. Data on the selected low energy resource is transmitted. INDEPENDENT CLAIMS are also included for the following:an apparatus for facilitating communication between user equipment'sa communication devicea non-transitory computer-readable medium comprising a set of instructions for facilitating communication between vehicles. Method for facilitating communication i.e. vehicle-to-vehicle communication, between user equipment's e.g. vehicles such as automobiles, for a wireless communications system. Can also be used for cellular phones, smart phones, session initiation protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players i.e. MPEG-1 audio layer 3 (MP3) players, cameras, game consoles, tablets, smart devices and wearable devices for Code Division Multiple Access (CDMA) , Time Division Multiple Access (TDMA) , Frequency-Division Multiple Access (FDMA) , OFDMA , Single-Carrier Frequency-Division Multiple Access (SC-FDMA) systems. The method enables transmitting data streams to the single user equipment so as to increase data rate, and transmitting data streams to the user equipment and other user equipment's to increase system capacity. The drawing shows a schematic view of a communication frame structure. 900Data structure901Trigger time for resource selection or reselection910Candidate resources922-946Communication resources | Please summarize the input |
Methods and apparatus for parking lot exit management using V2XAspects of the present disclosure include methods, apparatuses, and computer readable media for receiving a plurality of requests, from a plurality of user equipments (UEs), to exit a parking area comprising a plurality of vehicles, wherein each of the plurality of UEs is associated with a corresponding vehicle of the plurality of vehicles, determining an exit order for the plurality of vehicles to exit the parking area, and transmitting, to the plurality of UEs, a plurality of exit commands, based on the exit order, for the plurality of vehicles to exit the parking area.What is claimed is:
| 1. A method of wireless communication by a road side unit in a network, comprising:
receiving a plurality of requests, from a plurality of user equipments (UEs), to exit a parking area comprising a plurality of vehicles, wherein each of the plurality of UEs is associated with a corresponding vehicle of the plurality of vehicles;
determining an estimated exit duration to exit the parking area from a current location for an individual vehicle of the plurality of vehicles;
determining, based on the estimated exit duration, an exit order for the plurality of vehicles to exit the parking area;
transmitting, to the plurality of UEs, a plurality of exit commands, based on the exit order, for the plurality of vehicles to exit the parking area;
collecting, via one or more sensors, sensor information corresponding to activity within the parking area, the collecting comprising:
monitoring the plurality of vehicles exiting the parking area; and
detecting at least one of an out-of-order exit, a collision, a pedestrian, or other road user;
generating, based on the sensor information, one or more updated exit commands to supersede the plurality of exit commands; and
transmitting the one or more updated exit commands to at least a subset of the plurality of UEs in response to detecting the at least one of the out-of-order exit, the collision, the pedestrian, or the other road user.
| 2. The method of claim 1, wherein receiving the plurality of requests comprises:
receiving an emergency exit request from a first responder vehicle of the plurality of vehicles; and
wherein determining the exit order comprises prioritizing the first responder vehicle in the exit order for the plurality of vehicles.
| 3. The method of claim 1, wherein determining the exit order comprises:
determining the exit order based on one or more of a reception order associated with receiving the plurality of requests, proximities of the plurality of vehicles to one or more exits of the parking area, sizes of the plurality of vehicles, maneuverabilities of the plurality of vehicles, estimated durations for the plurality of vehicles to exit the parking area, estimated fuel consumptions of the plurality of vehicles, or priorities associated with the plurality of requests.
| 4. The method of claim 1, wherein transmitting the plurality of exit commands comprises:
sequentially transmitting each of the plurality of exit commands based on a corresponding scheduled exit time of a plurality of scheduled exit times in accordance with the exit order.
| 5. The method of claim 1, wherein transmitting the plurality of exit commands comprises:
transmitting a first exit command of the plurality of exit commands to a first vehicle of the plurality of vehicles scheduled to exit the parking area before remaining vehicles of the plurality of vehicles; and
transmitting, to the remaining vehicles, remaining exit commands of the plurality of exit commands each comprising identification information associated with a vehicle scheduled to exit the parking area immediately before each of the remaining vehicles.
| 6. The method of claim 5, wherein:
the identification information includes at least one of a make, a model, a color, a license plate, a build, a location, or a vehicle type.
| 7. The method of claim 1, wherein transmitting the plurality of exit commands comprises:
transmitting a plurality of scheduled exit times.
| 8. The method of claim 1, wherein:
the plurality of exit commands comprises location information associated with one or more exits of the parking area.
| 9. A road side unit, comprising:
a memory comprising instructions;
a transceiver; and
one or more processors operatively coupled with the memory and the transceiver, the one or more processors configured to execute the instructions in the memory to:
receive a plurality of requests, from a plurality of user equipments (UEs), to exit a parking area comprising a plurality of vehicles, wherein each of the plurality of UEs is associated with a corresponding vehicle of the plurality of vehicles;
determine an estimated exit duration to exit the parking area from a current location for an individual vehicle of the plurality of vehicles;
determine, based on the estimated exit duration, an exit order for the plurality of vehicles to exit the parking area;
transmit, to the plurality of UEs, a plurality of exit commands, based on the exit order, for the plurality of vehicles to exit the parking area;
collect, via one or more sensors, sensor information corresponding to activity within the parking area, wherein to collect, the one or more processors are configured:
monitor the plurality of vehicles exiting the parking area; and
detect at least one of an out-of-order exit, a collision, a pedestrian, or other road user;
generate, based on the sensor information, one or more updated exit commands to supersede the plurality of exit commands; and
transmit the one or more updated exit commands to at least a subset of the plurality of UEs in response to detecting the at least one of the out-of-order exit, the collision, the pedestrian, or the other road user.
| 10. The road side unit of claim 9, wherein receiving the plurality of requests comprises:
receive an emergency exit request from a first responder vehicle of the plurality of vehicles; and
wherein determining the exit order comprises prioritizing the first responder vehicle in the exit order for the plurality of vehicles.
| 11. The road side unit of claim 10, wherein determining the exit order comprises:
determine the exit order based on one or more of a reception order associated with receiving the plurality of requests, proximities of the plurality of vehicles to one or more exits of the parking area, sizes of the plurality of vehicles, maneuverabilities of the plurality of vehicles, estimated durations for the plurality of vehicles to exit the parking area, estimated fuel consumptions of the plurality of vehicles, or priorities associated with the plurality of requests.
| 12. The road side unit of claim 10, wherein transmitting the plurality of exit commands comprises:
sequentially transmit each of the plurality of exit commands based on a corresponding scheduled exit time of a plurality of scheduled exit times in accordance with the exit order.
| 13. The road side unit of claim 10, wherein transmitting the plurality of exit commands comprises:
transmit a first exit command of the plurality of exit commands to a first vehicle of the plurality of vehicles scheduled to exit the parking area before remaining vehicles of the plurality of vehicles; and
transmit to the remaining vehicles, remaining exit commands of the plurality of exit commands each comprising identification information associated with a vehicle scheduled to exit the parking area immediately before each of the remaining vehicles.
| 14. The road side unit of claim 13, wherein:
the identification information includes at least one of a make, a model, a color, a license plate, a build, a location, or a vehicle type.
| 15. The road side unit of claim 10, wherein transmitting the plurality of exit commands comprises:
transmit a plurality of scheduled exit times.
| 16. The road side unit of claim 10, wherein:
the plurality of exit commands comprises location information associated with one or more exits of the parking area.
| 17. A method of wireless communication by a user equipment (UE) associated with a vehicle in a network, comprising:
transmitting, to a road side unit (RSU), an exit request; and
receiving, from the RSU based on an estimated exit duration to exit a parking area from a current location, one or more exit commands comprising identification information associated with an other vehicle scheduled to exit the parking area immediately before the vehicle, and one or both of an indication for the vehicle to begin exiting the parking area or a scheduled exit time for the vehicle.
| 18. The method of claim 17, wherein transmitting the exit request comprises:
transmitting an emergency exit request from a first responder vehicle; and
wherein receiving the one or more exit commands comprises receiving a priority exit command to exit the parking area ahead of a plurality of vehicles.
| 19. The method of claim 17, wherein:
the identification information includes at least one of a make, a model, a color, a license plate, a build, an identifying mark, or an accessory associated with the other vehicle.
| 20. The method of claim 17, further comprising:
displaying via a graphical user interface, exit information based on one or more exit commands.
| 21. The method of claim 17, further comprising:
transmitting, to an autonomous drive system, exit information based on the one or more exit commands.
| 22. A user equipment (UE) associated with a vehicle, comprising:
a memory comprising instructions;
a transceiver; and
one or more processors operatively coupled with the memory and the transceiver, the one or more processors configured to execute instructions in the memory to:
transmit, to a road side unit (RSU), an exit request; and
receive, from the RSU based on an estimated exit duration to exit a parking area from a current location, one or more exit commands comprising identification information associated with an other vehicle scheduled to exit the parking area immediately before the vehicle, and one or both of an indication for the vehicle to begin exiting the parking area or a scheduled exit time scheduled for the vehicle.
| 23. The UE of claim 22, wherein transmitting the exit request comprises:
transmitting an emergency exit request from a first responder vehicle; and
wherein receiving the one or more exit commands comprises receiving a priority exit command to exit the parking area ahead of a plurality of vehicles.
| 24. The UE of claim 22, wherein:
the identification information includes at least one of a make, a model, a color, a license plate, a build, an identifying mark, or an accessory associated with the other vehicle.
| 25. The UE of claim 22, wherein the one or more processors are further configured to:
display, via a graphical user interface, exit information based on the one or more exit commands.
| 26. The UE of claim 22, wherein the one or more processors are further configured to:
transmit, to an autonomous drive system, exit information based on the one or more exit commands. | The method involves receiving multiple requests from multiple user equipments (UEs) to exit a parking area (S605), where the parking area comprises multiple vehicles, where each UE is associated with corresponding vehicle. An exit order is determined (S610) from multiple vehicles to exit the parking area, where the exit order comprises prioritizing the responder vehicles in the exit orders for multiple vehicles. Multiple exit commands are transmitted (S615) to multiple vehicles to exit the parking area based on the exit order and estimated exit duration. An emergency exit request is received from a responder vehicle. Identification information of the vehicle is obtained, where the identification information includes make, a model, a color, a number plate, build, a location or a vehicle type. Multiple vehicles exiting the parking area is monitored. Processors are operatively coupled with a memory and a transceiver. INDEPENDENT CLAIMS are included for:(a). a roadside unit;(b). a method for establishing wireless communication by using a user equipment;(c). a user equipment associated with a vehicle Method for facilitating parking lot exit management by using a vehicle-to-everything (V2X) network. The method enables establishing the vehicle-to-everything (V2X) network to manage traffic and reducing congestions. The method enables utilizing Roadside Unit (RSU) to transmit the exit commands to the User Equipment (UEs) to exit the parking area based on the exit order for the vehicles. The method enables performing clear channel assessment (CCA) to determine whether the channel is available or not. The drawing shows a flow diagram illustrating a method for performing parking lot exit management by using a vehicle-to-everything network.S605Step for receiving multiple requests from multiple user equipments to exit a parking area S610Step for determining exit order from multiple vehicles to exit the parking area S615Step for transmitting multiple exit commands to multiple vehicles to exit the parking area based on the exit order and estimated exit duration | Please summarize the input |
ENHANCING NAVIGATION EXPERIENCE USING V2X SUPPLEMENTAL INFORMATIONEmbodiments of the disclosure are directed to the use of supplemental information received from Vehicle-to-Everything (V2X) capable entities in order to enhance navigation and route selection based on available advanced driver assistance systems (ADAS) functionality. A number of potential routes are evaluated by retrieving the V2X capabilities and locations from V2X capable entities along those routes. That information is used to assess traffic density and availability of supplemental information used by ADAS along each route, allowing for an evaluation of each route on travel time and ADAS support. The driver can then select the best route that supports their needs.|1. A method comprising:
* obtaining (222) a destination address and a source address;
* determining (224) a plurality of routes from the source address to the destination address;
* for each route in the plurality of routes:
* determining (226) an availability of Vehicle to Everything, V2X,-capable entities, capable of providing V2X information, along one or more portions of each respective route;
* calculating a travel time estimate for each route in the plurality of routes based on the availability of V2X-capable entities along the one or more portions of each respective route;
* generating (228) a navigation map for display in an in-vehicle display, the navigation map comprising each route of the plurality of routes and an indication of the availability of V2X-capable entities along the one or more portions of each respective route; and
* causing (230) the navigation map to be displayed in the in-vehicle display;
* wherein the method further comprises:
* receiving a first user selection of a navigation route from the plurality of routes;
* determining, based on a user event having a duration, that the navigation route will not provide a sufficient level/density of V2X-capable entities for the duration of the user event;
* determining an alternative route, wherein the alternative route has a sufficient level/density of V2X-capable entities allowing for autonomous driving over the duration of the user event;
* causing the navigation map in the in-vehicle display to show an indication of the alternative route;
* receiving a second user selection of the alternative route;
* calculating a travel time estimate for the alternative route based on a greater availability of V2X-capable entities along one or more portions of the alternative route;
* determining an estimated change in travel time associated with the second user selection of the alternative route, the estimated change in travel time based at least on the calculated travel time estimate for the alternative route; and
* causing the in-vehicle display to show the estimated change in travel time.
| 2. The method of claim 1, further comprising:
* selecting a navigation route from the plurality of routes based on the availability of V2X-capable entities along the one or more portions of the navigation route; or
* receiving a third user selection of a navigation route from the plurality of routes; and
* updating the navigation map in the in-vehicle display to show only the user selected navigation route.
| 3. The method of claim 1, wherein calculating the travel time estimate for each route in the plurality of routes further comprises:
* determining V2X-capabilities of the V2X-capable entities along each respective route; and preferably
* the method further comprises:
* updating the travel time estimate for each route in the plurality of routes based on the V2X-capabilities of V2X-capable entities along each respective route; and further preferably
* the method further comprises:
* ordering each route in the plurality of routes in an ordered list based on the travel time estimate for each respective route; and
* causing the ordered list of the plurality of routes to be displayed in the in-vehicle display.
| 4. The method of claim 1, further comprising:
* for each route in the plurality of routes:
receiving a V2X-capability and a location for each of the plurality of V2X-capable entities along the one or more portions of each respective route; and preferably
* the method further comprises:
* for each route in the plurality of routes:
determining, from the V2X-capability and the location for each of the plurality of V2X-capable entities along the one or more portions of each respective route, an availability of assisted driving features along the one or more portions of each respective route; and further preferably
* the method further comprises:
updating the navigation map in the in-vehicle display to show availability of assisted driving features along the one or more portions of each route in the plurality of routes.
| 5. The method of claim 1, wherein the plurality of V2X-capable entities includes Vehicle-to-Vehicle, V2V, capable vehicles or Vehicle-to-Infrastructure, V2I, capable infrastructure.
| 6. A system comprising:
* a vehicle (100) having an in-vehicle display (756) and an on-board navigation computer (716), the on-board navigation computer capable of receiving communication over Vehicle-to-Everything, V2X, communication; and
* a navigation application executable by the on-board navigation computer to cause the on-board navigation computer to:
* obtain a destination address and a source address;
* determine a plurality of routes from the source address to the destination address;
* for each route in the plurality of routes:
* determine an availability of Vehicle to Everything, V2X,-capable entities, capable of providing V2X information, along one or more portions of each respective route;
* calculate a travel time estimate for each route in the plurality of routes based on the availability of V2X-capable entities along the one or more portions of each respective route;
* generate a navigation map for display in the in-vehicle display, the navigation map comprising each route of the plurality of routes and an indication of the availability of V2X-capable entities along the one or more portions of each respective route; and
* cause the navigation map to be displayed in the in-vehicle display;
* further causing the on-board navigation computer to:
* receive a first user selection of a navigation route from the plurality of routes;
* determine, based on a user event having a duration, that the navigation route will not provide a sufficient level/density of V2X-capable entities for the duration of the user event;
* determine an alternative route, wherein the alternative route has a sufficient level/density of V2X-capable entities allowing for autonomous driving over the duration of the user event;
* cause the navigation map in the in-vehicle display to show an indication of the alternative route;
* receive a second user selection of the alternative route;
* calculate a travel time estimate for the alternative route based on a greater availability of V2X-capable entities along one or more portions of the alternative route;
* determine an estimated change in travel time associated with the second user selection of the alternative route, the estimated change in travel time based at least on the calculated travel time estimate for the alternative route; and
* cause the in-vehicle display to show the estimated change in travel time.
| 7. The system of claim 6, wherein the navigation application is executable by the on-board navigation computer to further cause the on-board navigation computer to:
* select a navigation route from the plurality of routes based on the availability of V2X-capable entities along the one or more portions of the navigation route; or
* receive a third user selection of a navigation route from the plurality of routes; and
* update the navigation map in the in-vehicle display to show only the user selected navigation route.
| 8. The system of claim 6, wherein the navigation application is executable by the on-board navigation computer to further cause the on-board navigation computer to:
* determine V2X-capabilities of the V2X-capable entities along each respective route; and preferably
* the on-board navigation computer is further caused to:
* update the travel time estimate for each route in the plurality of routes based on V2X-capabilities of the V2X-capable entities along each respective route; and further preferably
* the on-board navigation computer is further caused to:
* order each route in the plurality of routes in an ordered list based on the travel time estimate for each respective route; and
* cause the ordered list of the plurality of routes to be displayed in the in-vehicle display.
| 9. The system of claim 6, wherein the navigation application is executable by the on-board navigation computer to further cause the on-board navigation computer to:
for each route in the plurality of routes:
receive a V2X-capability and a location for each of the plurality of V2X-capable entities along the one or more portions of each respective route; and preferably the on-board navigation computer is further caused to:
for each route in the plurality of routes:
determine, from the V2X-capability and the location for each of the plurality of V2X-capable entities along the one or more portions of each respective route, an availability of assisted driving features along the one or more portions of each respective route; and further preferably the on-board navigation computer is further caused to:
update the navigation map in the in-vehicle display to show availability of assisted driving features along the one or more portions of each route in the plurality of routes.
| 10. The system of claim 6, wherein the plurality of V2X-capable entities includes Vehicle-to-Vehicle, V2V, capable vehicles or Vehicle-to-Infrastructure, V2I, capable infrastructure.
| 11. A non-transitory computer readable memory containing instructions executable by a processor to cause the processor to:
* obtain a destination address and a source address;
* determine a plurality of routes from the source address to the destination address;
* for each route in the plurality of routes:
* determine an availability of Vehicle to Everything, V2X,-capable entities, capable of providing V2X information, along one or more portions of each respective route;
* calculating a travel time estimate for each route in the plurality of routes based on the availability of V2X-capable entities along the one or more portions of each respective route;
* generate a navigation map for display in the in-vehicle display, the navigation map comprising each route in the plurality of routes and an indication of the availability of V2X-capable entities along the one or more portions of each respective route; and
* cause the navigation map to be displayed in the in-vehicle display;
* and further causing the processor to:
* receive a first user selection of a navigation route from the plurality of routes;
* determine, based on a user event having a duration, that the navigation route will not provide a sufficient level/density of V2X-capable entities for the duration of the user event;
* determine an alternative route, wherein the alternative route has available a sufficient level/density of V2X-capable entities allowing for autonomous driving over the duration of the user event;
* cause the navigation map in the in-vehicle display to show an indication of the alternative route;
* receive a second user selection of the alternative route;
* calculating a travel time estimate for the alternative route based on a greater availability of V2X-capable entities along one or more portions of the alternative route;
* determine an estimated change in travel time associated with the second user selection of the alternative route, the estimated change in travel time based at least on the calculated travel time estimate for the alternative route; and
* cause the in-vehicle display to show the estimated change in travel time. | The method involves obtaining a destination address and a source address. Multiple routes are determined from the source address to the destination address. An availability of vehicle (100,104) to everything (V2X)-capable entities are determined capable of providing V2X information, along one or more portions of each respective route. A navigation map is generated for display in an in-vehicle display, the navigation map includes each route of multiple routes and an indication of the availability of V2X-capable entities along the one or more portions of each respective route. The navigation map is caused to be displayed in the in-vehicle display. INDEPENDENT CLAIMS are included for the following:a system for autonomous driving and advanced driver assistance systems;an apparatus for autonomous driving and advanced driver assistance systems; anda non-transitory computer readable for autonomous driving and advanced driver assistance systems. Method for autonomous driving and advanced driver assistance systems (ADAS). The supplemental information can also be used to generate suggestions to the driver and enable the driver to make better decisions. The drawing shows a schematic view of a V2X-capable entities. 100,104Vehicle102Infrastructure106Power grid110Pedestrian | Please summarize the input |
WIRELESS COMMUNICATION APPARATUS AND METHOD IN WIRELESS DEVICESThe present invention relates to a method and apparatus for wireless communication at wireless devices, in particular a method and apparatus for collaborative early detection and threat mitigation in C-V2X. In one aspect, the apparatus detects a threat entity in a threat area based on data signals received from the threat entity, wherein the threat entity interferes with wireless resources or spectrum used in autonomous driving and cooperative decisions. The transmitter transmits, to at least one second wireless device, a message indicating the threat entity in the threat zone. | The apparatus has a memory (360), a transceiver and a processor (359) communicatively connected to the memory and the transceiver. The processor is configured to detect a threat entity within a threat zone based on data signals received from the threat entity. The threat entity obstructs wireless spectrum or resources utilized in cooperative or automated driving decisions; and transmit to second wireless device and a message indicating the threat entity within the threat zone. The data signals received from the threat entity comprise data that is inconsistent with projected data for wireless devices. The data signals comprise data of a misbehaving wireless device. The data of the misbehaving wireless device comprises implausible data related to characteristic of the misbehaving wireless device. INDEPENDENT CLAIMS are included for the following:a method for wireless communication of first wireless device;a apparatus for wireless communication at second wireless device; anda method for wireless communication of second wireless device. Method for cooperative early threat detection and avoidance in cellular vehicle-to-everything (C-V2X). The method enables facilitating cooperative early threat detection and avoidance in cellular vehicle-to-everything (C-V2X) and/or D2D technology in an effective manner. The drawing shows a schematic view of a first device and a second device. 310Wireless communication device359Processor360Memory370Receive processor374Channel estimator | Please summarize the input |
Methods and systems for managing interactions between vehicles with varying levels of autonomyMethods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Adjusting a driving parameter may include adjusting one or more of a minimum separation distance, a minimum following distance, a speed parameter, or an acceleration rate parameter.What is claimed is:
| 1. A method of controlling an autonomous vehicle, comprising:
determining dynamically, via a processor of the autonomous vehicle, a threshold distance appropriate for current conditions;
identifying, via the processor of the autonomous vehicle, vehicles that are within the dynamically determined threshold distance of the autonomous vehicle;
determining an autonomous capability metric of each of the identified vehicles; and
adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles.
| 2. The method of claim 1, wherein determining the autonomous capability metric of each of the identified vehicles comprises determining a level of autonomy of each identified vehicle.
| 3. The method of claim 1, wherein adjusting the driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each identified vehicle comprises:
adjusting a minimum separation distance to be maintained between the autonomous vehicle and at least one vehicle of the identified vehicles.
| 4. The method of claim 3, wherein adjusting the minimum separation distance to be maintained between the autonomous vehicle and the at least one vehicle of the identified vehicles comprises adjusting the minimum separation distance based on the autonomous capability metric of the least one vehicle and a behavior model of the at least one vehicle.
| 5. The method of claim 1, wherein adjusting the driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each identified vehicle comprises:
adjusting a minimum following distance to be maintained between the autonomous vehicle and at least one vehicle of the identified vehicles.
| 6. The method of claim 5, wherein adjusting the minimum following distance to be maintained between the autonomous vehicle and the at least one vehicle of the identified vehicles comprises adjusting the minimum following distance based on the autonomous capability metric of the least one vehicle and a behavior model of the at least one vehicle.
| 7. The method of claim 1, wherein adjusting the driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles comprises one or more of:
adjusting a speed of the autonomous vehicle; or
adjusting an acceleration rate at which the autonomous vehicle will change speed.
| 8. The method of claim 7, wherein adjusting the speed of the autonomous vehicle or the acceleration rate at which the autonomous vehicle will change speed comprises adjusting the speed or the acceleration rate based on the autonomous capability metric of at least one vehicle of the identified vehicles and a behavior model of the at least one vehicle.
| 9. The method of claim 1, wherein determining the autonomous capability metric of each of the identified vehicles comprises receiving the autonomous capability metric from at least one vehicle of the identified vehicles.
| 10. The method of claim 1, wherein determining the autonomous capability metric of each of the identified vehicles comprises determining values that collectively identify or predict a level of autonomy or a performance capability of a nearby vehicle.
| 11. The method of claim 10, wherein determining the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle comprises determining the values by one or more of:
observing driving behavior of the nearby vehicle;
determining computing or sensor capability of the nearby vehicle; or
receiving information regarding the nearby vehicle's rating or certifications via C-V2X communications.
| 12. The method of claim 11, further comprising determining at least one of the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on the observed driving behavior, the determined at least one value representing one or more of:
a consistency, regularity or uniformity of vehicle operations;
a level of predictability for future vehicle operations;
a level of driver aggression;
a degree to which the nearby vehicle tracks a center of a driving lane;
number of driving errors per unit time;
compliance with local road rules;
compliance with safety rules;
reaction time of the autonomous vehicle; or
responsiveness of the autonomous vehicle to observable events.
| 13. The method of claim 10, further comprising determining at least one of the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on the determined sensor capability, the determined at least one value representing one of:
a sensor type;
a sensor make or model;
a sensor manufacturer;
number of autonomous driving sensors operating in the nearby vehicle;
sensor accuracy; or
precision of one or more sensors.
| 14. The method of claim 10, further comprising determining one or more of the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on information received via C-V2X communications, the one or more values representing one or more of:
a key performance indicator (KPI);
a surface performance rating;
a weather performance rating;
a vehicle capability;
a vehicle feature;
a supported algorithm; or
a prediction and control strategy.
| 15. A processor for a vehicle, wherein the processor is configured with processor executable instructions to:
determine dynamically a threshold distance appropriate for current conditions;
identify vehicles that are within the dynamically determined threshold distance of the vehicle;
determine an autonomous capability metric of each of the identified vehicles; and
adjust a driving parameter based on the determined autonomous capability metric of each of the identified vehicles.
| 16. The processor of claim 15, wherein the processor is further configured with processor executable instructions to determine the autonomous capability metric of each of the identified vehicles by determining a level of autonomy of each identified vehicle.
| 17. The processor of claim 15, wherein the processor is further configured with processor executable instructions to adjust the driving parameter of the vehicle based on the determined autonomous capability metric of each identified vehicle by adjusting at least one of:
a minimum separation distance to be maintained between the vehicle and at least one vehicle of the identified vehicles;
a minimum following distance to be maintained between the vehicle and the at least one vehicle of the identified vehicles;
a speed of the vehicle; or
an acceleration rate at which the vehicle will change speed.
| 18. The processor of claim 17, wherein the processor is further configured with processor executable instructions to:
adjust the minimum separation distance based on the autonomous capability metric of the least one vehicle and a behavior model of the at least one vehicle;
adjust the minimum following distance based on the autonomous capability metric of the least one vehicle and the behavior model of the at least one vehicle;
adjust the speed based on the autonomous capability metric of the at least one vehicle of the identified vehicles and the behavior model of the at least one vehicle; or
adjust the acceleration rate based on the autonomous capability metric of the at least one vehicle of the identified vehicles and the behavior model of the at least one vehicle.
| 19. The processor of claim 15, wherein the processor is further configured with processor executable instructions to determine the autonomous capability metric of each of the identified vehicles by receiving the autonomous capability metric from at least one vehicle of the identified vehicles.
| 20. The processor of claim 15, wherein the processor is further configured with processor executable instructions to determine the autonomous capability metric of each of the identified vehicles by determining values that collectively identify or predict a level of autonomy or a performance capability of a nearby vehicle.
| 21. The processor of claim 20, wherein the processor is further configured with processor executable instructions to determine the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle by determining the values by one or more of:
observing driving behavior of the nearby vehicle;
determining computing or sensor capability of the nearby vehicle; or
receiving information regarding the nearby vehicle's rating or certifications via C-V2X communications.
| 22. The processor of claim 21, wherein the processor is further configured with processor executable instructions to determine the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on the observed driving behavior by determining a value representing one or more of:
a consistency, regularity or uniformity of vehicle operations;
a level of predictability for future vehicle operations;
a level of driver aggression;
a degree to which the nearby vehicle tracks a center of a driving lane;
number of driving errors per unit time;
compliance with local road rules;
compliance with safety rules;
reaction time of the vehicle; or
responsiveness of the vehicle to observable events.
| 23. The processor of claim 21, wherein the processor is further configured with processor executable instructions to determine the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on the determined sensor capability by determining a value representing one or more of:
a sensor type;
a sensor make or model;
a sensor manufacturer;
number of autonomous driving sensors operating in the nearby vehicle;
sensor accuracy; or
precision of one or more sensors.
| 24. The processor of claim 21, wherein the processor is further configured with processor executable instructions to determine the values that collectively identify or predict the level of autonomy or the performance capability of the nearby vehicle based on information received via C-V2X communications by determining a value representing one or more of:
a key performance indicator (KPI);
a surface performance rating;
a weather performance rating;
a vehicle capability;
a vehicle feature;
a supported algorithm; or
a prediction and control strategy.
| 25. A non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of an autonomous vehicle to perform operations comprising:
determining dynamically a threshold distance appropriate for current conditions;
identifying vehicles that are within the dynamically determined threshold distance of the autonomous vehicle;
determining an autonomous capability metric of each of the identified vehicles; and
adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles.
| 26. A vehicle, comprising:
means for determining dynamically a threshold distance appropriate for current conditions;
means for identifying vehicles that are within the dynamically determined threshold distance of the vehicle;
means for determining an autonomous capability metric of each of the identified vehicles; and
means for adjusting a driving parameter of the vehicle based on the determined autonomous capability metric of each of the identified vehicles.
| 27. The vehicle of claim 26, wherein means for determining an autonomous capability metric of each of the identified vehicles comprises means for determining values that collectively identify or predict a level of autonomy or performance capability of a nearby vehicle based on one or more of:
observing driving behavior of the nearby vehicle;
determining computing or sensor capability of the nearby vehicle; or
receiving information regarding the nearby vehicle's rating or certifications via C-V2X communications.
| 28. The vehicle of claim 26, wherein means for determining an autonomous capability metric of each of the identified vehicles comprises means for determining one or more values that collectively identify or predict a level of autonomy or a performance capability of a nearby vehicle based on information received via C-V2X communications, the one or more values representing one or more of:
a key performance indicator (KPI);
a surface performance rating;
a weather performance rating;
a vehicle capability;
a vehicle feature;
a supported algorithm; or
a prediction and control strategy. | The method involves identifying (902) the vehicles that are within a threshold distance of an autonomous vehicle through a processor of the autonomous vehicle. An autonomous capability metric of each of the identified vehicles is determined (1104) in which the determining comprises a determining of a level of autonomy of each identified vehicle. A driving parameter of the autonomous vehicle is adjusted based on the determined autonomous capability metric of the identified vehicles. INDEPENDENT CLAIMS are included for the following:a processor;a non-transitory processor-readable storage medium storing program for controlling autonomous vehicle; anda vehicle. Method for controlling autonomous vehicle (claimed). The sensors enable the autonomous vehicle to operate safely with improved performance. The drawing shows a flow diagram illustrating method for adjusting behavior and operations of an autonomous vehicle based on the determined capabilities of the other surrounding vehicle. 902Step for identifying the vehicles that are within a threshold distance of an autonomous vehicle1012Step for controlling behavior of operation of vehicle1104Step for determining autonomous capability metric of each of the identified vehicles1108Step for adjusting driving parameter of the autonomous vehicle to be more trusting1114Step for adjusting driving parameter of the autonomous vehicle to be less trusting | Please summarize the input |
TRANSMISSION CONTROL IN APPLICATION LAYER BASED ON RADIO BEARER QUALITY METRICS IN VEHICULAR COMMUNICATIONMethods, apparatuses, and computer-readable mediums for wireless communication are disclosed by the present disclosure. In an aspect, receive, an application layer in a host user equipment (UE) receives, from an access layer in the host UE, a quality of service (QoS) indication comprising a metric that represents a quality of one or more radio bearers used for a vehicular communication with one or more other UEs. The application layer performs a transmission control over the vehicular communication based on the QoS indication.|1. A?method?of?wireless?communication,?comprising:
receiving,?by?an?application?layer?in?a?host?user?equipment?(UE)?,?from?an?access?layer?in?the?host?UE,?a?quality?of?service?(QoS)?indication?comprising?a?metric?that?represents?a?quality?of?one?or?more?radio?bearers?used?for?a?vehicular?communication?with?one?or?more?other?UEs;?and
performing,?at?the?application?layer,?a?transmission?control?over?the?vehicular?communication?based?on?the?QoS?indication.
| 2. The?method?of?claim?1,?wherein?the?metric?is?indicative?of?a?message?reception?performance?as?affected?by?a?presence?or?an?absence?of?message?interference?or?collision?in?the?one?or?more?radio?bearers.
| 3. The?method?of?claim?1,?wherein?the?performing?comprises?one?or?more?of?adjusting?a?transmission?rate?of?a?unicast?communication?of?the?host?UE,?a?transmission?range?of?a?groupcast?communication?of?the?host?UE,?a?maneuver?of?the?host?UE,?or?an?autonomous?driving?status?of?the?host?UE,?according?to?the?QoS?indication.
| 4. The?method?of?claim?1,?further?comprising?sharing?sensor?data?of?the?host?UE?with?a?remote?UE?via?a?unicast?communication?at?a?first?transmission?rate.
| 5. The?method?of?claim?4,?wherein?the?performing?comprises:
determining,?by?the?application?layer,?based?on?the?QoS?indication,?a?second?transmission?rate?supportable?by?the?unicast?communication;?and
adjusting?the?unicast?communication?according?to?the?second?transmission?rate.
| 6. The?method?of?claim?5,?wherein?the?adjusting?comprises?performing?inter-transmission?time?(ITT)?control?at?the?host?UE.
| 7. The?method?of?claim?5,
wherein?the?sharing?comprises?sharing?video?sensor?data?of?the?host?UE?with?the?remote?UE?over?the?unicast?communication;?and
wherein?the?adjusting?comprises?adjusting?a?video?resolution?of?a?video?codec?of?the?host?UE?according?to?the?second?transmission?rate?supportable?by?the?unicast?communication.
| 8. The?method?of?claim?4,?wherein?the?receiving?comprises?receiving?at?least?one?of?a?packet?error?rate?(PER)?or?a?negative?acknowledgement?(NACK)?statistic?related?to?the?unicast?communication?with?the?remote?UE.
| 9. The?method?of?claim?1,?further?comprising:
communicating,?by?the?host?UE,?with?a?plurality?of?other?UEs?via?a?groupcast?communication;?and
wherein?the?receiving?comprises?receiving?at?least?one?of?a?packet?error?rate?(PER)?or?a?negative?acknowledgement?(NACK)?statistic?related?to?the?groupcast?communication?with?the?plurality?of?other?UEs.
| 10. The?method?of?claim?9,?wherein?the?performing?comprises:
determining,?based?on?the?at?least?one?of?the?PER?or?the?NACK?statistic,?that?a?reachable?range?of?the?host?UE?fails?to?comply?with?a?minimum?range?requirement?of?a?vehicular?application?configured?for?controlling?a?maneuver?of?the?host?UE.
| 11. The?method?of?claim?10,?wherein?the?performing?further?comprises?cancelling?or?postponing?the?maneuver?of?the?host?UE.
| 12. The?method?of?claim?10,?wherein?the?performing?further?comprises?regenerating?a?driving?strategy?of?the?host?UE?to?match?the?reachable?range.
| 13. The?method?of?claim?10,?wherein?the?performing?further?comprises:
modifying?a?range?of?the?host?UE?according?to?the?reachable?range;?and
adjusting?the?maneuver?of?the?host?UE?based?on?the?range.
| 14. The?method?of?claim?13,?wherein?the?modifying?comprises?adjusting?a?radiated?power?of?the?host?UE.
| 15. The?method?of?claim?13,?wherein?adjusting?the?maneuver?comprises?slowing?down?the?host?UE,?following?a?stop?and?go?operation?at?the?host?UE,?or?exiting?an?autonomous?driving?mode?at?the?host?UE.
| 16. The?method?of?claim?10,?wherein?the?maneuver?comprises?a?coordinated?intersection?crossing.
| 17. The?method?of?claim?1,?wherein?the?QoS?indication?comprises?one?or?more?of?a?Packet?Error?Rate?(PER)?,?a?Packet?Received?Rate?(PRR)?,?an?average?number?of?retransmissions,?an?average?PER,?an?average?PRR,?an?acknowledgement?(ACK)?/negative?acknowledgement?(NACK)?statistic,?a?range?statistic?of?a?groupcast?group,?or?a?supported?bit?rate?for?a?radio?bearer.
| 18. The?method?of?claim?1,?wherein?the?vehicular?communication?comprises?a?new?radio?(NR)?vehicle-to-everything?(V2X)?communication.
| 19. A?non-transitory?computer-readable?medium?storing?instructions?that?when?executed?by?a?processor?cause?the?processor?to:
receive,?by?an?application?layer?in?a?host?user?equipment?(UE)?,?from?an?access?layer?in?the?host?UE,?a?quality?of?service?(QoS)?indication?comprising?a?metric?that?represents?a?quality?of?one?or?more?radio?bearers?used?for?a?vehicular?communication?with?one?or?more?other?UEs;?and
perform,?at?the?application?layer,?a?transmission?control?over?the?vehicular?communication?based?on?the?QoS?indication.
| 20. A?host?user?equipment?(UE)?for?wireless?communication,?comprising:
a?memory?storing?instructions;?and
a?processor?in?communication?with?the?memory,?wherein?the?processor?is?configured?to?execute?the?instructions?to:
receive,?by?an?application?layer?in?the?host?UE,?from?an?access?layer?in?the?host?UE,?a?quality?of?service?(QoS)?indication?comprising?a?metric?that?represents?a?quality?of?one?or?more?radio?bearers?used?for?a?vehicular?communication?with?one?or?more?other?UEs;?and
perform,?at?the?application?layer,?a?transmission?control?over?the?vehicular?communication?based?on?the?QoS?indication.
| 21. A?host?user?equipment?(UE)?for?wireless?communication,?comprising:
means?for?receiving,?by?an?application?layer?in?the?host?UE,?from?an?access?layer?in?the?host?UE,?a?quality?of?service?(QoS)?indication?comprising?a?metric?that?represents?a?quality?of?one?or?more?radio?bearers?used?for?a?vehicular?communication?with?one?or?more?other?UEs;?and
means?for?performing,?at?the?application?layer,?a?transmission?control?over?the?vehicular?communication?based?on?the?QoS?indication. | The method involves receiving, by an application layer (142) in a host user equipment (UE), from an access layer (146) in the host UE, a quality of service (QoS) indication including a metric that represents a quality of multiple radio bearers used for a vehicular communication with the other UEs. The transmission control over the vehicular communication is performed at the application layer based on the QoS indication. The inter-transmission time control is performed at the host UE. The range of the host UE is modified according to the reachable range. INDEPENDENT CLAIMS are included for the following:a non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to perform an operation for transmission control in application layer based on radio bearer quality metrics in vehicular communication, such as new radio vehicle-to-everything communication with vehicular communication system; anda host user equipment for wireless communication. Method for performing transmission control in application layer based on radio bearer quality metrics in vehicular communication, such as new radio vehicle-to-everything communication with vehicular communication system. Can also be used to provide various telecommunication services, such as telephony, video, data, messaging, and broadcasts. The quality of service indications may be used by the application layer to adapt the range for groupcast, thus allowing the application layer to adjust autonomous driving behavior. The drawing shows a schematic view of a wireless communications system and an access network. 100Wireless communications system110Coverage area132,134Backhaul links142Application layer146Access layer | Please summarize the input |
APPLICATION LAYER MESSAGES FOR LANE DESCRIPTION IN VEHICULAR COMMUNICATIONMethods, apparatuses, and computer-readable mediums for wireless communication are disclosed by the present disclosure. In an aspect, an application layer of a protocol layer stack of vehicular user equipment (UE) receives a vehicular communication message including an application layer data element that directly indicates a curvature or a slope of a lane in a road. The vehicular UE may then implement autonomous driving functionality based on the application layer data element. In another aspect, an application layer of a protocol layer stack of a device generates a vehicular communication message including an application layer data element that directly indicates a curvature or a slope of a lane in a road. The device may then transmit the vehicular communication message to a vehicular UE configured to implement autonomous driving functionality.|1. A?method?of?wireless?communication?at?a?vehicular?user?equipment?(UE)?,?comprising:
receiving,?by?an?application?layer?of?a?protocol?layer?stack?of?the?vehicular?UE,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
implementing?autonomous?driving?functionality?based?on?the?application?layer?data?element.
| 2. The?method?of?claim?1,?wherein?the?receiving?comprises?receiving?the?vehicular?communication?message?from?another?vehicular?UE,?a?network,?an?infrastructure,?a?road?side?unit?(RSU)?,?or?a?relay.
| 3. The?method?of?claim?1,?wherein?the?vehicular?communication?message?further?includes?one?or?more?application?layer?data?elements?that?indicate?a?lane?width?of?the?lane.
| 4. The?method?of?claim?1,?wherein?the?vehicular?communication?message?further?includes?one?or?more?application?layer?data?elements?that?indicate?a?longitude?value,?a?latitude?value,?and?an?elevation?value?for?each?point?in?a?list?of?spaced?points?positioning?a?center?line?of?the?lane.
| 5. The?method?of?claim?4,?wherein?a?spacing?between?two?consecutive?points?in?the?list?of?spaced?points?is?a?function?of?the?curvature?of?the?road.
| 6. The?method?of?claim?4,?wherein?at?least?one?of?the?one?or?more?application?layer?data?elements?indicates?a?differential?value?of?a?position,?curvature,?or?slope?of?a?point?in?the?list?of?spaced?points?as?compared?to?a?neighboring?point?in?the?list?of?spaced?points.
| 7. The?method?of?claim?4,?wherein?at?least?one?of?the?one?or?more?application?layer?data?elements?indicates?a?differential?value?of?a?position,?curvature,?or?slope?of?a?point?in?the?list?of?spaced?points?as?compared?to?a?corresponding?previous?value?of?the?position,?curvature,?or?slope?of?the?point?in?the?list?of?spaced?points.
| 8. The?method?of?claim?4,?wherein?the?one?or?more?application?layer?data?elements?indicate?a?plurality?of?curvatures?or?slopes,?each?associated?with?at?least?one?point?in?the?list?of?spaced?points.
| 9. The?method?of?claim?1,?wherein?implementing?the?autonomous?driving?functionality?comprises?controlling?a?motion?of?the?vehicular?UE?on?the?road.
| 10. The?method?of?claim?1,?wherein?implementing?the?autonomous?driving?functionality?comprises?implementing?according?to?an?advanced?driver-assistance?system?(ADAS)?.
| 11. The?method?of?claim?1,?wherein?implementing?the?autonomous?driving?functionality?comprises?controlling?a?speed?or?an?acceleration?of?the?vehicular?UE.
| 12. The?method?of?claim?1,?wherein?implementing?the?autonomous?driving?functionality?comprises?adjusting?a?detection?range?of?a?sensor?used?in?an?advanced?driver-assistance?system?(ADAS)?.
| 13. The?method?of?claim?12,?wherein?the?sensor?comprises?a?camera,?a?radar,?or?a?light?detection?and?ranging?(LIDAR)?sensor.
| 14. The?method?of?claim?12,?wherein?adjusting?the?detection?range?of?the?sensor?comprises?adjusting?a?position?or?an?angle?of?the?sensor?based?on?the?curvature?of?the?lane?in?the?road.
| 15. The?method?of?claim?12,?wherein?adjusting?the?detection?range?of?the?sensor?comprises?adjusting?a?yaw?angle?of?the?sensor?toward?the?curvature?of?the?lane?in?the?road.
| 16. The?method?of?claim?12,?wherein?the?slope?comprises?a?longitudinal?slope,?wherein?adjusting?the?detection?range?of?the?sensor?comprises?adjusting?a?pitch?angle?of?the?sensor?toward?the?longitudinal?slope?of?the?lane?in?the?road.
| 17. The?method?of?claim?1,?wherein?implementing?the?autonomous?driving?functionality?comprises?determining?a?speed?or?acceleration?limitation?based?on?the?curvature?or?the?slope?of?the?lane?in?the?road.
| 18. The?method?of?claim?17,?wherein?implementing?the?autonomous?driving?functionality?further?comprises?managing?a?safe?turning?of?the?vehicular?UE?by?decelerating?to?an?allowed?maximum?speed.
| 19. The?method?of?claim?17,?wherein?implementing?the?autonomous?driving?functionality?comprises?determining?the?speed?or?acceleration?limitation?based?on?a?sharpness?level?of?the?curvature?of?the?lane?in?the?road.
| 20. The?method?of?claim?1,?wherein?the?slope?comprises?a?longitudinal?slope,?wherein?implementing?the?autonomous?driving?functionality?comprises?determining?an?efficient?acceleration?value?based?on?the?longitudinal?slope?to?manage?an?uphill?motion?of?the?vehicular?UE.
| 21. The?method?of?claim?1,?wherein?the?slope?comprises?a?longitudinal?slope?or?a?transverse?slope?or?both.
| 22. The?method?of?claim?1,?wherein?the?vehicular?communication?message? comprises?a?vehicle-to-everything?(V2X)?message.
| 23. A?non-transitory?computer-readable?medium?storing?instructions?that?when?executed?by?a?processor,?cause?the?processor?to:
receive,?by?an?application?layer?of?a?protocol?layer?stack?of?a?vehicular?user?equipment?(UE)?,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
implement?autonomous?driving?functionality?based?on?the?application?layer?data?element.
| 24. The?non-transitory?computer-readable?medium?of?claim?23,?wherein?the?processor?is?further?configured?to?perform?any?of?methods?2-22.
| 25. A?vehicular?user?equipment?(UE)?for?wireless?communication,?comprising:
a?memory?storing?instructions;?and
a?processor?in?communication?with?the?memory,?wherein?the?processor?is?configured?to?execute?the?instructions?to:
receive,?by?an?application?layer?of?a?protocol?layer?stack?of?the?vehicular?UE,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
implement?autonomous?driving?functionality?based?on?the?application?layer?data?element.
| 26. The?vehicular?UE?of?claim?25,?wherein?the?processor?is?further?configured?to?perform?any?of?methods?2-22.
| 27. A?vehicular?user?equipment?(UE)?for?wireless?communication,?comprising:
means?for?receiving,?by?an?application?layer?of?a?protocol?layer?stack?of?the? vehicular?UE,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
means?for?implementing?autonomous?driving?functionality?based?on?the?application?layer?data?element.
| 28. The?vehicular?UE?of?claim?27,?further?comprising?means?for?performing?any?of?methods?2-22.
| 29. A?method?of?wireless?communication,?comprising:
generating,?by?an?application?layer?of?a?protocol?layer?stack?of?a?device,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
transmitting?the?vehicular?communication?message?to?a?vehicular?user?equipment?(UE)?configured?to?implement?autonomous?driving?functionality.
| 30. A?non-transitory?computer-readable?medium?storing?instructions?that?when?executed?by?a?processor,?cause?the?processor?to:
generate,?by?an?application?layer?of?a?protocol?layer?stack?of?a?device,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
transmit?the?vehicular?communication?message?to?a?vehicular?user?equipment?(UE)?configured?to?implement?autonomous?driving?functionality.
| 31. A?device,?comprising:
a?memory?storing?instructions;?and
a?processor?in?communication?with?the?memory,?wherein?the?processor?is?configured?to?execute?the?instructions?to:
generate,?by?an?application?layer?of?a?protocol?layer?stack?of?a?device,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
transmit?the?vehicular?communication?message?to?a?vehicular?user?equipment?(UE)?configured?to?implement?autonomous?driving?functionality.
| 32. A?device,?comprising:
means?for?generating,?by?an?application?layer?of?a?protocol?layer?stack?of?a?device,?a?vehicular?communication?message?including?an?application?layer?data?element?that?directly?indicates?a?curvature?or?a?slope?of?a?lane?in?a?road;?and
means?for?transmitting?the?vehicular?communication?message?to?a?vehicular?user?equipment?(UE)?configured?to?implement?autonomous?driving?functionality. | The method (900) involves receiving (902) a vehicular communication message including an application layer data element that directly indicates a curvature or a slope of a lane in a road by an application layer of a protocol layer stack of the vehicular UE. The autonomous driving functionality is implemented (904) based on the application layer data element. The vehicular communication message is received from another vehicular UE, a network, an infrastructure, a road side unit (RSU), or a relay. The vehicular communication message is provided with several application layer data elements that indicate a lane width of the lane. The vehicular communication message is provided with application layer data elements that indicate a longitude value, a latitude value, and an elevation value for each point in a list of spaced points positioning a center line of the lane. INDEPENDENT CLAIMS are included for the following:a non-transitory computer-readable medium storing program for wireless communication;a vehicular user equipment for wire1ess communication; anda device for wire1ess communication. Method for wireless communication at vehicular UE referred to as internet of things (IoT) devices such as parking meter, gas pump, toaster, vehicles, and heart monitor. The capacity of the access network is improved. The accuracy of estimations depend on the density of points is improved. The system allows for improved driving assistance such as speed and acceleration control. The drawing shows a flowchart illustrating the method for wireless communication at vehicular UE. 900Method for wireless communication at vehicular UE902Step for receiving vehicular communication message including application layer data element904Step for implementing autonomous driving functionality based on application layer data | Please summarize the input |
ENFORCING RANGE RELIABILITY FOR INFORMATION SHARED VIA WIRELESS TRANSMISSIONSAn ego vehicle determines an intended maneuver and identifies a first set of agents for coordinating the intended maneuver. The ego vehicle also determines a spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold. The ego vehicle further applies the determined spatial distance to a sensor-sharing message. The ego vehicle also transmits the sensor-sharing message to a second set of agents within the determined range. The ego vehicle performs the intended maneuver.What is claimed is:
| 1. A method performed by an ego vehicle, comprising:
determining an intended maneuver of the ego vehicle;
identifying a first set of agents for coordinating the intended maneuver;
determining a spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold;
applying the determined spatial distance to a sensor-sharing message;
transmitting the sensor-sharing message to a second set of agents within the determined spatial distance; and
performing the intended maneuver.
| 2. The method of claim 1, further comprising transmitting the sensor-sharing message via at least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I) transmission, or a combination thereof.
| 3. The method of claim 1, in which agents in the first set of agents and the second set of agents comprise at least one of a vehicle, an infrastructure component, a road side unit, a non-vehicular road user, or a combination thereof.
| 4. The method of claim 3, further comprising receiving communications from an embedded vehicle-to-everything (V2X) device of the non-vehicular road user or a hand-held V2X device of the non-vehicular road user.
| 5. The method of claim 1, further comprising determining the range based on at least one of the intended maneuver, a speed of the ego vehicle, a number of agents detected within a distance of the ego vehicle, a speed of at least one other agent, a direction of travel of at least one other agent, a road condition, a visibility level, a type of road, a quality of service (QoS), an automation level of the ego vehicle, a direction of travel of the ego vehicle, or a combination thereof.
| 6. The method of claim 5, in which the distance is based on at least the intended maneuver, the road condition, the type of road, the speed of the ego vehicle or a combination thereof.
| 7. The method of claim 5, in which:
the ego vehicle is capable of performing a plurality of maneuvers, and
each maneuver corresponds to a different range.
| 8. The method of claim 1, further comprising coordinating the intended maneuver with each agent of the first set of agents within the determined spatial distance.
| 9. The method of claim 8, further comprising coordinating the intended maneuver via least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I), or a combination thereof.
| 10. The method of claim 1, in which the sensor sharing message identifies objects detected within a distance of the ego vehicle via at least one sensor integrated with the ego vehicle.
| 11. The method of claim 10, in which the objects comprise at least one of non-V2X capable vehicles, non-vehicular road users, infrastructure, road obstacles, road impairments, or a combination thereof.
| 12. The method of claim 1, further comprising:
determining the range at an application-layer; and
enforcing the range at a physical-layer.
| 13. The method of claim 1, in which the ego vehicle comprises an autonomous vehicle or a semi-autonomous vehicle.
| 14. An apparatus of an ego vehicle, comprising:
means for determining an intended maneuver of the ego vehicle;
means for identifying a first set of agents for coordinating the intended maneuver;
means for determining a spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold;
means for applying the determined spatial distance to a sensor-sharing message;
means for transmitting the sensor-sharing message to a second set of agents within the determined spatial distance; and
means for performing the intended maneuver.
| 15. The apparatus of claim 14, further comprising means for transmitting the sensor-sharing message via at least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I) transmission, or a combination thereof.
| 16. The apparatus of claim 14, in which agents in the first set of agents and the second set of agents comprise at least one of a vehicle, an infrastructure component, a road side unit, a non-vehicular road user, or a combination thereof.
| 17. The apparatus of claim 16, further comprising means for receiving communications from an embedded vehicle-to-everything (V2X) device of the non-vehicular road user or a hand-held V2X device of the non-vehicular road user.
| 18. The apparatus of claim 14, further comprising means for determining the range based on at least one of the intended maneuver, a speed of the ego vehicle, a number of agents detected within a distance of the ego vehicle, a speed of at least one other agent, a direction of travel of at least one other agent, a road condition, a visibility level, a type of road, a quality of service (QoS), an automation level of the ego vehicle, a direction of travel of the ego vehicle, or a combination thereof.
| 19. The apparatus of claim 18, in which the distance is based on at least the intended maneuver, the road condition, the type of road, the speed of the ego vehicle or a combination thereof.
| 20. The apparatus of claim 18, in which:
the ego vehicle is capable of performing a plurality of maneuvers, and
each maneuver corresponds to a different range.
| 21. The apparatus of claim 14, further comprising means for coordinating the intended maneuver with each agent of the first set of agents within the determined spatial distance.
| 22. The apparatus of claim 21, further comprising means for coordinating the intended maneuver via least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I), or a combination thereof.
| 23. The apparatus of claim 14, in which the sensor sharing message identifies objects detected within a distance of the ego vehicle via at least one sensor integrated with the ego vehicle.
| 24. The apparatus of claim 23, in which the objects comprise at least one of non-V2X capable vehicles, non-vehicular road users, infrastructure, road obstacles, road impairments, or a combination thereof.
| 25. The apparatus of claim 14, further comprising:
means for determining the range at an application-layer; and
means for enforcing the range at a physical-layer.
| 26. The apparatus of claim 14, in which the ego vehicle comprises an autonomous vehicle or a semi-autonomous vehicle.
| 27. An ego vehicle, comprising:
a processor;
a memory coupled with the processor; and
instructions stored in the memory and operable, when executed by the processor, to cause the ego vehicle:
to determine an intended maneuver;
to identify a first set of agents for coordinate the intended maneuver;
to determine a spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold;
to apply the determined spatial distance to a sensor-sharing message;
to transmit the sensor-sharing message to a second set of agents within the determined spatial distance; and
to perform the intended maneuver.
| 28. The ego vehicle of claim 27, in which the instructions further cause the ego vehicle to transmit the sensor-sharing message via at least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I) transmission, or a combination thereof.
| 29. The ego vehicle of claim 27, in which agents in the first set of agents and the second set of agents comprise at least one of a vehicle, an infrastructure component, a road side unit, a non-vehicular road user, or a combination thereof.
| 30. The ego vehicle of claim 29, in which the instructions further cause the ego vehicle to receive communications from an embedded vehicle-to-everything (V2X) device of the non-vehicular road user or a hand-held V2X device of the non-vehicular road user.
| 31. The ego vehicle of claim 27, in which the instructions further cause the ego vehicle to determine the range based on at least one of the intended maneuver, a speed of the ego vehicle, a number of agents detected within a distance of the ego vehicle, a speed of at least one other agent, a direction of travel of at least one other agent, a road condition, a visibility level, a type of road, a quality of service (QoS), an automation level of the ego vehicle, a direction of travel of the ego vehicle, or a combination thereof.
| 32. The ego vehicle of claim 31, in which the distance is based on at least the intended maneuver, the road condition, the type of road, the speed of the ego vehicle or a combination thereof.
| 33. The ego vehicle of claim 31, in which:
the ego vehicle is capable of performing a plurality of maneuvers, and
each maneuver corresponds to a different range.
| 34. The ego vehicle of claim 27, in which the instructions further cause the ego vehicle to coordinate the intended maneuver with each agent of the first set of agents within the determined spatial distance.
| 35. The ego vehicle of claim 34, in which the instructions further cause the ego vehicle to coordinate the intended maneuver via least one of a vehicle-to-everything (V2X) transmission, a vehicle-to-vehicle (V2V) transmission, a vehicle-to-infrastructure (V2I), or a combination thereof.
| 36. The ego vehicle of claim 27, in which the sensor sharing message identifies objects detected within a distance of the ego vehicle via at least one sensor integrated with the ego vehicle.
| 37. The ego vehicle of claim 36, in which the objects comprise at least one of non-V2X capable vehicles, non-vehicular road users, infrastructure, road obstacles, road impairments, or a combination thereof.
| 38. The ego vehicle of claim 27, in which the instructions further cause the ego vehicle:
to determine the range at an application-layer; and
to enforce the range at a physical-layer.
| 39. The ego vehicle of claim 27, in which the ego vehicle comprises an autonomous vehicle or a semi-autonomous vehicle.
| 40. A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising:
program code to determine an intended maneuver of an ego vehicle;
program code to identify a first set of agents for coordinate the intended maneuver;
program code to determine a spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold;
program code to transmit the sensor-sharing message to a second set of agents within the determined spatial distance; and
program code to perform the intended maneuver. | The method involves determining (12) an intended maneuver of an ego vehicle, and identifying a set of agents for coordinating the intended maneuver. A spatial distance is determined (13) for obtaining a level of communication reliability with the agents that is greater than a communication reliability threshold. The determined spatial distance to a sensor-sharing message is applied, and the message is transmitted to another set of the agents within the determined distance. The intended maneuver is performed (17), and a range is determined at an application-layer. The range is enforced at a physical-layer, where the vehicle is an autonomous vehicle or semi-autonomous vehicle. INDEPENDENT CLAIMS are included for the following:an apparatus of an ego vehicle;an ego vehicle; anda non-transitory computer-readable medium storing program for applying a spatial distance to sensor-sharing messages. Method for applying spatial distance to sensor-sharing messages and used for enforcing range reliability for information through wireless transmissions by ego vehicle. Uses include but are not limited to telephony, video, data, messaging, and broadcasts. The method enables determining the spatial distance for obtaining a level of communication reliability with the set of agents that is greater than a communication reliability threshold in an efficient manner. The method allows the ego vehicle to transmit the sensor-sharing message to the agents within the determined spatial distance, so that the vehicle can perform the intended maneuver, thus increasing safety and preventing collisions of the vehicles. The drawing shows a flow diagram of a method for applying a spatial distance to sensor-sharing messages. 12Step for determining an intended maneuver of an ego vehicle13Step for determining spatial distance14Step for identifying objects15Step for sharing sensor information16Step for coordinating the intended maneuver | Please summarize the input |
ARCHITECTURE AND PROTOCOL LAYERING FOR SIDELINK POSITIONINGIn some implementations, a user equipment (UE) may implement a ranging support protocol layer comprising one or more ranging support elements. The UE may communicate, using the one or more ranging support elements of the ranging support protocol layer, with a corresponding ranging support protocol layer in one or more other UEs, wherein the communicating is conducted via at least one lower protocol layer implemented at the UE. The UE may provide, at the ranging support protocol layer, a positioning service to an upper protocol layer implemented at the UE, the positioning service based at least in part on the communicating.|1. A method for supporting side link (SL) positioning, the method is performed by a user equipment (UE) and includes the following steps:
implement a ranging support protocol layer including one or more ranging support elements at the UE;
The one or more ranging support elements of the ranging support protocol layer are used to communicate with a corresponding ranging support protocol layer in one or more other UEs, wherein the steps of communicating are via at least one step performed at the UE performed at the underlying protocol level; and
A positioning service is provided at the ranging support protocol layer to an upper protocol layer implemented at the UE, the positioning service being based at least in part on the steps of communication.
| 2. The method of claim 1, wherein the one or more ranging support elements include a discovery function, and the positioning service includes:
Information including a unique identifier of another UE among the one or more other UEs that is capable of participating in side link positioning and ranging services,
information including an indication of a service supported by another of the one or more other UEs,
a side link communication channel with another one of the one or more other UEs,
side link communication period with another one of the one or more other UEs, or
any combination thereof.
| 3. The method described in claim 2 further includes the following steps: using the exploration function to receive information from the upper protocol layer, wherein the information includes:
a trigger for exploring UEs participating in side-link positioning and ranging services,
The attributes of the UE to be explored,
a permission for exploration by another of the one or more other UEs and corresponding attributes of the other UE,
a request or permission for side-link positioning and ranging services, or
any combination thereof.
| 4. The method as described in request item 1, wherein:
the one or more ranging support elements include a group support function; and
Steps to provide a location service to the upper protocol layer include using the group support function to:
when the upper protocol layer specifies a side link positioning and ranging service group, establishing the side link positioning and ranging service group with more than two of the one or more other UEs,
Provide the group ID and group local member ID to the upper protocol layer,
Manage the addition or removal of group members,
Split or merge groups,
Monitor group membership status, or
any combination thereof.
| 5. The method described in claim 4 further includes the following steps: using the group support function to receive information from the upper protocol layer, wherein the information includes:
a request to establish the sidelink positioning and ranging service group,
a request to add or remove a specific group member UE,
A management request for the sidelink positioning and ranging service group that includes merging or splitting groups, or
any combination thereof.
| 6. The method of claim 1, wherein the one or more ranging support elements include side link positioning and ranging protocol functions, and the step of providing a positioning service to the upper protocol layer includes providing:
On-demand sidelink positioning and ranging for determining a range, direction, relative position or relative velocity of another UE or each of a group of other UEs;
Periodic sidelink positioning and ranging for periodically determining a range, direction, relative position or relative speed of another UE or each UE in a group of other UEs;
Triggered sidelink positioning and ranging for triggering determination of a range, direction, relative position or relative speed of another UE or each of a group of other UEs; or
any combination thereof.
| 7. The method described in claim 6 further includes the following steps: using the side link positioning and ranging protocol function to receive information from the upper protocol layer, wherein the information includes:
a request for a current range, direction, relative position or relative speed of another UE or group of UEs,
a request for a periodic range, direction, relative position or relative speed of another UE or group of UEs,
a request for a triggered range, direction, relative position or relative velocity for another UE or group of UEs, or
any combination thereof.
| 8. The method of claim 6 further includes the step of using the side-link positioning and ranging protocol function to communicate with a network server that supports side-link positioning and ranging.
| 9. The method of claim 8, wherein the side-link positioning and ranging protocol function communicates with the network server supporting side-link positioning and ranging using non-access layer (NAS) signaling.
| 10. The method of claim 1, wherein the upper protocol layer is an application layer, and the at least one lower protocol layer includes a ProSe layer, a V2X layer or an Access Layer (AS) layer.
| 11. The method of claim 10, wherein the application layer supports vehicle-to-everything (V2X), autonomous driving, movement of objects in a factory or warehouse, UE-to-UE ranging, or a combination thereof.
| 12. The method of claim 10, wherein the step of communicating using the one or more ranging support elements of the ranging support protocol layer includes using a PC5 communication provided by the ProSe layer, the V2X layer or the AS layer Serve.
| 13. A user equipment (UE) including:
a transceiver;
a memory; and
One or more processors communicatively coupled to the transceiver and the memory, wherein the one or more processors are configured to:
implement a ranging support protocol layer including one or more ranging support elements;
communicating via the transceiver using the one or more ranging support elements of the ranging support protocol layer with a corresponding ranging support protocol layer in one or more other UEs, wherein the communication is performed at the UE at least one lower protocol layer; and
A positioning service is provided at the ranging support protocol layer to an upper protocol layer implemented at the UE, the positioning service being based at least in part on the communication.
| 14. A UE as described in request item 13, wherein:
In order to communicate using the one or more ranging support elements, the one or more processors are configured to implement a discovery function; and
To provide the location service, the one or more processors are configured to provide:
Information including a unique identifier of another UE among the one or more other UEs that is capable of participating in side link positioning and ranging services,
information including an indication of a service supported by another of the one or more other UEs,
a side link communication channel with another one of the one or more other UEs,
side link communication period with another one of the one or more other UEs, or
any combination thereof.
| 15. The UE of claim 14, wherein the one or more processors are further configured to utilize the discovery function to receive information from the upper protocol layer, wherein the information includes:
a trigger for exploring UEs participating in side-link positioning and ranging services,
The attributes of the UE to be explored,
a permission for exploration by another of the one or more other UEs and corresponding attributes of the other UE,
a request or permission for side-link positioning and ranging services, or
any combination thereof.
| 16. A UE as described in request item 13, wherein:
To communicate using the one or more ranging support elements, the one or more processors are configured to implement a group support function; and
To provide the location service to the upper protocol layer, the one or more processors are configured to use the group support function to:
when the upper protocol layer specifies a side link positioning and ranging service group, establishing the side link positioning and ranging service group with more than two of the one or more other UEs,
Provide the group ID and group local member ID to the upper protocol layer,
Manage the addition or removal of group members,
Split or merge groups,
Monitor group membership status, or
any combination thereof.
| 17. The UE of claim 16, wherein the one or more processors are further configured to utilize the group support function to receive information from the upper protocol layer, wherein the information includes:
a request to establish the sidelink positioning and ranging service group,
a request to add or remove a specific group member UE,
A management request for the sidelink positioning and ranging service group that includes merging or splitting groups, or
any combination thereof.
| 18. A UE as described in request item 13, wherein:
To communicate using the one or more ranging support elements, the one or more processors are configured to implement side link positioning and ranging protocol functions; and
In order to provide the location service to the upper protocol layer, the one or more processors are configured to provide:
On-demand sidelink positioning and ranging for determining a range, direction, relative position or relative velocity of another UE or each of a group of other UEs;
Periodic sidelink positioning and ranging for periodically determining a range, direction, relative position or relative speed of another UE or each UE in a group of other UEs;
Triggered sidelink positioning and ranging for triggering determination of a range, direction, relative position or relative speed of another UE or each of a group of other UEs; or
any combination thereof.
| 19. The UE of claim 18, wherein the one or more processors are further configured to utilize the sidelink positioning and ranging protocol function to receive information from the upper protocol layer, wherein the information includes:
a request for a current range, direction, relative position or relative speed of another UE or group of UEs,
a request for a periodic range, direction, relative position or relative speed of another UE or group of UEs,
a request for a triggered range, direction, relative position or relative velocity for another UE or group of UEs, or
any combination thereof.
| 20. The UE of claim 18, wherein the one or more processors are further configured to use the sidelink positioning and ranging protocol function and a network server supporting sidelink positioning and ranging via the transceiver. communication.
| 21. The UE of claim 20, wherein the one or more processors are configured to use the side-link positioning and ranging protocol function to transmit and support side-link positioning and ranging using non-access layer (NAS) signaling. communication with this web server.
| 22. A UE as described in request item 13, wherein:
To provide the location service to the upper protocol layer, the one or more processors are configured to provide the location service to an application layer; and
To communicate via the at least one lower protocol layer, the one or more processors are configured to communicate via a ProSe layer, V2X layer or Access Layer (AS) layer.
| 23. The UE of claim 22, wherein in order to communicate using the one or more ranging support elements of the ranging support protocol layer, the one or more processors are configured to use the ProSe layer, the V2X layer Or a PC5 communication service provided by the AS layer.
| 24. A device for supporting side link (SL) positioning, the device comprising:
Components for implementing a ranging support protocol layer including one or more ranging support elements;
Means for communicating with a corresponding ranging support protocol layer in one or more other UEs using the one or more ranging support elements of the ranging support protocol layer, wherein the communication is performed at the UE at least one underlying protocol layer; and
Means for providing a positioning service at the ranging support protocol layer to an upper protocol layer implemented at the UE, the positioning service being based at least in part on the communication.
| 25. A device as claimed in claim 24, wherein:
The means for communicating using the one or more ranging support elements include means for implementing an exploration function; and
The components used to provide the location service include components used to provide the following information:
Information including a unique identifier of another UE among the one or more other UEs that is capable of participating in side link positioning and ranging services,
information including an indication of a service supported by another of the one or more other UEs,
a side link communication channel with another one of the one or more other UEs,
side link communication period with another one of the one or more other UEs, or
any combination thereof.
| 26. The device of claim 25, further comprising means for utilizing the discovery function to receive information from the upper protocol layer, wherein the information includes:
a trigger for exploring UEs participating in side-link positioning and ranging services,
The attributes of the UE to be explored,
a permission for exploration by another of the one or more other UEs and corresponding attributes of the other UE,
a request or permission for side-link positioning and ranging services, or
any combination thereof.
| 27. A device as claimed in claim 24, wherein:
the means for communicating using the one or more ranging support elements include means for implementing a group support function; and
The means for providing the location service include means for using the group support functionality to:
when the upper protocol layer specifies a side link positioning and ranging service group, establishing the side link positioning and ranging service group with more than two of the one or more other UEs,
Provide the group ID and group local member ID to the upper protocol layer,
Manage the addition or removal of group members,
Split or merge groups,
Monitor group membership status, or
any combination thereof.
| 28. The device of claim 27, further comprising means for utilizing the group support function to receive information from the upper protocol layer, wherein the information includes:
a request to establish the sidelink positioning and ranging service group,
a request to add or remove a specific group member UE,
A management request for the sidelink positioning and ranging service group that includes merging or splitting groups, or
any combination thereof.
| 29. A device as claimed in claim 24, wherein:
the means for communicating using the one or more ranging support elements include means for implementing side link positioning and ranging protocol functions; and
The components used to provide the location service include components used to provide the following information:
On-demand sidelink positioning and ranging for determining a range, direction, relative position or relative velocity of another UE or each of a group of other UEs;
Periodic sidelink positioning and ranging for periodically determining a range, direction, relative position or relative speed of another UE or each UE in a group of other UEs;
Triggered sidelink positioning and ranging for triggering determination of a range, direction, relative position or relative speed of another UE or each of a group of other UEs; or
any combination thereof.
| 30. A non-transitory computer-readable medium that stores instructions for supporting side-link (SL) positioning, including code for:
implement a ranging support protocol layer including one or more ranging support elements;
The one or more ranging support elements of the ranging support protocol layer are used to communicate with a corresponding ranging support protocol layer in one or more other UEs, wherein the communication is via at least one lower layer protocol implemented at the UE carried out in layers; and
A positioning service is provided at the ranging support protocol layer to an upper protocol layer implemented at the UE, the positioning service being based at least in part on the communication. | The method (1400) involves implementing a ranging support protocol layer comprising ranging support elements at a user equipment (UE) e.g. mobile phone. The communication is made with a corresponding ranging support layer in other UEs through a lower protocol layer implemented at the former UE. The positioning service is provided (1402) to an upper protocol layer at the latter UE, where the positioning service comprises information comprising a unique identifier of another UE of the latter UEs that participate in a sidelink positioning and ranging service. The information is received from the upper layer with a discovery function. INDEPENDENT CLAIMS are included for: (1) a user equipment comprises a transceiver; (2) an apparatus for supporting sidelink positioning of user equipment; (3) a non-transitory computer-readable medium for storing instructions. Method for supporting sidelink positioning of user equipment, such as cellular phone, personal digital assistant, laptop computer, cordless phone, wireless local loop station, personal computer, tablet, set-top box, web appliance, network router, switch or bridge. The method enables allowing the UEs to communicate using sidelink signaling and to be located using the sidelink related positioning in an effective manner. The method allows a user equipment (UE) to communicate with other UEs using the positioning service based on the positioning measurements obtained by the base station, so that the positioning services can be provided to the UE in an efficient manner. The drawing shows a flow diagram of a sidelink positioning supporting method.1400Sidelink positioning supporting method 1402Providing services to an upper layer of the architecture 1404Communicating by the ranging support elements | Please summarize the input |
METHOD AND APPARATUS FOR VEHICLE MANEUVER PLANNING AND MESSAGINGTechniques are provided which may be implemented using various methods and/or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external V2X input to determine, send, receive and utilize V2X information and control data, sent between the vehicle and a road side unit (RSU) to determine intersection access and vehicle behavior when approaching the intersection.|1. A method for an autonomous vehicle to enter an intersection, which includes:Determine a braking distance for the autonomous vehicle based on a vehicle external sensor, a vehicle internal sensor, vehicle capability, or external V2X input, or a combination thereof;A first message is sent from the autonomous vehicle, where the first message includes an identification data element or a vehicle type or a vehicle priority or a combination thereof for the autonomous vehicle and a braking distance data for the autonomous vehicle element;Receiving a second message from a roadside unit (RSU) based at least in part on the braking distance for the autonomous vehicle, which includes one or more instructions regarding the autonomous vehicle's intersection entry; andControl the autonomous vehicle to enter the intersection in response to the one or more instructions received from the RSU.
| 2. For example, the method for intersection entry of request item 1, further includes sending a third message from the autonomous vehicle to the RSU before the second message, thereby requesting intersection entry.
| 3. For example, the method for driving at an intersection in claim 1, wherein the braking distance for the autonomous vehicle is determined based at least in part on the speed of the autonomous vehicle.
| 4. For example, the intersection approach method of claim 3, wherein the braking distance for the autonomous vehicle is determined based at least in part on the tire pressure or weather conditions or tire traction data for the autonomous vehicle or a combination thereof.
| 5. For example, the method of driving at an intersection in claim 1, wherein the braking distance for the autonomous vehicle is shorter in the automatic control mode than in the manual mode.
| 6. For example, the method for entering the intersection of request item 1, wherein the first message is a broadcast message.
| 7. For example, the method of entering the intersection of request item 1, wherein the first message is a point-to-point message.
| 8. For example, the method for entering an intersection of request item 1, wherein the first message is a basic safety message or a cooperative sensing message.
| 9. An autonomous vehicle, which includes:One or more wireless transceivers;Vehicle interior sensor;Vehicle external sensor;A memory; andOne or more processors, which are communicatively coupled to the one or more wireless transceivers, the vehicle internal sensors, the vehicle external sensors, and the memory;The one or more processors are configured to:Determine a braking distance for the autonomous vehicle based on the external sensors of the vehicle, the internal sensors of the vehicle, the vehicle capability or the external V2X input, or a combination thereof;A first message is sent from the one or more wireless transceivers, wherein the first message includes an identification data element or a vehicle type or a vehicle priority or a combination thereof for the autonomous vehicle and for the autonomous vehicle One of the braking distance data elements;A second message is received at the one or more wireless transceivers from a road side unit (RSU) based at least in part on the braking distance for the autonomous vehicle, which includes one or more information about the intersection of the autonomous vehicle Multiple instructions; andControl the autonomous vehicle to enter the intersection in response to the one or more instructions received from the RSU.
| 10. Such as the autonomous vehicle of claim 9, wherein the one or more processors are further configured to send a third message from the one or more wireless transceivers to the RSU before the second message, thereby requesting an intersection Drive in.
| 11. Such as the autonomous vehicle of claim 9, wherein the braking distance for the autonomous vehicle is determined based at least in part on the speed of the autonomous vehicle and the empirical stopping distance data associated with the speed of the autonomous vehicle.
| 12. Such as the autonomous vehicle of claim 11, wherein the braking distance for the autonomous vehicle is determined based at least in part on the tire pressure or weather conditions or tire traction data for the autonomous vehicle or a combination thereof.
| 13. Such as the autonomous vehicle of claim 9, wherein the braking distance for the autonomous vehicle is shorter in the automatic control mode than in the manual mode.
| 14. Such as the autonomous vehicle of claim 9, wherein the first message is a broadcast message.
| 15. For example, the autonomous vehicle of claim 9, wherein the first message is a point-to-point message.
| 16. For example, the autonomous vehicle of claim 9, wherein the first message is a basic safety message or a cooperative sensing message.
| 17. An autonomous vehicle, which includes:A component used to determine a braking distance for the autonomous vehicle based on a vehicle exterior sensor, a vehicle interior sensor, vehicle capability, or external V2X input, or a combination thereof;A means for sending a first message from the autonomous vehicle, wherein the first message includes an identification data element or a vehicle type or a vehicle priority or a combination thereof for the autonomous vehicle and the information for the autonomous vehicle A braking distance data element;A member for receiving a second message from a road side unit (RSU) based at least in part on the braking distance for the autonomous vehicle, the second message including one or more instructions regarding the autonomous vehicle entering an intersection ;andA component for controlling the entry of the autonomous vehicle at the intersection in response to the one or more instructions received from the RSU.
| 18. For example, the autonomous vehicle of claim 17, which further includes a component for sending a third message from the autonomous vehicle to the RSU before the second message to request entry at the intersection.
| 19. For example, the autonomous vehicle of claim 17, wherein the first message is a broadcast message.
| 20. For example, the autonomous vehicle of claim 17, wherein the first message is a point-to-point message.
| 21. For example, the autonomous vehicle of claim 17, wherein the first message is a basic safety message or a cooperative sensing message.
| 22. A non-transitory computer-readable medium on which is stored computer-readable instructions that cause one or more processors on an autonomous vehicle to perform the following operations:Determine a braking distance for the autonomous vehicle based on a vehicle external sensor, a vehicle internal sensor, vehicle capability, or external V2X input, or a combination thereof;A first message is sent from the autonomous vehicle, where the first message includes an identification data element or a vehicle type or a vehicle priority or a combination thereof for the autonomous vehicle and a braking distance data for the autonomous vehicle element;Receiving a second message from a roadside unit (RSU) based at least in part on the braking distance for the autonomous vehicle, which includes one or more instructions regarding the autonomous vehicle's intersection entry; andControl the autonomous vehicle to enter the intersection in response to the one or more instructions received from the RSU.
| 23. For example, the non-transitory computer-readable medium of the request item 22 further includes an instruction for the one or more processors to send a third message to the RSU before the second message, so as to request entry into the intersection.
| 24. For example, the non-transitory computer-readable medium of claim 22, wherein the first message is a broadcast message.
| 25. For example, the non-transitory computer-readable medium of request 22, wherein the first message is a point-to-point message.
| 26. For example, the non-transitory computer-readable medium of request 22, wherein the first message is a basic security message or a cooperative awareness message. | The method involves determining a braking distance for the ego vehicle based upon vehicle external sensors, vehicle internal sensors, vehicle capabilities, or external V2X input, or a combination. A first message is sent from the ego vehicle. The first message includes an identification data element for the ego vehicle or a vehicle type or a vehicle priority or a combination thereof and a braking distance data element for the ego vehicle. A second message includes instructions with respect to intersection access by the ego vehicle is received from a roadside unit (RSU) based upon the braking distance for the ego vehicle. The intersection access is controlled by the ego vehicle in response to the instructions received from the RSU. INDEPENDENT CLAIMS are included for the following:an ego vehicle; anda non-transitory computer-readable medium storing program for an ego vehicle. Method for intersection access by ego vehicle. Increased tire inflation decreases the tire surface in contact with the road, reducing traction, and thus increases vehicle turning radius at current speed and reduces maneuverability at current speed. The drawing shows a block diagram of a system level embodiment for an ego vehicle. 910Processor930Wireless transceiver935Camera940Car sensor950Lidar | Please summarize the input |
System and method for relative positioning based safe autonomous drivingDisclosed is a method and apparatus for managing a driving plan of an autonomous vehicle. The method may include obtaining observations of a neighboring vehicle using one or more sensors of the autonomous vehicle. The method may also include classifying one or more behavioral driving characteristics of the neighboring vehicle based on the observations. Furthermore, the method may include updating the driving plan based on a classification of the one or more behavioral driving characteristics of the neighboring vehicle, and controlling one or more operations of the autonomous vehicle based on the updated driving plan.What is claimed is:
| 1. A method for managing a driving plan of an autonomous vehicle, comprising:
obtaining observations of a neighboring vehicle using one or more sensors of the autonomous vehicle, the observations including observed driving behaviors of the neighboring vehicle;
generating a driving risk pattern of the neighboring vehicle based on the observations;
updating the driving plan based on the generated driving risk pattern of the neighboring vehicle; and
controlling one or more operations of the autonomous vehicle based on the updated driving plan,
wherein the generated driving risk pattern indicates one of a plurality of different risk levels,
wherein generating the driving risk pattern of the neighboring vehicle comprises classifying the neighboring vehicle as a vehicle that lacks autonomous driving capability based on the observed driving behaviors of the neighboring vehicle, and
wherein the generated driving risk pattern comprises a first classification indicating the neighboring vehicle as a vehicle lacking autonomous driving capability.
| 2. The method of claim 1, further comprising:
determining a second classification of the neighboring vehicle based on vehicle characteristics of the neighboring vehicle exchanged in a vehicle to vehicle communication; and
cross-checking the second classification against the first classification.
| 3. The method of claim 1, wherein the one or more sensors of the autonomous vehicle used to obtain the observations comprise a RADAR sensor, a LIDAR sensor, a GPS sensor, a proximity sensor, a visual sensor, or a combination thereof.
| 4. The method of claim 1, wherein the generated driving risk pattern is generated using a machine learning model.
| 5. The method of claim 1, wherein obtaining the observations comprises:
collecting, using the one or more sensors of the autonomous vehicle, one or more observable vehicle characteristics of the neighboring vehicle.
| 6. The method of claim 5, wherein the one or more observable vehicle characteristics of the neighboring vehicle collected by the one or more sensors comprise one or more relative accelerations of the neighboring vehicle, a relative speed of the neighboring vehicle, a relative direction of travel of the neighboring vehicle, one or more visual characteristics of the neighboring vehicle, one or more visual characteristics of a driver of the neighboring vehicle, or a combination thereof.
| 7. The method of claim 5, further comprising:
sending, to a server, the one or more observable vehicle characteristics of the neighboring vehicle.
| 8. The method of claim 7, further comprising:
sending, to the server, a plurality of observable vehicle characteristics associated with a plurality of observed vehicles collected by the autonomous vehicle; and
receiving, from the server, a machine learning model trained to identify behavioral characteristics from observable vehicle characteristics using the plurality of observable vehicle characteristics and associated known behavioral characteristics.
| 9. The method of claim 8, further comprising:
periodically sending, to the server, new observable vehicle characteristics associated with new observed vehicles collected by the autonomous vehicle; and
periodically receiving, from the server, an updated machine learning model.
| 10. The method of claim 1, wherein the autonomous vehicle is an autonomous car.
| 11. A system for managing a driving plan of an autonomous vehicle, the system comprising:
one or more sensors configured to obtain observations of a neighboring vehicle, the observations including observed driving behaviors of the neighboring vehicle;
a memory configured to store the observations; and
one or more processors communicably coupled with the memory and the sensors, the one or more processors configured to:
generate a driving risk pattern of the neighboring vehicle based on the observations,
update the driving plan based on the generated driving risk pattern of the neighboring vehicle, and
control one or more operations of the autonomous vehicle based on the updated driving plan,
wherein the generated driving risk pattern indicates one of a plurality of different risk levels,
wherein the one or more processors configured to generate the driving risk pattern of the neighboring vehicle are configured to classify the neighboring vehicle as a vehicle that lacks autonomous driving capability based on the observed driving behaviors of the neighboring vehicle, and
wherein the generated driving risk pattern comprises a first classification indicating the neighboring vehicle as a vehicle lacking autonomous driving capability.
| 12. The system of claim 11, wherein the one or more processors are further configured to:
determine a second classification of the neighboring vehicle based on vehicle characteristics of the neighboring vehicle exchanged in a vehicle to vehicle communication; and
cross-check the second classification against the first classification.
| 13. The system of claim 11, wherein the one or more sensors used to obtain the observations comprise a RADAR sensor, a LIDAR sensor, a GPS sensor, a proximity sensor, a visual sensor, or a combination thereof.
| 14. The system of claim 11, wherein the one or more processors are further configured to use a machine learning model to generate the driving risk pattern of the neighboring vehicle.
| 15. The system of claim 11, further wherein the one or more sensors are configured to:
collect, using the one or more sensors of the autonomous vehicle, one or more observable vehicle characteristics of the neighboring vehicle.
| 16. The system of claim 15, wherein the one or more observable vehicle characteristics of the neighboring vehicle collected by the one or more sensors comprise one or more relative accelerations of the neighboring vehicle, a relative speed of the neighboring vehicle, a relative direction of travel of the neighboring vehicle, one or more visual characteristics of the neighboring vehicle, one or more visual characteristics of a driver of the neighboring vehicle, or a combination thereof.
| 17. The system of claim 15, further comprising:
a wireless subsystem configured to send to a server the one or more observable vehicle characteristics of the neighboring vehicle.
| 18. The system of claim 17, wherein the wireless subsystem is further configured to:
send a plurality of observable vehicle characteristics associated with a plurality of observed vehicles to the server; and
receive, from the server, a machine learning model trained to identify behavioral characteristics from observable vehicle characteristics using the plurality of observable vehicle characteristics and associated known behavioral characteristics.
| 19. The system of claim 18, wherein the wireless subsystem is further configured to:
periodically send, to the server, new observable vehicle characteristics associated with new observed vehicles collected by the autonomous vehicle; and
periodically receive, from the server, an updated machine learning model.
| 20. The system of claim 11, wherein the autonomous vehicle is an autonomous car.
| 21. A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for managing a driving plan of an autonomous vehicle, the operations comprising:
obtaining observations of a neighboring vehicle using one or more sensors of the autonomous vehicle, the observations including observed driving behaviors of the neighboring vehicle;
generating a driving risk pattern of the neighboring vehicle based on the observations;
updating the driving plan based on the generated driving risk pattern of the neighboring vehicle; and
controlling one or more operations of the autonomous vehicle based on the updated driving plan,
wherein the generated driving risk pattern indicates one of a plurality of different risk levels,
wherein generating the driving risk pattern of the neighboring vehicle comprises classifying the neighboring vehicle as a vehicle that lacks autonomous driving capability based on the observed driving behaviors of the neighboring vehicle, and
wherein the generated driving risk pattern comprises a first classification indicating the neighboring vehicle as a vehicle lacking autonomous driving capability.
| 22. The non-transitory computer readable storage medium of claim 21, wherein the operations further comprise:
determining a second classification of the neighboring vehicle based on vehicle characteristics of the neighboring vehicle exchanged in a vehicle to vehicle communication; and
cross-checking the second classification against the first classification.
| 23. The non-transitory computer readable storage medium of claim 21, wherein obtaining the observations comprises:
collecting, using the one or more sensors of the autonomous vehicle, one or more observable vehicle characteristics of the neighboring vehicle; and
sending to a server the one or more observable vehicle characteristics of the neighboring vehicle.
| 24. An apparatus, comprising:
means for obtaining observations of a neighboring vehicle using one or more sensors of an autonomous vehicle, the observations including observed driving behaviors of the neighboring vehicle;
means for generating a driving risk pattern of the neighboring vehicle based on the observations;
means for updating a driving plan based on the generated driving risk pattern of the neighboring vehicle; and
means for controlling one or more operations of the autonomous vehicle based on the updated driving plan,
wherein the generated driving risk pattern indicates one of a plurality of different risk levels,
wherein the means for generating the driving risk pattern of the neighboring vehicle comprises means for classifying the neighboring vehicle as a vehicle that lacks autonomous driving capability based on the observed driving behaviors of the neighboring vehicle, and
wherein the generated driving risk pattern comprises a first classification indicating the neighboring vehicle as a vehicle lacking autonomous driving capability.
| 25. The apparatus of claim 24, further comprising:
means for determining a second classification of the neighboring vehicle based on vehicle characteristics of the neighboring vehicle exchanged in a vehicle to vehicle communication; and
means for cross-checking the second classification against the first classification.
| 26. The apparatus of claim 24, wherein the means for obtaining the observations comprises:
means for collecting, using the one or more sensors of the autonomous vehicle, one or more observable vehicle characteristics of the neighboring vehicle; and
means for sending, to a server, the one or more observable vehicle characteristics of the neighboring vehicle.
| 27. The method of claim 1, further comprising:
obtaining observations of a second neighboring vehicle;
generating a second driving risk pattern of the second neighboring vehicle based on the observations of the second neighboring vehicle; and
updating the driving plan based on a weight average of the driving risk pattern of the neighboring vehicle and the second driving risk pattern of the second neighboring vehicle.
| 28. The method of claim 1, further comprising:
determining a second classification of the neighboring vehicle based on observed visual characteristics of the neighboring vehicle; and
cross-checking the second classification against the first classification.
| 29. The method of claim 1, wherein the observed driving behaviors comprise at least one of a relative speed, a relative acceleration, a relative deceleration, a relative position, or relative direction changes of the neighboring vehicle.
| 30. The method of claim 4, wherein the machine learning model uses objective driving behavior information as truth data to analyze the observations. | The method (300) involves obtaining (302) observations of a neighboring vehicle using one or more sensors of an autonomous vehicle. One or more behavioral driving characteristics of the neighboring vehicle is classified (304) based on the observations. A driving plan is updated (306) based on a classification of the one or more behavioral driving characteristics of the neighboring vehicle. One or more operations of the autonomous vehicle is controlled (308) based on the updated driving plan. INDEPENDENT CLAIMS are included for the following:a system for managing driving plan of autonomous vehicle; anda non-transitory computer readable storage medium storing program for managing driving plan of autonomous vehicle. Method for managing driving plan of autonomous motor vehicles such as cars, trucks and trains using machine learning model. The drive control system updates drive plan relative to the irregular behavioral driving characteristics of vehicle, causing autonomous vehicle to slow down, increase a distance between autonomous vehicle and other vehicle, activate an emergency system e.g. collision warning and brake support. Enables autonomous vehicle to operate in a safe and autonomous manner and continuously adjust and react its environment. The drawing shows the flow diagram of a method for managing a driving plan of an autonomous vehicle. 300Method for managing driving plan of autonomous vehicle302Step for obtaining observations of a neighboring vehicle304Step for classifying one or more behavioral driving characteristic306Step for updating a driving plan308Step for controlling one or more operations of the autonomous vehicle | Please summarize the input |
Shape detecting autonomous vehicleAccording to various embodiments, there is provided a method for controlling a vehicle, the method including detecting a triggering event. The method further includes, in response to detecting the triggering event, determining updated dimensions of the vehicle. The method further includes adjusting operation of the vehicle based on the updated dimensions.What is claimed is:
| 1. A method for controlling a vehicle, the method comprising:
detecting, by a sensor, a triggering event;
determining updated dimensions of the vehicle in response to detecting the triggering event; and
adjusting, by control electronics, at least one operation of the vehicle, wherein the at least one operation of the vehicle comprises an adjustment of at least one of a speed, a turn radius, a navigation path, a clearance allowance, or a parking behavior of the vehicle based at least in part on the updated dimensions.
| 2. The method of claim 1, wherein the triggering event comprises a changed shape event.
| 3. The method of claim 2, wherein the changed shape event comprises detecting a parameter associated with the vehicle and determining whether the parameter exceeds a threshold.
| 4. The method of claim 3, wherein the parameter corresponds to one or more of a weight parameter, a wind parameter, a drag parameter, or an engine torque value.
| 5. The method of claim 2, further comprising:
determining one or more surrounding conditions of the vehicle; and
detecting the changed shape event of the vehicle based at least in part on the one or more surrounding conditions.
| 6. The method of claim 5, wherein the one or more surrounding conditions comprises at least one of a wind force, a road slope, a radius of curvature of a road, or road terrain conditions.
| 7. The method of claim 1, wherein determining the updated dimensions of the vehicle comprises:
sending a scan request to one or more proximate vehicles;
receiving one or more at least partial scans of at least one of the one or more proximate vehicles; and
constructing the updated dimensions of the vehicle based at least in part on at least one of the one or more at least partial scans.
| 8. The method of claim 7, wherein the scan request is sent via vehicle-to-vehicle (V2V) communication.
| 9. The method of claim 7, wherein the one or more at least partial scans comprises at least one Light Detection and Ranging (LIDAR) scan.
| 10. The method of claim 1, wherein the at least one operation of the vehicle serves to control braking, to perform wireless communication, or to perform environment scanning.
| 11. The method of claim 1, further comprising configuring at least one of an engine sensor, a weight sensor, a wind sensor, or a cargo sensor.
| 12. The method of claim 1, wherein determining the updated dimensions of the vehicle comprises:
sending a scan request to one or more scanning devices of the vehicle;
receiving an at least partial scan from at least one of the one or more scanning devices; and
constructing the updated dimensions of the vehicle based on at least one of the at least partial scan.
| 13. The method of claim 12, wherein the at least partial scan is received from another vehicle.
| 14. The method of claim 12, wherein the at least partial scan is received from an unmanned aerial vehicle.
| 15. The method of claim 12, wherein the at least partial scan is received from a camera arranged on a fixed object.
| 16. A controller in a vehicle, the controller comprising:
a processor; and
a memory storing instructions that, when executed by the processor, cause the vehicle to:
detect a triggering event;
determine updated dimensions of the vehicle in response to detection of the triggering event; and
adjust at least one operation of the vehicle, wherein the at least one operation of the vehicle comprises an adjustment of at least one of a speed, a turn radius, a navigation path, a clearance allowance, or a parking behavior of the vehicle based at least in part on the updated dimensions.
| 17. The controller of claim 16, wherein the triggering event comprises detecting a changed shape event.
| 18. The controller of claim 17, wherein execution of the instructions causes the vehicle to:
detect a parameter associated with the vehicle; and
determine whether the parameter exceeds a threshold.
| 19. The controller of claim 18, wherein the parameter corresponds to one or more of a weight parameter, a wind parameter, a drag parameter, or an engine torque parameter.
| 20. The controller of claim 17, wherein execution of the instructions causes the vehicle to further:
determine one or more surrounding conditions of the vehicle; and
detect the changed shape event of the vehicle based at least in part on the one or more surrounding conditions.
| 21. The controller of claim 20, wherein the one or more surrounding conditions comprises at least one of a wind force, a road slope, a radius of curvature of a road, or road terrain conditions.
| 22. The controller of claim 16, wherein execution of the instructions for determining the updated dimensions further causes the vehicle to:
send a scan request to one or more proximate vehicles;
receive one or more at least partial scan of at least one of the one or more proximate vehicles; and
construct the updated dimensions of the vehicle based at least in part on at least one of the one or more at least partial scans.
| 23. The controller of claim 22, wherein the one or more at least partial scans comprises at least one Light Detection and Ranging (LIDAR) scan.
| 24. The controller of claim 16, wherein execution of the instructions causes the vehicle to control braking, to perform wireless communication, or to perform environment scanning.
| 25. The controller of claim 16, wherein execution of the instructions causes the vehicle to:
send a scan request to one or more scanning devices of the vehicle;
receive an at least partial scan from at least one of the one or more scanning devices; and
construct the updated dimensions of the vehicle based on the at least partial scan.
| 26. The controller of claim 25, wherein at least a partial scan is received from another vehicle.
| 27. An apparatus for controlling a vehicle, the apparatus comprising:
means for detecting a triggering event;
means for determining updated dimensions of the vehicle in response to detecting the triggering event; and
means for adjusting at least one operation of the vehicle, wherein the at least one operation of the vehicle comprises an adjustment of at least one of a speed, a turn radius, a navigation path, a clearance allowance, or a parking behavior of the vehicle based at least in part on the updated dimensions.
| 28. The apparatus of claim 27, wherein the triggering event comprises a changed shape event.
| 29. The apparatus of claim 28, wherein the changed shape event comprises detecting a parameter associated with the vehicle and determining whether the parameter exceeds a threshold.
| 30. The apparatus of claim 29, wherein the parameter corresponds to one or more of a weight parameter, a wind parameter, a drag parameter, or an engine torque value. | The method involves detecting a triggering event, determining updated dimensions of the vehicle in response to detecting the triggering event, and adjusting operation of the vehicle based on the updated dimensions. The triggering event involves detecting a changed shape event of the vehicle. The changed shape event is detected by detecting a parameter associated with the vehicle and determining whether the parameter exceeds a threshold. The parameter corresponds to the weight, wind drag, or engine torque of the vehicle. INDEPENDENT CLAIMS are also included for the following:a controller in a vehicle; anda vehicle. Controlling method of vehicle. The vehicle accesses the updated shape information to determine the optimal turn radius for safely traversing the curvature in the road, by using the environment scanning information and the temperature sensor information. The drawing shows the flowchart of a method of controlling an autonomous vehicle. 402Receiving sensor data404Detecting changed shape event406Continuing normal operation408Triggering shape scanning410Adjusting operation based on new shape | Please summarize the input |
VIRTUAL TRAFFIC LIGHT VIA C-V2XTechniques are provided for traffic intersection control information to vehicles via V2X communication links. An example method for providing traffic intersection control messages includes receiving vehicle information associated with a plurality of proximate vehicles, generating one or more vehicle groups based on the vehicle information, generating a traffic control plan based at least in part on the one or more vehicle groups, and transmitting one or more traffic intersection control messages to one or more of the plurality of proximate vehicles based at least in part on the traffic control plan.CLAIMS:
| 1. A method for providing traffic intersection control messages, comprising: receiving vehicle information associated with a plurality of proximate vehicles; generating one or more vehicle groups based on the vehicle information; generating a traffic control plan based at least in part on the one or more vehicle groups; and transmitting one or more traffic intersection control messages to one or more of the plurality of proximate vehicles based at least in part on the traffic control plan.
| 2. The method of claim 1 wherein the vehicle information includes basic safety messages transmitted by one or more vehicles in the plurality of proximate vehicles.
| 3. The method of claim 1 the one or more vehicle groups are based a location of a vehicle, a number of vehicles in a proximate area, a traffic density flowing in a direction, a configuration of an intersection, a size associated with a vehicle, a priority value associated with one or more vehicles, or any combination thereof.
| 4. The method of claim 1 wherein receiving the vehicle information includes receiving vehicle group information from a network resource.
| 5. The method of claim 1 wherein the traffic control plan is based at least in part on a time of day, a date, a current density of traffic, a turn lane configuration, or any combination thereof.
| 6. The method of claim 1 wherein transmitting the one or more traffic intersection control messages includes unicasting a traffic control message including proceed information to one or more vehicles in the plurality of proximate vehicles.
| 7. The method of claim 1 wherein transmitting the one or more traffic intersection control messages includes groupcasting a traffic control message including a list of vehicle identification values.
| 8. The method of claim 1 wherein the one or more traffic intersection control messages are transmitted via a PC5 interface, a Uu interface, a device-to-device protocol, or any combinations thereof.
| 9. A method of receiving a traffic intersection control message, comprising: transmitting one or more basic safety messages; receiving one or more traffic intersection control messages including proceed information; and providing an indication to proceed or halt progress through an intersection based at least in part on the one or more traffic intersection control messages.
| 10. The method of claim 9 further comprising transmitting vehicle priority information.
| 11. The method of claim 9 wherein receiving the one or more traffic intersection control messages includes receiving a unicast message including the proceed information.
| 12. The method of claim 9 wherein receiving the one or more traffic intersection control messages includes receiving a groupcast message including a list of vehicle identification values.
| 13. The method of claim 9 wherein providing the indication to proceed or halt progress through the intersection includes providing an instruction to a controller in an autonomous or semi-autonomous vehicle.
| 14. The method of claim 9 wherein providing the indication to proceed or halt progress through the intersection includes activating a driver alert device.
| 15. The method of claim 9 wherein the one or more traffic intersection control messages are received via a PC5 interface, a Uu interface, a device-to-device protocol, or any combinations thereof.
| 16. An apparatus, comprising: a memory; at least one transceiver; at least one processor communicatively coupled to the memory and the at least one transceiver, and configured to: receive vehicle information associated with a plurality of proximate vehicles; generate one or more vehicle groups based on the vehicle information; generate a traffic control plan based at least in part on the one or more vehicle groups; and transmit one or more traffic intersection control messages to one or more of the plurality of proximate vehicles based at least in part on the traffic control plan.
| 17. The apparatus of claim 16 wherein the vehicle information includes basic safety messages transmitted by one or more vehicles in the plurality of proximate vehicles.
| 18. The apparatus of claim 16 the one or more vehicle groups are based a location of a vehicle, a number of vehicles in a proximate area, a traffic density flowing in a direction, a configuration of an intersection, a size associated with a vehicle, a priority value associated with one or more vehicles, or any combination thereof.
| 19. The apparatus of claim 16 wherein the at least one processor is further configured to receive vehicle group information from a network resource as at least part of the vehicle information associated with the plurality of proximate vehicles.
| 20. The apparatus of claim 16 wherein the traffic control plan is based at least in part on a time of day, a date, a current density of traffic, a turn lane configuration, or any combination thereof.
| 21. The apparatus of claim 16 wherein the at least one processor is further configured to unicast a traffic control message including proceed information to one or more vehicles in the plurality of proximate vehicles as the one or more traffic intersection control messages.
| 22. The apparatus of claim 16 wherein the at least one processor is further configured to groupcast a traffic control message including a list of vehicle identification values as the one or more traffic intersection control messages.
| 23. The apparatus of claim 16 wherein the one or more traffic intersection control messages are transmitted via a PC5 interface, a Uu interface, a device-to-device protocol, or any combinations thereof.
| 24. An apparatus, comprising: a memory; at least one transceiver; at least one processor communicatively coupled to the memory and the at least one transceiver, and configured to: transmit one or more basic safety messages; receive one or more traffic intersection control messages including proceed information; and provide an indication to proceed or halt progress through an intersection based at least in part on the one or more traffic intersection control messages.
| 25. The apparatus of claim 24 wherein the at least on processor is further configured to transmit vehicle priority information.
| 26. The apparatus of claim 24 wherein the at least on processor is further configured to receive a unicast message including the proceed information as the one or more traffic intersection control messages.
| 27. The apparatus of claim 24 wherein the at least one processor is further configured to receive a groupcast message including a list of vehicle identification values as the one or more traffic intersection control messages.
| 28. The apparatus of claim 24 wherein the at least one processor is further configured to provide an instruction to a controller in an autonomous or semi- autonomous vehicle as the indication to proceed or halt progress through the intersection.
| 29. The apparatus of claim 24 wherein the at least one processor is further configured to activate a driver alert device as the indication to proceed or halt progress through the intersection.
| 30. The apparatus of claim 24 wherein the one or more traffic intersection control messages are received via a PC5 interface, a Uu interface, a device-to-device protocol, or any combinations thereof. | The method (1100) involves receiving vehicle information associated with multiple proximate vehicles (1102). The vehicle groups are generated (1104) based on the vehicle information. A traffic control plan is generated (1106) based in portion on the vehicle groups. The traffic intersection control messages are transmitted (1108) to the proximate vehicle through PC5 interface, a UMTS air interface (Uu) interface, or a device-to-device protocol, based in portions on the traffic control plans, where the vehicle information includes basic safety messages transmitted by the vehicles in the multiple vehicles. INDEPENDENT CLAIMS are included for: (1) a method for receiving a traffic intersection control message; (2) an apparatus for providing traffic intersection control messages to vehicles through vehicle-to-everything communication links; (3) an apparatus for receiving a traffic intersection control message. Method for providing traffic intersection control messages to vehicles such as autonomous or semi-autonomous vehicles, i.e. car through vehicle-to-everything communication links uisng a user euipment (UE). Uses include but are not limited to mobile phone, router, tablet computer, laptop computer, consumer asset tracking device, Internet of Things (IoT) device, ot on-board unit (OBU). The traffic congestion and the potential for collisions at intersections can be reduced. The traffic control messages can be unicasted or groupcasted to the vehicles and the vehicles can proceed or halt at an intersection as a group. The vehicle groups are evaluated in view of a traffic control plan and the groups can be prioritized for proceeding through a traffic intersection. The positioning reference signal (PRS) muting can be used to reduce interference by muting PRS signals. The drawing shows a flow diagram illustrating a method for providing traffic intersection control information to vehicles.1100Method for providing traffic intersection control messages to vehicles 1102Receiving vehicle information associated with multiple proximate vehicle 1104Generating vehicle groups 1106Generating traffic control plan 1108Transmitting traffic intersection control messages to the proximate vehicle | Please summarize the input |
IMPLEMENTING CONFIDENCE METRICS IN VEHICLE-TO-EVERYTHING (V2X) COMMUNICATIONSCertain aspects of the present disclosure provide techniques for enhancing vehicle operations safety using coordinating vehicle platooning or enhancing platooning safety against location spoofing attacks. In one example, a source user equipment (UE) detects a potential spoofing event associated with location information being altered in an unauthorized manner, the source UE may transmit a request to a platoon control system (PCS) to join a vehicle platoon. In another example, a first UE associated with a lead vehicle in an existing platoon may detect a potential spoofing event associated with location information being altered in an unauthorized manner. The lead vehicle may transmit to a second UE of another vehicle in the platoon an indication of the detection and a request to exchange the respective roles in the platoon. The PCS may also monitor the conditions of the first and the second UEs, and arrange for the platoon reorganization.WHAT IS CLAIMED IS:
| 1. A source user equipment (UE) for wireless communications, comprising: a memory; and a processor coupled with the memory, the processor and the memory configured to: detect a potential spoofing event associated with location information being altered in an unauthorized manner; and transmit, in response to the detected potential spoofing event, a request to a platoon control system (PCS) to join a vehicle platoon, wherein the request includes an indication of the detected potential spoofing event.
| 2. The source UE of claim 1, wherein the request to the PCS comprises a confidence metric that indicates a probability that the source UE is receiving spoofed location information.
| 3. The source UE of claim 2, wherein the processor and the memory are configured to detect the potential spoofing event by detecting that the confidence metric is above a threshold value.
| 4. The source UE of claim 2, wherein the confidence metric indicates one of a plurality of levels of accuracy of a corresponding level of certainty of the potential spoofing event, and wherein a threshold value corresponds to a predefined level of accuracy.
| 5. The source UE of claim 2, wherein the processor and the memory are configured to detect the potential spoofing event by receiving one or more signals from at least one of a network entity or a second UE in one or more basic safety messages (BSMs).
| 6. The source UE of claim 5, wherein the confidence metric is determined by comparing at least one characteristic indicated by the one or more signals and a characteristic indicated by received location information. 48
| 7. The source UE of claim 2, wherein the processor and the memory are configured to detect the potential spoofing event by measuring, using at least one onboard sensor independent from the location information, a movement attribute of the source UE to examine a validity of the location information.
| 8. The source UE of claim 2, wherein the request further comprises at least one of: a vehicle identifier, destination information, or a source positioning location.
| 9. The source UE of claim 1, wherein the request further indicates at least one of: an occupancy parameter of a vehicle associated with the source UE; an autonomy level of a vehicle associated with the UE; or a travel preference parameter.
| 10. The source UE of claim 1, wherein the processor and the memory are further configured to : receive a response indicating confirmation that the source UE is allowed to join a vehicle platoon assigned by the PCS; receive an invitation corresponding to the confirmation from a lead UE of a lead vehicle of the vehicle platoon; and abstain from transmitting vehicle-to-everything (V2X) messages upon receiving the response.
| 11. The source UE of claim 1, wherein the processor and the memory are further configured to: receive an alert notice from the PCS when the PCS does not have an available vehicle platoon to assign, wherein the alert notice comprises alert messages requesting manual control.
| 12. A network entity for wireless communications, comprising: a memory; and a processor coupled with the memory, the processor and the memory configured to: 49 receive a request from a user equipment (UE), the request triggered by a detection of a potential spoofing event at the UE; and transmit, to the UE, an assignment of a vehicle platoon for the UE to join based on the request.
| 13. The network entity of claim 12, wherein the request further includes a confidence metric that indicates a probability that the UE is receiving spoofed location information of the potential spoofing event.
| 14. The network entity of claim 13, wherein the request comprises at least one of: a vehicle identifier, destination information, a source positioning location, or the confidence metric.
| 15. The network entity of claim 12, wherein the assignment has a higher priority when the UE is associated with an autonomous vehicle than when the UE is associated with a non-autonomous vehicle.
| 16. The network entity of claim 12, wherein the processor and the memory are configured to: transmit, to at least one platoon UE of a corresponding vehicle in the vehicle platoon, an instruction for the at least one platoon UE to transmit a beacon to the UE, wherein the beacon is to be measured by the UE.
| 17. The network entity of claim 12, wherein the processor and the memory are configured to: transmit, to a roadside unit (RSU), an instruction for the RSU to measure a location of the UE for comparison with location information therein and assessment of the potential spoofing event; and confirm the assignment of the vehicle platoon based on the location measured by the RSU.
| 18. A first user equipment (UE), comprising: a memory; and 50 a processor coupled with the memory, the processor and the memory configured to: detect a potential spoofing event associated with location information being altered in an unauthorized manner; transmit, to a second UE, an indication of the detection of the potential spoofing event, wherein the first UE and the second UE are associated with vehicles in a platoon; and transmit, to the second UE, a request to exchange a role of a vehicle corresponding to the first UE in the platoon with a role of a vehicle corresponding to the second UE in the platoon.
| 19. The first UE of claim 18, wherein the indication is carried in one or more basic safety messages (BSMs).
| 20. The first UE of claim 18, wherein the role of the vehicle corresponding to the first UE is a lead vehicle managing UEs of other vehicles in the platoon, and wherein the role of the vehicle corresponding to the second UE is a secondary vehicle managed by the lead vehicle.
| 21. The first UE of claim 18, wherein the processor and the memory are configured to: detect the potential spoofing event by determining a first confidence metric of the first UE, the first confidence metric associated with a position accuracy based on a verification of global navigation satellite system (GNSS) position information received at the first UE.
| 22. The first UE of claim 21, wherein the processor and the memory are further configured to: detect the potential spoofing event by determining that the first confidence metric indicating a probability that the first UE is receiving spoofed location information is above a threshold value.
| 23. The first UE of claim 21, wherein the processor and the memory are further configured to: receive from at least the second UE, data of sensors thereof, wherein the verification of the GNSS position information is based on the data of sensors.
| 24. The first UE of claim 21, wherein the processor and the memory are further configured to: receive data from a roadside unit (RSU), wherein the verification of the GNSS position information is further based on the data of the RSU.
| 25. The first UE of claim 21, wherein the processor and the memory are further configured to: transmit, an indication of the potential spoofing event, to a platoon control system (PCS) in control of the platoon when the confidence metric is above a threshold value.
| 26. The first UE of claim 21, wherein the processor and the memory are further configured to: request, from the second UE in the platoon, a second confidence metric of the second UE, the second confidence metric indicating a probability that the second UE is receiving spoofed location information, wherein transmitting the request to exchange roles in the platoon is based on the second confidence metric being below a threshold value and indicating an absence of spoofing attack to the second UE; and transmit, to the second UE in the platoon, an indication for the second UE to leave the platoon based on the second confidence metric being greater than or equal to the threshold value.
| 27. An apparatus for wireless communications, comprising: a memory; and a processor coupled with the memory, the processor and the memory configured to: receive an indication from a first user equipment (UE) of a first vehicle in a vehicle platoon, the indication triggered by the first UE detecting a first potential spoofing event associated with location information being altered in an unauthorized manner; and transmit, to a second UE in the vehicle platoon, an indication for the second UE to assume functionalities performed by the first UE in the vehicle platoon.
| 28. The apparatus of claim 27, wherein the first UE is a lead UE configured to perform functionalities including management of other UEs in the vehicle platoon.
| 29. The apparatus of claim 27, wherein the second UE and the first UE are in sidelink communication, and the second UE is managed by the first UE before the indication of the first potential spoofing event.
| 30. The apparatus of claim 27, wherein the first potential spoofing event is determined based on that a first confidence metric of the first UE indicating a probability that the first UE is receiving spoofed location information is above a threshold value. | The equipment has a processor coupled with a memory. The processor detects a potential spoofing event associated with location information altered in an unauthorized manner and transmits a request to a platoon control system (PCS) to join a vehicle platoon in response to the detected potential spoofing event, where the request includes an indication of the detected potential spoofing event and the confidence metric indicates levels of accuracy of a corresponding level of certainty of the potential spoofing event. The processor receives a response indicating confirmation of a source user equipment allowed to join a vehicle platoon assigned by the PCS. INDEPENDENT CLAIMS are included for:(1) a network entity for performing wireless communication for coordinating vehicle platooning;(2) an apparatus for performing wireless communication for coordinating vehicle platooning. Source user equipment e.g. mobile station for performing wireless communication for coordinating vehicle platooning for use in telecommunication services e.g. telephony. Uses include but are not limited to video, data, messaging, broadcasts, a terminal, an access terminal, a subscriber unit, a station, a customer premises equipment, a cellular phone, an intelligent phone, a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a intelligent book, an ultrabook and a medical device. The equipment enhances vehicle operations safety using coordinated vehicle platooning or platooning safety from location spoofing attacks or attempts to alter location information in unauthorized manners. The equipment realizing improved spectral efficiency, reduced operation cost and increased reliability, maintains a minimal distance or headway between moving vehicles at high speeds and avoids use of potentially spoofed location information. The drawing shows a schematic view of a source user equipment. 400Vehicle-to-everything system 402Vehicle 406Wireless communication link 408Vehicle-to-vehicle interface 410Roadside service unit | Please summarize the input |
Method and apparatus for vehicle steering plan and messagingThe present invention provides techniques that may be implemented using various methods and/or devices in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capability and external V2X input to determine, transmit; receiving and using the V2X information and control data sent between the vehicle and the roadside unit (RSU), so as to determine the intersection entrance and the vehicle behavior at the near intersection.|1. A method for entering a crossroad of a self-control vehicle, comprising: based on vehicle external sensor, vehicle internal sensor, vehicle capacity or external V2X input or a combination thereof to determine the braking distance of the self-control vehicle; sending a first message from the autonomous vehicle, wherein the first message comprises identification data element or vehicle type or vehicle priority or a combination thereof for the self-control vehicle and a braking distance data element for the self-control vehicle; receiving a second message from the roadside unit RSU at least partially based on the braking distance for the autonomous vehicle; the second message comprises one or more instructions related to the intersection of the autonomous vehicle; and controlling the intersection entry of the self-control vehicle in response to the one or more instructions received from the RSU.
| 2. The method according to claim 1, further comprising sending a third message from the self-control vehicle to the RSU prior to the second message to request entry of a crossroad.
| 3. The method of entering a crossroad according to claim 1, wherein the braking distance for the self-control vehicle is determined based at least in part on the speed of the self-control vehicle.
| 4. The method for entering a crossroad according to claim 3, wherein the braking distance for the self-control vehicle is determined based at least in part on a tire pressure or weather condition or tire traction data for the self-control vehicle or a combination thereof.
| 5. The method according to claim 1, wherein the braking distance for the self-control vehicle is shorter in the autonomous mode than in the manual mode.
| 6. The method of entering a crossroad according to claim 1, wherein the first message is a broadcast message.
| 7. The method according to claim 1, wherein the first message is a peer-to-peer message.
| 8. The intersection entry method according to claim 1, wherein the first message is a basic security message or a cooperative awareness message.
| 9. A self-control vehicle, comprising: one or more wireless transceivers; a vehicle internal sensor; a vehicle external sensor; a memory; and one or more processors, the one or more processors communicatively coupling to the one or more wireless transceivers, the vehicle internal sensor, the vehicle external sensor and the memory; wherein the one or more processors are configured to: based on the vehicle external sensor, the vehicle internal sensor, vehicle capacity or external V2X input or a combination thereof to determine a braking distance for the autonomous vehicle; sending a first message from the one or more transceivers, wherein the first message comprises identification data element or vehicle type or vehicle priority or a combination thereof for the self-control vehicle and a braking distance data element for the self-control vehicle; at the one or more wireless transceivers at least partially based on the braking distance of the self-control vehicle from the roadside unit RSU receives the second message, the second message comprises one or more instructions related to the intersection of the self-control vehicle; and controlling the intersection entry of the self-control vehicle in response to the one or more instructions received from the RSU.
| 10. The self-control vehicle according to claim 9, wherein the one or more processors are further configured to send a third message from the one or more wireless transceivers to the RSU prior to the second message to request entry of a crossroad.
| 11. The self-control vehicle according to claim 9, wherein the braking distance for the self-control vehicle is determined based at least in part on the speed of the self-control vehicle and the experience stop distance data associated with the speed of the self-control vehicle.
| 12. The self-control vehicle according to claim 11, wherein the braking distance for the self-control vehicle is determined based at least in part on a tire pressure or weather condition or tire traction data for the self-control vehicle or a combination thereof.
| 13. The self-control vehicle according to claim 9, wherein the braking distance for the self-control vehicle is shorter in the autonomous mode than in the manual mode.
| 14. The self-control vehicle according to claim 9, wherein the first message is a broadcast message.
| 15. The self-control vehicle according to claim 9, wherein the first message is a peer-to-peer message.
| 16. The self-control vehicle according to claim 9, wherein the first message is a basic security message or a cooperative sensing message.
| 17. A self-control vehicle, comprising: means for determining a braking distance for the autonomous vehicle based on a vehicle external sensor, a vehicle internal sensor, a vehicle capability or an external V2X input, or a combination thereof; means for sending a first message from the self-control vehicle, wherein the first message comprises identification data element or vehicle type or vehicle priority or a combination thereof for the self-control vehicle and a braking distance data element for the self-control vehicle; means for receiving a second message from a roadside unit RSU based at least in part on the braking distance for the self-control vehicle; the second message comprises one or more instructions to enter at a crossroad of the self-control vehicle; and means for controlling the entry of the crossroads of the self-control vehicle in response to the one or more instructions received from the RSU.
| 18. The self-control vehicle according to claim 17, further comprising means for sending a third message from the self-control vehicle to the RSU prior to the second message to request entry of a crossroad.
| 19. The self-control vehicle according to claim 17, wherein the first message is a broadcast message.
| 20. The self-control vehicle according to claim 17, wherein the first message is a peer-to-peer message.
| 21. The self-control vehicle according to claim 17, wherein the first message is a basic security message or a cooperative sensing message.
| 22. A non-transitory computer-readable medium having stored thereon computer-readable instructions for causing one or more processors on a self-control vehicle to perform the following operations: based on vehicle external sensor, vehicle internal sensor, vehicle capacity or external V2X input or a combination thereof to determine the braking distance of the self-control vehicle; sending a first message from the autonomous vehicle, wherein the first message comprises identification data element or vehicle type or vehicle priority or a combination thereof for the self-control vehicle and a braking distance data element for the self-control vehicle; receiving a second message from the roadside unit RSU at least partially based on the braking distance for the autonomous vehicle; the second message comprises one or more instructions related to the intersection of the autonomous vehicle; and controlling the intersection entry of the self-control vehicle in response to the one or more instructions received from the RSU.
| 23. The non-transitory computer-readable medium according to claim 22, further comprising instructions that cause the one or more processors to send a third message to the RSU prior to the second message to request entry of a crossroad.
| 24. The non-transitory computer readable medium according to claim 22, wherein the first message is a broadcast message.
| 25. The non-transitory computer-readable medium according to claim 22, wherein the first message is a peer-to-peer message.
| 26. The non-transitory computer-readable medium according to claim 22, wherein the first message is a basic security message or a cooperative awareness message. | The method involves receiving a first message from a first vehicle at an ego vehicle. The first message includes an identification data element for the first vehicle, an autonomous vehicle status data element for the first vehicle or a braking distance data element for the first vehicle or a combination. A second message is received from a second vehicle at the ego vehicle. The second message comprises an identification data element for the second vehicle. A target space is determined based upon a size of the ego vehicle, the autonomous vehicle status data element for the first vehicle. The autonomous vehicle status data element for the second vehicle. The braking distance data element for the first vehicle or the braking distance data element for the second vehicle or a combination. An INDEPENDENT CLAIM is included for an ego vehicle with wireless transceivers. Method for messaging an automotive device to facilitate maneuvering of an ego vehicle (claimed). Method for messaging an automotive device to facilitate vehicle maneuvering increases vehicle turning radius at current speed and reduces maneuverability at current speed, and avoid collisions during an emergency stop of the vehicles. The drawing shows a block diagram of a device for determination and communication of a Vehicle-to-everything (V2X) capability data element value based on vehicle internal and external sensors. 100Vehicle external sensors110Vehicle internal sensors120Vehicle capabilities910Processor | Please summarize the input |
Methods and systems for managing interactions between vehicles with varying levels of autonomyMethods, devices and systems enable controlling an autonomous vehicle by identifying a vehicle that is within a threshold distance of the autonomous vehicle, determining an autonomous capability metric (ACM) the identified vehicle, determining whether the ACM of the identified vehicle is greater than a first threshold, determining whether the ACM of the identified vehicle is less than a second threshold, and adjusting a driving parameter of the autonomous vehicle so that the autonomous vehicle is more or less reliant on the capabilities of the identified vehicle based on whether the ACM of the identified vehicle exceeds the thresholds.What is claimed is:
| 1. A method of controlling an autonomous vehicle, comprising:
identifying, via a processor of the autonomous vehicle, a vehicle that is within a threshold distance of the autonomous vehicle;
determining an autonomous capability metric (ACM) of the identified vehicle, wherein the ACM is a vector data structure including a plurality of values each representing a capability of the identified vehicle, the ACM being dynamically determined based on real-time data from the identified vehicle and certificates received via cellular vehicle-to-everything (C-V2X) communications;
determining whether the ACM of the identified vehicle is greater than a first threshold; and
adjusting a driving parameter of the autonomous vehicle based on capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 2. The method of claim 1, wherein adjusting the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle exceeds the first threshold comprises decreasing a minimum following distance to be maintained between the autonomous vehicle and the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 3. The method of claim 1, further comprising:
determining whether the ACM of the identified vehicle is less than a second threshold in response to determining that the ACM of the identified vehicle is not greater than the first threshold; and
adjusting the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold.
| 4. The method of claim 3, wherein adjusting the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold comprises increasing a minimum following distance to be maintained between the autonomous vehicle and the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold.
| 5. The method of claim 1, wherein identifying the vehicle that is within the threshold distance of the autonomous vehicle comprises identifying a vehicle that is in front of the autonomous vehicle and within the threshold distance of the autonomous vehicle.
| 6. The method of claim 1, wherein determining the ACM of the identified vehicle comprises determining a value that identifies:
a current level of autonomy of the identified vehicle;
an autonomous capability of the identified vehicle; or
whether the identified vehicle includes an advanced autonomous control system.
| 7. The method of claim 1, wherein:
determining whether the ACM of the identified vehicle is greater than the first threshold comprises applying the plurality of values to a plurality of decision nodes that each evaluate a different feature, factor or data point.
| 8. The method of claim 7, wherein applying the plurality of values to the plurality of decision nodes that each evaluate the different feature, factor or data point comprises applying one or more of the plurality of values to a decision node that evaluates:
whether vehicle-to-vehicle (V2V) communication circuitry is present in the identified vehicle;
whether an accuracy range of a sensor in the identified vehicle is greater than a threshold value; or
whether a thickness of each brake pad in the identified vehicle exceeds a threshold thickness of friction material.
| 9. A processor for an autonomous vehicle, wherein the processor is configured with processor executable instructions to:
identify a vehicle that is within a threshold distance of the autonomous vehicle;
determine an autonomous capability metric (ACM) of the identified vehicle, wherein the ACM is a vector data structure including a plurality of values each representing a capability of the identified vehicle, the ACM being dynamically determined based on real-time data from the identified vehicle and certificates received via cellular vehicle-to-everything (C-V2X) communications;
determine whether the ACM of the identified vehicle is greater than a first threshold; and
adjust a driving parameter of the autonomous vehicle based on capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 10. The processor of claim 9, wherein the processor is configured with processor executable instructions to adjust the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle exceeds the first threshold by decreasing a minimum following distance to be maintained between the autonomous vehicle and the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 11. The processor of claim 9, wherein the processor is further configured with processor executable instructions to:
determine whether the ACM of the identified vehicle is less than a second threshold in response to determining that the ACM of the identified vehicle is not greater than the first threshold; and
adjust the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold.
| 12. The processor of claim 11, wherein the processor is configured with processor executable instructions to adjust the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold by increasing a minimum following distance to be maintained between the autonomous vehicle and the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold.
| 13. The processor of claim 9, wherein the processor is further configured with processor executable instructions to identify the vehicle that is within the threshold distance of the autonomous vehicle by identifying a vehicle that is in front of the autonomous vehicle and within the threshold distance of the autonomous vehicle.
| 14. The processor of claim 9, wherein the processor is configured with processor executable instructions to determine the ACM of the identified vehicle by determining a value that identifies:
a current level of autonomy of the identified vehicle;
an autonomous capability of the identified vehicle; or
whether the identified vehicle includes an advanced autonomous control system.
| 15. The processor of claim 9, wherein the processor is configured with processor executable instructions to:
determine whether the ACM of the identified vehicle is greater than the first threshold by applying the plurality of values to a plurality of decision nodes that each evaluate a different feature, factor or data point.
| 16. The processor of claim 15, wherein the processor is configured with processor executable instructions to apply the plurality of values to the plurality of decision nodes that each evaluate the different feature, factor or data point by applying one or more of the plurality of values to a decision node that evaluates:
whether vehicle-to-vehicle (V2V) communication circuitry is present in the identified vehicle;
whether an accuracy range of a sensor in the identified vehicle is greater than a threshold value; or
whether a thickness of each brake pad in the identified vehicle exceeds a threshold thickness of friction material.
| 17. A non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of an autonomous vehicle to perform operations comprising:
identifying a vehicle that is within a threshold distance of the autonomous vehicle;
determining an autonomous capability metric (ACM) of the identified vehicle, wherein the ACM is a vector data structure including a plurality of values each representing a capability of the identified vehicle, the ACM being dynamically determined based on real-time data from the identified vehicle and certificates received via cellular vehicle-to-everything (C-V2X) communications;
determining whether the ACM of the identified vehicle is greater than a first threshold; and
adjusting a driving parameter of the autonomous vehicle based on capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 18. The non-transitory processor-readable storage medium of claim 17, wherein the stored processor-executable instructions are configured to cause the processor of the autonomous vehicle to perform the operations such that adjusting the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle exceeds the first threshold comprises decreasing a minimum following distance to be maintained between the autonomous vehicle and the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold.
| 19. The non-transitory processor-readable storage medium of claim 17, wherein the stored processor-executable instructions are configured to cause the processor of the autonomous vehicle to perform operations further comprising:
determining whether the ACM of the identified vehicle is less than a second threshold in response to determining that the ACM of the identified vehicle is not greater than the first threshold; and
adjusting the driving parameter of the autonomous vehicle based on the capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is not greater the first threshold and is less than the second threshold.
| 20. An autonomous vehicle, comprising:
means for identifying a vehicle that is within a threshold distance of the autonomous vehicle;
means for determining an autonomous capability metric (ACM) of the identified vehicle, wherein the ACM is a vector data structure including a plurality of values each representing a capability of the identified vehicle, the ACM being dynamically determined based on real-time data from the identified vehicle and certificates received via cellular vehicle-to-everything (C-V2X) communications;
means for determining whether the ACM of the identified vehicle is greater than a first threshold; and
means for adjusting a driving parameter of the autonomous vehicle so that the autonomous vehicle based on capabilities of the identified vehicle in response to determining that the ACM of the identified vehicle is greater than the first threshold. | The method (1100) involves identifying (902) a vehicle that is within a threshold distance of an autonomous vehicle by a processor of the autonomous vehicle. An Autonomous capability metric (ACM) of the identified vehicle is determined (1104). A determination is made (1106) to check whether the ACM is greater than a first threshold. A driving parameter of the autonomous vehicle is adjusted (1108) based on capabilities of the determined identified vehicle in response to determining that the determined ACM exceeds the first threshold by decreasing a minimum following distance to be maintained between the vehicle and an identified vehicle e.g. car. The vehicle is in front of and within the threshold distance. INDEPENDENT CLAIMS are included for: (1) a processor for an autonomous vehicle; (2) a non-transitory processor-readable storage medium for storing processor-executable instructions; (3) an autonomous vehicle comprises a unit for identifying a vehicle that is within a threshold distance of the autonomous vehicle. Method for controlling an autonomous vehicle, such as a car. The method enables utilizing vehicle-based communications for safer and more efficient use of motor vehicles and transportation resources. The method allows the autonomous vehicle to determine the autonomous capability metric of the identified vehicles and adjust the driving parameter of the autonomous vehicles based on the determined autonomous capability metrics of the vehicles, thus improving safety and performance of the vehicle in an efficient manner. The drawing shows a flow chart of the method for controlling an autonomous vehicle.902Identifying a vehicle that is within a threshold distance of the autonomous vehicle 1100Method for controlling an autonomous vehicle 1104Determining ACM of the identified vehicle 1106Determining whether the ACM of the identified vehicle is greater than a first threshold 1108Adjusting a driving parameter of the autonomous vehicle | Please summarize the input |
RESOURCE MANAGEMENT FOR COMMUNICATION AND SENSING SERVICESVarious aspects of the present disclosure generally relate to wireless communication. In some aspects, a first core network entity may receive a first request associated with initiation of a sensing service associated with a user equipment (UE). The first core network entity may receive, from at least one other core network entity and based at least in part on the first request, one or more sensing session parameters associated with the sensing service. The first core network entity may provide, to a second core network entity and based at least in part on the sensing session parameters, a second request to establish a virtual communication session with the UE, the request including one or more communication session parameters. Numerous other aspects are described.WHAT IS CLAIMED IS:
| 1. A first core network entity, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: receive a first request associated with initiation of a sensing service associated with a user equipment (UE); receive, from at least one other core network entity and based at least in part on the first request, one or more sensing session parameters associated with the sensing service; and provide, to a second core network entity and based at least in part on the sensing session parameters, a second request to establish a virtual communication session with the UE, the request including one or more communication session parameters.
| 2. The first core network entity of claim 1, wherein the one or more processors are further configured to: map the one or more sensing session parameters to the one or more communication session parameters.
| 3. The first core network entity of claim 1, wherein the one or more sensing session parameters include at least one of: a sensing type parameter, a range parameter, a range resolution parameter, a velocity parameter, a velocity resolution parameter, an azimuth field of view parameter, an angular resolution parameter, a maximum number of detected targets, a data rate parameter, or a latency parameter.
| 4. The first core network entity of claim 1, wherein the one or more communication session parameters include at least one of: a quality of service parameter, a signal-to-interference-plus-noise ratio (SINR) parameter, a data rate parameter, or a latency parameter.
| 5. The first core network entity of claim 1, wherein the one or more sensing session parameters are based at least in part on at least one of: subscription information associated with the UE and the sensing service, or policy information associated with the sensing service.
| 6. The first core network entity of claim 1, wherein the first core network entity comprises a non-communication session management function (N-SMF) entity and the second core network entity comprises a session management function (SMF) entity.
| 7. The first core network entity of claim 1, wherein the at least one other core network entity comprises at least one of: a non-communication policy control function (N-PCF) entity, or a unified data management (UDM) entity.
| 8. The first core network entity of claim 1, wherein the first request is received from an access and mobility management function (AMF) entity.
| 9. The first core network entity of claim 1, wherein the first request is associated with a sensing network slice that indicates the first request is for the sensing service.
| 10. The first core network entity of claim 1, wherein the first request is associated with a dynamic network name (DNN) or access point name (APN) that indicates the first request is for the sensing service.
| 11. The first core network entity of claim 1, wherein the one or more processors, to receive the one or more sensing session parameters, are configured to: receive information indicating one or more policies for managing the sensing service.
| 12. The first core network entity of claim 11, wherein the information indicating the one or more policies is based at least in part on information that identifies a location of the UE.
| 13. The first core network entity of claim 11, wherein the information indicating the one or more policies is received from a non-communication policy control function (N-PCF) entity.
| 14. The first core network entity of claim 1, wherein the one or more processors are further configured to: determine, based at least in part on the sensing session parameters, a communication service type for the virtual communication session; and indicate, to the second core network entity, that the virtual communication session is associated with the communication service type.
| 15. The first core network entity of claim 14, wherein the communication service type is associated with at least one of: vehicle-to-everything (V2X) communications, or unmanned autonomous vehicle (UAV) communications.
| 16. The first core network entity of claim 1, wherein the one or more processors are further configured to: provide, for a network node associated with the UE, embedded radio level operation configuration information in a sensing session specific container.
| 17. The first core network entity of claim 1, wherein the one or more processors are further configured to: provide, to a network node, a third request to establish the sensing service between the network node and the UE.
| 18. The first core network entity of claim 17, wherein the third request includes information indicating whether the sensing service is for a radar service or positioning service.
| 19. The first core network entity of claim 17, wherein the third request includes information indicating the one or more sensing session parameters.
| 20. The first core network entity of claim 17, wherein the third request includes information indicating whether the sensing service is for UE-based sensing or network node-based sensing.
| 21. The first core network entity of claim 17, wherein the third request includes information indicating a priority associated with the sensing service.
| 22. A first core network entity, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: receive a first request associated with a communication session associated with a user equipment (UE), wherein the first request is associated with one or more first communication session parameters; receive, from a second core network entity, a second request to establish a virtual communication session associated with the UE, wherein the second request is associated with one or more second communication session parameters, and wherein the virtual communication session corresponds to a sensing service; and provide, to a network node, information indicating the one or more first communication session parameters for the communication session and the one or more second communication session parameters for the virtual communication session.
| 23. The first core network entity of claim 22, wherein the one or more processors are further configured to: receive, from the second core network entity and embedded in a sensing session specific container, radio level operation configuration information for the network node; and provide, to the network node, the radio level operation configuration information.
| 24. The first core network entity of claim 22, wherein the first core network entity comprises a session management function (SMF) entity and the second core network entity comprises a non-communication session management function (N-SMF) entity.
| 25. The first core network entity of claim 22, wherein the one or more processors are further configured to: provide, to the network node, sensing information indicating that the virtual communication session is for the sensing service, to be established between the network node and the UE.
| 26. The first core network entity of claim 25, wherein the sensing information includes information indicating one or more sensing session parameters.
| 27. A network node, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: receive, from a first core network entity, a first request associated with initiation of a sensing service associated with a user equipment (UE), wherein the first request includes information identifying one or more sensing session parameters associated with the sensing service; determine, based at least in part on the one or more sensing session parameters and one or more communication session parameters associated with a communication session between the UE and the network node, one or more resources for the sensing service; and transmit, to the UE, information identifying the one or more resources for the sensing service.
| 28. The network node of claim 27, wherein the one or more processors are further configured to: receive, from a second core network entity, a second request including the one or more communication session parameters.
| 29. The network node of claim 27, wherein the one or more processors, to determine the one or more resources, are configured to: determine a first portion of joint communication and sensing resources for the sensing service; determine a second portion of the joint communication and sensing resources for the communication session; and determine, as the one or more resources, the first portion of the joint communication and sensing resources.
| 30. A method of wireless communication performed by a first core network entity, comprising: receiving a first request associated with initiation of a sensing service associated with a user equipment (UE); receiving, from at least one other core network entity and based at least in part on the first request, one or more sensing session parameters associated with the sensing service; and providing, to a second core network entity and based at least in part on the sensing session parameters, a second request to establish a virtual communication session with the UE, the second request including one or more communication session parameters. | The entity has a processor coupled to a memory configured to receive a first request associated with initiation of a sensing service associated with a user equipment (UE) (120) and sensing session parameters associated with the sensing service based on the first request, where the processor provides a second request including communication session parameters to establish a virtual communication session with the UE and the sensing session parameters include one of sensing type parameter, range parameter, range resolution parameter, velocity parameter, velocity resolution parameter, azimuth field of view parameter and angular resolution parameter and the communication session parameters include one of quality-of-service parameter, signal-to-interference-plus-noise ratio (SINR) parameter, data rate parameter or latency parameter. The processor maps the sensing session parameters to the communication session parameters. An INDEPENDENT CLAIM is also included for a method for performing wireless communication by a core network entity. Core network entity for performing wireless communication for facilitating resource management for communication and sensing services. Uses include but are not limited to telephony, video, data, messaging and broadcasts. The entity effectively supports mobile broadband internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum and/or providing new-radio (NR) services. The drawing shows a block diagram of a core network for facilitating resource management for communication and sensing services.100Wireless network120UE420Unified data repository455Message bus605Core network | Please summarize the input |
Location of a suspicious vehicle using V2X communication Location of a suspicious vehicle (SV), implemented within a detection entity (DE), such as another vehicle, the detection entity being distinct from the suspect vehicle, from a V2X communication . FIG. 1|1. [Claim 1] Method for locating a suspect vehicle (SV), implemented within a detection entity (DE), the detection entity being distinct from the suspect vehicle, comprising the steps of:
- reception (3) from a remote server (SRV) of a request for the location of the suspect vehicle, the request including an identification number of the suspect vehicle;
- generation (5) of an identification request message;
- transmission (7) of the message on a V2X network, that is to say by a direct link between the entity and the suspect vehicle;
- upon receipt of a positive response from the suspect vehicle, transmission (17) to the remote server of geolocation information of the entity and/or the suspect vehicle.
| 2. [Claim 2] Method according to claim 1, in which the suspect vehicle is geolocated and in which the identification request message further comprises a request for geolocation of the suspect vehicle, so that the positive response from the suspect vehicle further comprises the geolocation of the vehicle suspicious.
| 3. [Claim 3] Method according to one of the preceding claims, in which the detection entity is a motorized land vehicle and in which the suspect vehicle is geolocated, and further comprising a step of: - calculation (19) of a tracking route for the suspect vehicle by the detection vehicle, the tracking route being configured so that the detection vehicle remains within V2X range of the suspect vehicle while being out of visual range of the suspect vehicle.
| 4. [Claim 4] Method according to claim 3, in which the ability to be out of visual range of the suspect vehicle is determined from at least one of the following elements: ? a first predetermined distance; ? a plurality of second predetermined distances, each second predetermined distance corresponding to a type of geographical area; ? data acquired by a sensor of the detection vehicle;
? data acquired by a sensor of the suspect vehicle.
| 5. [Claim 5] Method according to one of claims 3 or 4, further comprising a step of: - display on a vehicle navigation aid system the tracking route.
| 6. [Claim 6] Method according to one of claims 3 to 5, further comprising a step of: - generation of a configured autonomous driving instruction for autonomous driving to follow the tracking route.
| 7. [Claim 7] Method according to one of claims 1 or 2, in which the detection entity is a road infrastructure element.
| 8. [Claim 8] Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor (200).
| 9. [Claim 9] Device for locating a suspect vehicle, included in a detection entity, the detection entity being distinct from the suspect vehicle, and comprising at least one memory and at least one processor arranged to perform the operations of: - reception from a remote server of a location request lization of the suspect vehicle, the request including an identification number of the suspect vehicle; - generation of an identification request message; - transmission of the message on a V2X network, that is to say by a direct link between the entity and the suspect vehicle; - upon receipt of a positive response from the suspect vehicle, transmission to the remote server of geolocation information of the suspicious entity and/or vehicle.
| 10. [Claim 10] Motorized land vehicle, corresponding to the detection entity, and comprising the device according to claim 9.
1/2 | The method involves receiving request for location of a suspect vehicle from a remote server (3), where the request includes an identification number of the suspect vehicle. An identification request message is generated (5). The message is transmitted (7) on a vehicle-to-everything network by a direct link between a detection entity and the suspect vehicle. Geolocation information of the entity and the suspect vehicle are transmitted (17) to the remote server upon receipt of a positive response from the suspect vehicle. A tracking route for the suspect vehicle is calculated (19) by the detection vehicle, while being out of visual range of the suspect vehicle. INDEPENDENT CLAIMS are also included for:a computer program comprising a set of instructions for locating a suspect vehicle implemented within a detection entity;a device for locating a suspect vehicle implemented within a detection entity; anda motorized land vehicle. Method for locating a suspect vehicle implemented within a detection entity i.e. road infrastructure element, of a motorized land vehicle (all claimed). Uses include but are not limited to a motor vehicle, a moped, a motorcycle and a storage robot in a warehouse. The method enables locating the suspect vehicle within the detection entity, so that interactions of the location method with the suspect vehicle can be reduced to a strict minimum. The drawing shows a flowchart illustrating a method for locating a suspect vehicle implemented within a detection entity.3Step for receiving request for location of a suspect vehicle from a remote server5Step for generating identification request message7Step for transmitting message on a vehicle-to-everything network by a direct link between a detection entity and the suspect vehicle17Step for transmitting geolocation information of the entity and the suspect vehicle to the remote server upon receipt of a positive response from the suspect vehicle19Step for calculating tracking route for the suspect vehicle by the detection vehicle, while being out of visual range of the suspect vehicle | Please summarize the input |
Immobilization of a suspicious vehicle using V2X communication Method and device for immobilizing a suspect vehicle (SV), implemented within a detection entity (DE), the detection entity being distinct from the suspect vehicle, based on V2X communication. FIG. 1|1. Claims
[Claim 1] Method for immobilizing a suspect vehicle (SV), implemented within a detection entity (DE), the detection entity being distinct from the suspect vehicle, comprising the steps of: - reception (3) from a remote server (SRV) of a request immobilization of the suspect vehicle, the request including an identification number of the suspect vehicle; - transmission (7) to the suspect vehicle of a message immobilization, the immobilization message being configured so that the suspect vehicle is immobilized, the message being transmitted over a V2X network, that is to say by a direct link between the entity and the suspect vehicle.
| 2. [Claim 2] Method according to claim 1, further comprising, still at the level of the detection entity, the steps of: - receipt (17) of an acknowledgment of receipt of immobilization from the suspect vehicle; - upon receipt of acknowledgment of receipt, transmission (17) to remote server of immobilization information of the suspect vehicle.
| 3. [Claim 3] Method according to one of the preceding claims, further comprising, at the level of the suspect vehicle, the step of: - upon receipt of the immobilization message, activation of a mode of inhibiting at least one function of a powertrain, the inhibition of the function being configured so that the suspect vehicle is immobilized.
| 4. [Claim 4] Method according to one of the preceding claims, further comprising, at the level of the suspect vehicle, the step of: - upon receipt of the immobilization message, activation of a locking mode of an electronic trajectory corrector, the locking being configured so that the suspect vehicle is immobilized by activation of at least one brake of the vehicle.
| 5. [Claim 5] Method according to one of the preceding claims, further comprising, at the level of the suspect vehicle, the step of: - upon receipt of the immobilization message, activation of a immediate stopping mode of at least one autonomous driving function; - upon activation of the immediate stop mode, generation of an ins- Autonomous driving action configured to make the suspect vehicle stop.
| 6. [Claim 6] Computer program comprising instructions for implementing the method according to any one of claims 1 or 2, when these instructions are executed by a processor (200).
| 7. [Claim 7] Device for locating a suspect vehicle, included in a detection entity, the detection entity being distinct from the suspect vehicle, and comprising at least one memory and at least one processor arranged to perform the operations of: - reception from a remote server of a request immobilization of the suspect vehicle, the request including an identification number of the suspect vehicle; - transmission of a message to the suspect vehicle immobilization, the immobilization message being configured so that the suspect vehicle is immobilized, the message being transmitted over a V2X network, that is to say by a direct link between the entity and the suspect vehicle.
| 8. [Claim 8] Motorized land vehicle, corresponding to the detection entity, and comprising the device according to claim 7. | The method involves performing reception (3) of request from a remote server (SRV) for immobilization of suspect vehicle. The request includes identification number of the suspect vehicle, and an immobilization message is transmitted (7) to the suspect vehicle in response to received request. The immobilization message is configured so that the suspect vehicle is immobilized, and the message is transmitted over V2X network via direct link between entity and suspect vehicle. INDEPENDENT CLAIMS are included for the following:a computer program comprising instructions for immobilizing suspect vehicle;a device for locating suspect vehicle; anda motorized land vehicle. Method for immobilizing suspect vehicle such as motorized land vehicle e.g. motor vehicle, moped, motorcycle. Can also be used in storage robot in warehouse. The stolen vehicle itself detects suspicious activity and transmits alert so that theft of vehicle is prevented. The interactions of immobilization method with the suspect vehicle are reduced to the strict minimum. The drawing shows a flow diagram illustrating the process for immobilizing suspect vehicle. (Drawing includes non-English language text) 1Step for generating request to immobilize suspect vehicle3Step for reception of request from remote server5Step for generating immobilization message7Step for transmitting immobilization message to suspect vehicle9Step for providing direct exchanges possible between entities | Please summarize the input |
Communication method for reinforcing the safety of autonomous driving of a vehicle, vehicle provided with means for implementing this method Communication method for strengthening the safety of autonomous driving of a vehicle (1), this method comprising an exchange of perception data which is limited to what is necessary. In this process, the V2X communication network is only used when the uncertainty on an environmental element (object external to the vehicle (1)) is too great. On the other hand, if the V2X communication network is requested, the corresponding perception request calls for a response which is limited by attributes specifically correlated to the environmental element. Vehicle (1) provided with means (2, 3, 4, 5, 7) for implementing this method. Figure 1|1. Claims
[Claim 1] Communication method for strengthening the safety of autonomous driving of a vehicle (1), this method comprising a) a step of acquiring perception data on an environmental element of the vehicle (1), b) a step of determining an uncertainty on the environmental element, c) a step of evaluating the level of uncertainty by comparing this level of uncertainty to a safety threshold, d) a step of creating a collection request, in which the perception request calls for a response limited by attributes specifically correlated to the environment element, e) a step of transmitting this request to a V2X communication network, f) a step of receiving a response to the request, from the V2X communication network, or taking into account a lack of response to the request, from the V2X communication network, g) a merger step, in the event of a response, collective perception data obtained in response to the perception request, with at least some of the perception data which had initially been obtained in step a), h) a step of returning to step b) for determining d an uncertainty updated with the perception data obtained by the fusion step of step g).
| 2. [Claim 2] A method according to claim 1, wherein the attributes specifically correlated to the environment element are defined by a perception query identity, a relevance region and a type of the environment element.
| 3. [Claim 3] Method according to one of the preceding claims in which step h) returning to step b) is carried out as part of a logic of isolation and correction of fault detection.
| 4. [Claim 4] Method according to one of the preceding claims, in which the security threshold is determined as a function of at least one of the following parameters: the severity of the situation, the recurrence of the situation and the controllability of the situation.
| 5. [Claim 5] Method according to one of the preceding claims, in which step e) of transmitting the perception request to the V2X communication network is carried out before activating an autonomous driving process in minimal risk maneuvering mode.
| 6. [Claim 6] Method according to one of the preceding claims, comprising, prior to step a), a step of filtering on the relevant environmental elements.
| 7. [Claim 7] Vehicle (1) equipped with a driving assistance system (2), sensors (3) configured to collect perception data, means for recording and storing perception data (4), means for calculating and processing perception data (5) configured to determine an uncertainty on an environmental element, and means for exchanging perception data (7) with a V2X network, in which at least some of the perception data are used to control the driving assistance system (2), characterized in that the means for exchanging perception data (7) are only implemented in relation to the environmental element only if the uncertainty on this environmental element is greater than or equal to a predetermined security threshold.
| 8. [Claim 8] Vehicle according to claim 7, in which the means for calculating and processing perception data (5), as well as the means for exchanging perception data (7) are configured to transmit a perception request comprising specifically correlated attributes to the environmental element on which the uncertainty is greater than or equal to the predetermined safety threshold.
| 9. [Claim 9] Computer program comprising program code instructions for executing the method according to one of claims 1 to 6, when said program is executed on a computer, and for triggering an action of said vehicle (1) depending on the updated uncertainty on the environmental element.
| 10. [Claim 10] Distributed computer system comprising an on-board computer (5) for executing the program according to claim 9, as well as on-board data processing means (I) configured to process said perception request. | The method involves acquiring perception data on an environmental element of the vehicle (1). The uncertainty on the environmental element is determined. The level of uncertainty is evaluated by comparing level of uncertainty to a safety threshold. The collection request in which the perception request calls for a response limited by attributes specifically correlated to the environment element is created. The request is transmitted to a V2X communication network. The response to the request is received from the V2X communication network, or taking into account a lack of response to the request from the V2X communication network. The collective perception data is obtained in response to the perception request. The uncertainty updated with the perception data obtained by the fusion step is determined. INDEPENDENT CLAIMS are included for the following:a vehicle;a computer program for strengthening safety of autonomous driving of vehicle; anda distributed computer system for strengthening safety of autonomous driving of vehicle. Communication method for strengthening safety of autonomous driving of vehicle such as motor vehicle. The controllability is the ability of users to maintain control in the event of a failure and indicator is identified based on knowledge of user behavior in the situation. The drawing shows a schematic view of the vehicle. (Drawing includes non-English language text) 1Vehicle2Driving assistance system3Sensor4Perception data5Processing perception data | Please summarize the input |
Method and device for parking a motor vehicle A method of parking a motor vehicle comprises the steps of: acquisition (31) of the environment of a parking space, comprising a sub-step of detection (35) of at least one connected autonomous vehicle parked at the edge of the parking space; sending (37) of a command travel to the connected autonomous vehicle, said movement command comprising movement instructions making it possible to widen the parking space; execution (45) of a parking maneuver after widening the parking space. A parking device and a motor vehicle comprising the device are also described. Figure to be published with the abstract: Fig2|1. Claims
[Claim 1] Method of parking a motor vehicle comprising the steps of: ? acquisition (31) of the environment of a parking space parking, comprising a sub-step of detection (35) of at least one connected autonomous vehicle parked at the edge of the parking space; ? sending (37) a movement command to the vehicle autonomous connected, said movement command comprising movement instructions making it possible to widen the parking space; ? execution (45) of a parking maneuver after widening the parking space.
| 2. [Claim 2] Method according to claim 1, in which the movement command is included in a DENM type V2X message.
| 3. [Claim 3] Method according to claim 1 or 2, wherein the step of acquiring the environment comprises acquiring (39) the environment of the at least one parked autonomous vehicle.
| 4. [Claim 4] Method according to claim 3, wherein the step of acquiring the environment of the at least one parked vehicle comprises receiving a message from said parked vehicle containing possible movement information.
| 5. [Claim 5] Method according to claim 3 or 4, in which the movement command is only sent after determining that the environment of the at least one parked autonomous vehicle allows movement to enlarge the parking space sufficient to allow parking of the vehicle.
| 6. [Claim 6] Method according to one of claims 1 to 5, further comprising a step of sending an information message to the owner of the parked autonomous vehicle.
| 7. [Claim 7] Method according to any one of the preceding claims, comprising a preliminary step of determining the size of the parking space and comparing it with the minimum size necessary to allow parking of the vehicle.
| 8. [Claim 8] Device for parking a motor vehicle (1) comprising: ? means of acquiring (5) the environment of a parking space, comprising detection of at least one connected autonomous vehicle parked at the edge of the space parking ;
? a transmitter (7) of a movement command to the connected autonomous vehicle, said movement command comprising movement instructions making it possible to widen the parking space;
? a controller (3) adapted to control a parking maneuver after widening the parking space.
| 9. [Claim 9]
Motor vehicle comprising a device according to claim 8.
| 10. [Claim 10] Computer program product downloadable from a communications network and/or recorded on a computer-readable medium and/or executable by a processor, characterized in that it comprises program code instructions for implementing the method according to at least one of claims 1 to 7. | The method involves acquiring (31) environment of parking space, and performing sub-step detection (35) of at least one connected autonomous vehicle parked at edge of the parking space. A movement command is send (37) to vehicle, which comprises movement instructions to widen the parking space, and a parking maneuver is executed (45) after widening the parking space. The movement command is included in DENM type V2X message, and the environment of at parked autonomous vehicle is acquired by receiving message from parked vehicle containing possible movement information. INDEPENDENT CLAIMS are included for the following:a device for parking motor vehicle; anda computer program product for parking motor vehicle. Method for parking motor vehicle (claimed) such as car and van. The available location calculation is carried out on sides of vehicles and the widening maneuver by parked vehicles is improved by automation of autonomous vehicles. The drawing shows a flow diagram illustrating the process for parking motor vehicle. 31Step for acquiring environment of parking space35Step for detecting connected autonomous vehicle parked at edge of parking space37Step for sending movement command to vehicle39Step for determining whether movement is possible45Step for executing parking maneuver | Please summarize the input |
Method and device for securing an autonomous vehicle The invention relates to a method and a device for securing a vehicle (10) adapted to travel in an autonomous driving mode. To this end, a communication infrastructure control device (1) detects when the vehicle (10) is stationary on a road. The control device transmits to the vehicle (10) a first request awaiting a response from the driver, according to a vehicle-to-infrastructure communication mode, called V2I. In the absence of a response from the driver to the first request, the control device transmits in V2I one or more driving instructions to the vehicle (10) so that the latter reaches a safety position. Figure for abstract: Figure 1|1. Claims
[Claim 1] Method for securing a vehicle (10), said vehicle (10) being configured to travel in an autonomous driving mode, said method comprising the following steps: - determination (21), by a communication infrastructure control device (1), of a current state of said vehicle (10) representative of a stop of said vehicle on a road; - transmission (22), by said control device, of first data representative of a first request intended for said vehicle (10), said first request requiring a response from a driver of said vehicle (10), said transmission being works according to a vehicle-to-infrastructure communication mode, known as V2I; - transmission (23), by said control device according to said V2I communication mode, of second data representative of at least one driving instruction towards a safety position intended for said vehicle (10) in the event of non-response from said driver to said first request.
| 2. [Claim 2] Method according to claim 1, for which said second data correspond to: - data representative of coordinates of a geolocation system corresponding to said safety position to be reached; and or - data representative of at least one image of said safety position to be reached; and or - data representative of a voice command to be given in said vehicle (10) to control said vehicle (10) towards said safety position to be reached.
| 3. [Claim 3] Method according to claim 1 or 2, further comprising a step of transmission (36), by said control device according to said V2I communication mode, of a second request intended for said vehicle (10), said second request requesting a passage said vehicle (10) from a current level of autonomy to a determined level of autonomy, said determined level of autonomy being greater than said current level of autonomy.
| 4. [Claim 4] Method according to any one of Claims 1 to 3, for which the said first data correspond to: - data representative of a voice message to be delivered in said vehicle (10) requiring an action from said driver in response to said voice message; and or - data representing graphic content to be displayed on a display screen on board said vehicle (10) requiring an action from said driver in response to said displayed graphic content; and/ Where - data representative of an alarm to be returned by an alarm system on board said vehicle and requiring an action from said driver in response to said alarm.
| 5. [Claim 5] Method according to any one of claims 1 to 4, further comprising a step of transmission, by said control device according to said V2I communication mode, of at least one command intended for at least one control system of said vehicle (10) in the event of non-execution of said at least one driving instruction by said vehicle (10).
| 6. [Claim 6] Method according to claim 5, for which said at least one command belongs to a set of commands comprising: - a command to start said vehicle; - A stop command of said vehicle; - A speed control of said vehicle; - A steering control of said vehicle; - A braking control of said vehicle; and - A path control of said vehicle.
| 7. [Claim 7] Method according to one of claims 1 to 6, for which said current state of said vehicle (10) is determined from information representative of the environment of said vehicle (10).
| 8. [Claim 8] Method according to claim 7, for which said information representative of the environment is obtained by said control device from: - at least one sensor (112) of said communication infrastructure (1) configured to acquire data representative of said environment; and or - said vehicle according to said V2I communication mode; and or - at least one other vehicle (11) according to said V2I communication mode.
| 9. [Claim 9] Device (4) for securing a vehicle, said device (4) comprising a memory (41) associated with at least one processor (40) configured for the implementation of the steps of the method according to any one of the claims 1 to 8.
| 10. [Claim 10] System comprising the device (4) according to claim 9 and at least one vehicle (10) configured to travel in an autonomous driving mode. | The method involves configuring vehicle (10) to travel in an autonomous driving mode. The current state of vehicle representative of a stop of vehicle on a road is determined by a communication infrastructure control device (1). The first data representative of a first request intended for vehicle is transmitted by a control device, where the first request requires a response from a driver of vehicle and the transmission works according to a vehicle-to-infrastructure communication mode. The second data representative of driving instruction is transmitted towards a safety position intended for vehicle in the event of non-response from driver to first request, by the control device according to the vehicle-to-infrastructure communication mode. INDEPENDENT CLAIMS are included for the following:a device for securing vehicle; anda system comprising device for securing vehicle. Method for securing vehicle e.g. autonomous vehicle such as motor vehicle, and land vehicle such as truck, bus and motorcycle. The safety of vehicles on the roads is improved. The vehicle is secured, and the safety of vehicles and passengers is increased. The risk of collision with another vehicle is avoided, and the risk of accident or additional accident linked to the presence of stationary vehicle on the road is reduced. The drawing shows a schematic view of communication infrastructure and vehicle. 1Communication infrastructure control device10Vehicle100Cloud of network110,111Communication devices112Camera | Please summarize the input |
Method for managing a convoy comprising at least two motor vehicles in an autonomous driving mode The invention relates to a method for managing a convoy grouping together at least two motor vehicles (1, 4, 5) in an autonomous driving mode, the convoy traveling on a road (3) and being formed by at least one lead vehicle (1) and at least one follower vehicle (4, 5), the method comprising the following steps: - determining, according to a programmed route, if the leading vehicle (1) must change direction and if the time or the distance remaining before a planned change of direction is less than a time or distance threshold ; - send an information message to the vehicles (4, 5, 6) located near the lead vehicle (1) indicating that a change of direction of the lead vehicle (1) must occur soon. Figure for the abstract: Fig. 3|1. Claims
[Claim 1] Method for managing a convoy grouping together at least two motor vehicles (1, 4, 5) in an autonomous driving mode, the convoy traveling on a road (3) and being formed by at least one leading vehicle (1) and at least one follower vehicle (4, 5), the method comprising the following steps: - determining (40), according to a programmed route, if the leading vehicle (1) must change direction and if the time or the distance remaining before a planned change of direction is less than a time threshold or distance; - Transmit (42) an information message to vehicles (4, 5, 6) located close to the lead vehicle (1) indicating that a change of direction of the lead vehicle (1) is due soon.
| 2. [Claim 2] Method according to the preceding claim, in which, when the information message is received by a follower vehicle (4, 5) of the convoy, this vehicle determines (44) whether the change of direction of the lead vehicle (1) is compatible with a programmed route of the following vehicle (4, 5).
| 3. [Claim 3] Method according to the preceding claim, in which, if it is determined that the programmed route of a following vehicle is incompatible with the change of direction of the leading vehicle, the following vehicle performs (46) at least one of the actions following; - search for a compatible lead vehicle; - maintenance of autonomous driving mode; - stopping autonomous driving mode.
| 4. [Claim 4] Method according to the preceding claim, in which the stopping of the autonomous driving mode is preceded by an alert message sent to the attention of the driver of the following vehicle (4, 5) concerned, the message being for example of the visual type and/or sound.
| 5. [Claim 5] Method according to one of the preceding claims, in which the step of transmitting (42) an information message is implemented by means of a wireless communication module on board the lead vehicle and compatible with the standard V2X.
| 6. [Claim 6] Method according to the preceding claim, in which the wireless communication module on board the lead vehicle is compatible with one or more of the following wireless communication protocols: IEEE 802.lip, ETSIITS-G5, Wifi?, Bluetooth?, GSM 3G-4G-5G, C, LTE.
| 7. [Claim 7] Method according to one of the preceding claims, in which the step of determining (40) whether the lead vehicle (1) must change direction is carried out by a computer on board the lead vehicle (1), as a function of information provided by a geolocation module (14) on board the lead vehicle (1).
| 8. [Claim 8] Method according to one of the preceding claims, in which the steps of determining (40) whether the lead vehicle (1) should change direction and of transmitting (42) an information message are implemented so that the information message can be transmitted before the leading vehicle (1) begins the change of direction maneuver.
| 9. [Claim 9] Method according to the preceding claim, in which the duration between the transmission of an information message and the start of the maneuver to change direction of the leading vehicle (1) is greater than or equal to a threshold duration, the threshold duration being at least equal to 10 seconds.
| 10. [Claim 10] Computer program product comprising instructions which, when the program is executed by one or more processor(s), cause the latter(s) to implement the steps of the method in accordance with one of Claims 1 to 9.
1/2 | The method involves determining if a leading vehicle (1) changes direction and if time or distance remaining before a planned change of direction is less than a time threshold or distance. An information message is transmitted to vehicles (4-6) located close to the lead vehicle indicating that change of direction of the lead vehicle is due soon, where the vehicle determines whether the change of direction of the lead vehicle is compatible with a programmed route of the following vehicle when the information message is received by the follower vehicle of a convoy. The leading vehicle is compatible with wireless communication protocols e.g. IEEE 802.lip protocols, ETSIITS-G5 protocols, Wifi protocols, Bluetooth protocols, GSM 3G protocols-4G protocols-5G protocols and LTE protocols. An INDEPENDENT CLAIM is also included for a computer program product comprising a set of instructions for managing a convoy of motor vehicles. Method for managing a convoy of motor vehicles i.e. autonomous or partially autonomous motor vehicles, traveling on a road. The method enables managing the convoy to manage a situation in which the leading vehicle is brought to change direction in complete safety so as to improve the management of driving in the convoy of autonomous vehicles. The drawing shows a schematic view of a portion of a road.1Leading vehicle4-6Vehicles18Sensors30, 32Traffic lanes38Exit lane | Please summarize the input |
Method and device for controlling an autonomous vehicleThe invention relates to a method and a device for controlling an autonomous vehicle (10). To this end, first information representing an environment of the vehicle (10) is obtained, the environment comprising a set of elements (11, 12, 13, 14). At least a portion of the environment is subdivided into a plurality of cells (211-217, 221-225, 231-238). For each cell, a value representative of a level of nuisance weighing on the vehicle (10) is determined from the first information, second information representative of the vehicle (10) and third information representative of a level of nuisance associated with each element (11, 12) of at least part of the set of elements. The vehicle (10) is controlled according to the values ??representative of the level of nuisance. Figure for the abstract: Figure 2|1. Claims
[Claim 1] A method of controlling an autonomous vehicle (10), said method comprising the following steps: - obtaining (51) first information representative of an environment (1) of the vehicle (10), said environment (1) comprising a set of elements (11, 12, 13, 14) comprising at least one element, at least a part of the first information being representative of said at least one element; - subdivision (52) of at least part of said environment into a plurality of cells (211 to 217, 221 to 225, 231 to 238); - for each cell, determination (53) of a value representative of a level of nuisance weighing on said autonomous vehicle (10) from said first information and second information representative of said autonomous vehicle (10), said determination of a value representative of a level of nuisance weighing on said autonomous vehicle (10) further being a function of third information representative of a level of nuisance associated with each element (11, 12) of at least one part said set of elements; - control of said autonomous vehicle (10) according to said values ??representative of the level of nuisance.
| 2. [Claim 2] A method according to claim 1, further comprising a step of representing a dynamic environment of said autonomous vehicle (10) based on said cells (211 to 217, 221 to 225, 231 to 238) and said values ??associated with said cells (211 to 217, 221 to 225, 231 to 238).
| 3. [Claim 3] Method according to claim 1 or 2, for which said set of elements comprises at least one element among the following elements: - static object; and/or - moving object; and/or - floor markings; and/or - traffic information; and/or - signaling device; and/or - hole in the road.
| 4. [Claim 4] Method according to claim 1, for which said nuisance level is a function of: - a kinetic energy resulting in the event of collision of said autonomous vehicle (10) with an element of said assembly; and or - a braking force resulting from braking of said autonomous vehicle (10); and or - a centrifugal force resulting from a change of direction of said autonomous vehicle (10); - a set of traffic rules; and or - information representative of a determined path for said autonomous vehicle (10).
| 5. [Claim 5] Method according to claim 1 or 2, for which said first information belongs to a set of information comprising: - position representative information; - information representative of a type of element; - representative size information; - kinematic information of the associated element; - information representative of meteorological conditions; - information representative of traffic rules; - information representative of trajectory; - traffic information; - information representative of traffic conditions.
| 6. [Claim 6] Method according to any one of Claims 1 to 3, for which the said second information belongs to a set of information comprising: - information representative of position; - Kinematic information of said autonomous vehicle; - information representative of the trajectory of said autonomous vehicle.
| 7. [Claim 7] Method according to any one of claims 1 to 4, for which said first information is obtained from at least one sensor of a detection system on board said autonomous vehicle (10) and/or from at least one element of said assembly of elements (11, 12, 13, 14) according to a vehicle-to-everything type communication mode, called V2X.
| 8. [Claim 8] Method according to one of claims 1 to 7, for which said step of controlling the autonomous vehicle comprises determining information representative of the trajectory of said autonomous vehicle (10) as a function of said cells (211 to 217, 221 to 225, 231 to 238) and associated representative nuisance values.
| 9. [Claim 9] Device (4) configured to control an autonomous vehicle, said device (4) comprising a memory (41) associated with at least one processor (40) configured to implement the steps of the method according to any one of Claims 1 to 8.
| 10. [Claim 10] Autonomous vehicle (10) comprising the device (4) according to claim 9. | The method involves obtaining first information representative of an environment of an autonomous vehicle (10), the environment comprises a set of elements e.g. Hole (13) and road sign (14). A part of the environment is subdivided into a set of cells (211-217). A value representative of a level of nuisance weighing is determined on the autonomous vehicle from the information and second information representative of the autonomous vehicle. The value representative of the level of nuisance weighing is determined on the autonomous vehicle from a function of third information representative of a level of nuisance associated with each element of the set of elements. The autonomous vehicle is controlled according to the values representative of the level of nuisance. INDEPENDENT CLAIMS are also included for:a device for controlling an autonomous vehicle; andan autonomous vehicle. Method for controlling a kinematic parameter e.g. speed and acceleration, a trajectory, a braking system and a safety system of an autonomous vehicle (claimed) i.e. autonomous land motor vehicle. The method enables controlling the autonomous vehicle so as to improve representation of the environment of the vehicle and to improve the decision-making of the vehicle in the context of autonomous driving. The drawing shows a schematic view representing a spatial subdivision of an environment.10Autonomous vehicle13Hole14Road sign101-103Three lanes of traffic211-217Cells | Please summarize the input |
Method for updating road signs by an autonomous vehicle The invention relates to a method and device for updating road signs by an autonomous vehicle traveling in a road environment, comprising the steps of: - detection (42), by the autonomous vehicle, of a presence or of an absence of a road sign element from the road environment; - emission (43), by the autonomous vehicle, of information representing a result of the detection, - reception (44) of said information by a device remote from an infrastructure of a communication network and; - updating (45) of the road signs by the remote device according to said information received. Figure for abstract: Figure 4|1. Claims
[Claim 1] Method for updating road signs by an autonomous vehicle traveling in a road environment, comprising steps of: - Detection (42), by the autonomous vehicle, of a presence or absence of a road sign element of the road environment; - emission (43), by the autonomous vehicle, of information representing a result of the detection, - reception (44) of said information by a device remote from an infrastructure of a communication network and; - updating (45) of the road signs by the remote device as a function of said information received.
| 2. [Claim 2] Method according to claim 1, which further comprises a step of transmitting said information by the autonomous vehicle to another autonomous vehicle.
| 3. [Claim 3] A method according to claim 2, wherein said information is transmitted from the autonomous vehicle to the other autonomous vehicle according to vehicle-to-vehicle communication.
| 4. [Claim 4] Method according to claim 2 or 3, which further comprises a step of transmitting said information by said other autonomous vehicle and intended for the remote device.
| 5. [Claim 5] Method according to one of claims 1 to 4, for which said information is transmitted from an autonomous vehicle to the remote device according to a vehicle-to-infrastructure communication.
| 6. [Claim 6] Method according to one of claims 1 to 5, which further comprises, prior to the steps of detecting and transmitting by the autonomous vehicle, a step of transmitting (41), by the remote device, of a request for obtaining an update of the road signs and a step of receiving said request by the autonomous vehicle.
| 7. [Claim 7] A method according to claim 6, wherein said request informs the autonomous vehicle that a road sign element is present at a particular location.
| 8. [Claim 8] Device for updating road signs by an autonomous vehicle traveling in a road environment, comprising a memory associated with at least one processor configured for implementing the steps of the method according to any one of the claims
| 9. [Claim 9] 1 to 7. Computer program product comprising instructions suitable for executing the steps of the method according to one of claims 1 to 7, when the computer program is executed by at least one
| 10. [Claim 10] processor. Computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the method according to one of claims 1 to 7. | The method (400) involves detection (42) of a presence or absence of a road sign element of the road environment by the autonomous vehicle. Information is emitted (43) that represents a result of the detection by the autonomous vehicle. An information is received (44) by a device remote from an infrastructure of a communication network. The road signs are updated (45) by the remote device as a function of information received. Information is transmitted by the autonomous vehicle to another autonomous vehicle. INDEPENDENT CLAIMS are included for the following:a device for updating road signs by an autonomous vehicle traveling in a road environment; anda computer program product for updating road signs by an autonomous vehicle traveling in a road environment. Method for updating road signs by an autonomous vehicle traveling in a road environment. Method avoids the congestion of the communication network and overloading of the remote device by avoiding the systematic sending of information representing detection results by autonomous vehicles as soon as they detect an element of road signs along their route. The drawing shows a flow chart of a method for updating road signs by an autonomous vehicle traveling in a road environment. (Drawing includes non-English language text). 42Detection of a presence or absence of a road sign element of the road environment by the autonomous vehicle43Emitting the information that represents a result of the detection by the autonomous vehicle44Receiving the information by a device remote from an infrastructure of a communication network45Updating the road signs by the remote device as a function of information received400Method | Please summarize the input |
Vehicle communication method and device The invention relates to a communication method and device for vehicles (10 and 11). To this end, information representative of the arrival of a second vehicle (11), traveling on a traffic lane, is transmitted to the first vehicle (10) via a wireless link of the vehicle-to-vehicle type. At least one guidance instruction is then determined so that the first vehicle (10) allows the second vehicle (11) to pass. Figure for abstract: Figure 1|1. Claims [Claim 1] Communication method for a vehicle, said method being implemented by a first vehicle (10), said method comprising the following steps: - reception (31) of information representative of an approach of a second vehicle (11) on a traffic lane of said first vehicle (10) according to a vehicle-to-vehicle type communication mode, V2V; - determination (32) of at least one instruction for guiding said first vehicle (10) in order to allow passage to said second vehicle (11).
| 2. [Claim 2] The method of claim 1, further comprising a step of rendering said at least one guidance instruction, to rendering means associated with said first vehicle (10).
| 3. [Claim 3] A method according to claim 1 or 2, wherein said first vehicle (10) implements said at least one guidance instruction in autonomous driving level 3 or higher.
| 4. [Claim 4] Method according to any one of claims 1 to 3, for which said at least one guidance instruction comprises at least one command representative of a movement of said first vehicle (10) with respect to said traffic lane.
| 5. [Claim 5] Method according to any one of the preceding claims, for which said at least one guidance instruction is further dependent on information representative of traffic conditions and / or on information representative of the speed of said first vehicle.
| 6. [Claim 6] Method according to any one of the preceding claims, for which said second vehicle (11) corresponds to: - a priority vehicle; and or - a two-wheeled vehicle.
| 7. [Claim 7] Method according to any one of the preceding claims, for which said information representative of an approach of a second vehicle (11) is included in at least one message of CAM and / or DENM type.
| 8. [Claim 8] Device (2) comprising a memory (21) associated with at least one processor (20) configured for implementing the steps of the method according to any one of claims 1 to 7.
| 9. [Claim 9] Vehicle (10) comprising the device (3) according to claim 8.
| 10. [Claim 10] Computer program product comprising instructions suitable for executing the steps of the method according to one of claims 1 to 7, when the computer program is executed by at least one processor.
1/2 | The method involves receiving the information representative of an approach of second vehicle (11) on a traffic lane of first vehicle (10) according to a vehicle-to-vehicle (V2V) type communication mode. The instruction is determined for guiding first vehicle in order to allow passage to second vehicle. The guidance instruction is rendered to a rendering unit associated with first vehicle. The first vehicle implements the guidance instruction in autonomous driving level 3 or higher. The guidance instruction includes a command representative of a movement of first vehicle with respect to the traffic lane. The guidance instruction is dependent on information representative of traffic conditions and/or on information representative of the speed of first vehicle. INDEPENDENT CLAIMS are included for the following:a communication device; anda computer program product. Communication method for a vehicle (claimed), such as an ambulance, fire engine and police vehicle. The safety on the roads is improved. The free passage of priority vehicles is facilitated. The drawing shows a schematic view of a first vehicle traveling on a traffic lane of a road. 1Road environment10First vehicle11Second vehicle101,102Communication devices1000Road | Please summarize the input |
System and method for projecting a trajectory of an autonomous vehicle onto a road surfaceThe invention claims a system and a method for projecting a current track of an autonomous vehicle onto a road surface In some embodiments, claims an autonomous vehicle with light projector, wherein the light projector is on the top surface the autonomous vehicle. In addition, in some embodiments, the autonomous vehicle may include an electronic control unit, which is used for controlling the operation of the light projector, wherein the electronic control unit detects whether the autonomous vehicle is started. In other embodiments, the electronic control unit receives the data of the environment condition around the autonomous vehicle and receiving the track of the imminent occurrence of the autonomous vehicle. the electronic control unit further can project the light from the light projector to the road, surface the track of the autonomous vehicle to appear.|1. A system for projecting a trajectory of an autonomous vehicle onto a road surface, the system comprising: a light projector on the top surface the autonomous vehicle, and an electronic control unit, which is used for controlling the operation of the light projector, wherein the electronic control unit: detecting whether the autonomous vehicle is activated; If the autonomous vehicle is detected: receiving data of the environment condition around the autonomous vehicle, wherein the environment condition is based on the shape of a part of the object in the image corresponding to the pedestrian, pixel intensity and line to indicate the presence of the upcoming turning track and the pedestrian; receiving data of an imminent track of the autonomous vehicle; adjusting the upcoming trajectory based on the environmental condition; and indicating the light projector to indicate the autonomous vehicle to the front appointed distance and the turning direction of the turning direction of the light beam and text is projected surface the road, the projected light beam indicates the track of the autonomous vehicle is to appear, and indicating the light projector after turning projection three-dimensional fence, The three-dimensional fence is perpendicular to the light beam and the text.
| 2. The system according to claim 1, wherein the data of the track to be present of the autonomous vehicle comprises: GPS data and at least one of the received data of the environmental condition.
| 3. The system according to claim 2, wherein the data of the environment condition comprises: Traffic information, road sign information object detection and road condition information
| 4. The system according to claim 3, wherein the data of the environmental condition is collected by at least one of a camera, a sensor, a navigation system, a vehicle communication system, a vehicle-to-infrastructure communication system, and a laser scanner
| 5. The system according to claim 2, further comprising a vehicle communication module, wherein the vehicle communication module is configured to transmit and receive GPS data and data of the environmental condition to different autonomous vehicles.
| 6. The system according to claim 1, wherein each of the projected beams comprises: straight arrow, turning arrow, inclined arrow, projection of at least one of character and number.
| 7. The system according to claim 6, wherein each of the projected beams further comprises: The projection of the current speed of the autonomous vehicle.
| 8. The system according to claim 1, wherein at least one of the projected beams comprises: the projection of the fence, for indicating the parking area of the autonomous surface on the road.
| 9. The system according to claim 1, wherein the light projector comprises: It comprises light source of light emitting diode or laser diode.
| 10. The system according to claim 1, wherein, when the autonomous vehicle detects the presence of a pedestrian, the light beam is projected surface the road.
| 11. The system according to claim 10, wherein the system comprises a vehicle-to-vehicle communication system for alerting a nearby vehicle of the possibility of the presence of the pedestrian.
| 12. The system according to claim 1, wherein the electronic control unit provides an audible notification when the vehicle determines that the upcoming track will collide with an object or a pedestrian.
| 13. The system according to claim 12, wherein when the electronic control unit determines that an object or pedestrian in a certain distance from the autonomous vehicle is collided, the audible notification is provided.
| 14. The system according to claim 1, wherein the light beam from the light projector is projected from 2 feet to 20 feet in front of the autonomous vehicle.
| 15. The system according to claim 1, wherein the light beam from the light projector is projected from 2 feet to 20 feet on the side of the autonomous vehicle.
| 16. A method for projecting a trajectory of an autonomous vehicle onto a road surface, the method comprising: detecting whether the autonomous vehicle is activated; If the autonomous vehicle is detected: receiving data of the environment condition around the autonomous vehicle, wherein the environment condition is based on the shape of a part of the object in the image corresponding to the pedestrian, pixel intensity and line to indicate the presence of the upcoming turning track and the pedestrian; receiving data of an imminent track of the autonomous vehicle; adjusting the upcoming trajectory based on the environmental condition; and indicating the light projector to indicate the autonomous vehicle to the front appointed distance and turning direction of the turning direction of the light beam and text is projected surface the road, the projected light beam indicates the track of the autonomous vehicle will appear, and indicating the light projector after turning projection three-dimensional fence, The three-dimensional fence is perpendicular to the light beam and the text.
| 17. The method according to claim 16, wherein the track to be present comprises: expected path of the autonomous vehicle.
| 18. The method according to claim 16, wherein the data of the imminent track of the autonomous vehicle is determined by at least one of the GPS data and the received data of the environment condition, the data of the environment condition comprises: Traffic information, road sign information object detection and road condition information
| 19. The method according to claim 16, wherein each of the projected beams comprises: straight arrow, turning arrow, inclined arrow, projection of at least one of character and number.
| 20. The method according to claim 16, wherein at least one of the projected beams comprises: the projection of the fence, for indicating the parking area of the autonomous surface on the road.
| 21. The method according to claim 16, further comprising: displaying the upcoming track on the screen in front of the driver of the autonomous vehicle.
| 22. The method according to claim 16, wherein the light beam from the light projector is projected on the surface of the road, from 2 feet to 20 feet in front of the autonomous vehicle. | The system (100) has a light projector (120) arranged on a top surface of an autonomous vehicle. An electronic control unit (140) controls operation of the light projector, and detects whether the autonomous vehicle is turned on, receives data of an environmental condition surrounding the autonomous vehicle, receives an upcoming trajectory path of the autonomous vehicle and projects a light from the light projector onto a surface of a road indicating the upcoming trajectory path of the autonomous vehicle, where the upcoming trajectory path of the autonomous vehicle is determined by one of received global positioning system data and the received data of the environmental condition. An INDEPENDENT CLAIM is also included for a method for projecting current trajectory path of an autonomous vehicle on a surface of road. System for projecting current trajectory path of an autonomous vehicle on a surface of road. The system allows autonomous vehicles to decrease traffic collision caused by human errors. The system allows the autonomous vehicles with enhanced driving control systems and safety mechanisms to ensure reliability and safety of the autonomous vehicles. The drawing shows a block diagram of a system for projecting current trajectory path of an autonomous vehicle on a surface of road. 100System for projecting current trajectory path of autonomous vehicle on surface of road120Light projector140Electronic control unit160Switch162Camera | Please summarize the input |
AN IMPROVED PERFORMANCE AND COST GLOBAL NAVIGATION SATELLITE SYSTEM ARCHITECTURESignificant, cost-effective improvement is introduced for Position, Navigation, and Timing (PNT) on a global basis, particularly enhancing the performance of Global Navigation Satellite Systems (GNSS), an example of which is the Global Positioning System (GPS). The solution significantly improves performance metrics including the accuracy, integrity, time to acquire, interference rejection, and spoofing protection. A constellation of small satellites employing a low-cost architecture combined with improved signal processing yields an affordable enabler for spectrum-efficient transportation mobility. As air traffic management modernization transitions to a greater dependence on satellite positioning, the solution provides aviation users new protections from both intentional and unintentional interference to navigation and surveillance. And in response to an era in which intelligent transportation is under development for automobiles, reliable where-in-lane positioning enables new applications in connected and autonomous vehicles. New military capability increases PNT availability.I claim:
| 1. A method for supporting resilient carrier phase positioning of user devices connected by respective communication links to at least one service data processor, measurements received from Global Navigation Satellite System (GNSS) satellites, and measurements received from low Earth orbit (LEO) satellites, said measurements including carrier phase pseudorange information, comprising the steps of: (a) the at least one service data processor accepting said measurements received from (i) at least one of said GNSS satellites by at least one LEO satellite, (ii) at least one of said GNSS satellites and the at least one LEO satellite by at least one ground reference station, and/or (iii) at least one other LEO satellite by the at least one LEO satellite via a LEO-to-LEO crosslink transmission; (b) the at least one service data processor generating precise orbit and clock predictions for the at least one LEO satellite from available said pseudorange infomiation; and (c) the at least one service data processor disseminating said predictions over said communications links to the user devices to enable the user devices to take into account the precise orbit and clock predictions when computing respective positions of the user devices upon receiving measurements from GNSS and LEO satellites.
| 2. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein (a) the at least one service data processor accepts said measurements received from (i) at least one of said GNSS satellites by the at least one LEO satellite and (ii) at least one of said GNSS satellites and the at least one LEO satellite by the at least one ground reference station and Date Recue/Date Received 2021-04-28 (b) the at least one service data processor (i) generates the orbit predictions from said pseudorange information received from at least one of said GNSS satellites by the at least one LEO satellite and (ii) generates the clock predictions from said pseudorange information received from at least one of said GNSS satellites and the at least one LEO satellite by the at least one ground reference station.
| 3. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein said measurements received from the at least one other LEO satellite by the at least one ground reference station are from configurations wherein the at least one ground reference station is outside the footprint of the at least one LEO satellite.
| 4. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein measurements received from LEO satellites by ground reference stations are unavailable.
| 5. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein measurements received from GNSS satellites by LEO satellites are unavailable.
| 6. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein (i) said at least one LEO satellite includes an oscillator of known stability coupled coherently to a receiver for use in measuring carrier phase pseudorange information from said GNSS satellites or from other said LEO satellites and a transmitter for use in broadcasting carrier phase to be received by said ground reference stations and (ii) the at least one user device endures loss of one or more clock predictions due to disablement of satellites, ground reference stations, service data processors, or data dissemination means via which the clock predictions are received. Date Recue/Date Received 2021-04-28
| 7. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein said at least one service data processor is integrated into a WAAS master station or a precise point positioning network operations center.
| 8. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 1, wherein said disseminating step is accomplished using SBAS satellites, Inmarsat Narrowband, NDGPS data broadcast, VHF aviation radio, 4G LTE, DOT ITS V2I 5.9 GHz standard broadcast, or said LEO satellites.
| 9. A method for supporting resilient carrier phase positioning of user devices utilizing at least one service data processor connected to the user devices by respective communication links, measurements received from GNSS satellites, and measurements received from LEO satellites, said measurements including carrier phase pseudorange information, comprising the steps of: (a) the user devices accepting precise orbit and clock predictions disseminated by the at least one service data processor for at least one LEO satellite, said precise orbit and clock predictions being generated from available pseudorange information accepted by the at least one service data processor received from (i) at least one GNSS satellite by at least one LEO satellite, (ii) at least one GNSS satellite and the at least one LEO satellite by at least one ground reference station, and/or (iii) LEO-to-LEO crosslink transmissions between at least one other LEO satellite and the at least one LEO satellite; and b) the user devices taking into account the precise orbit and clock predictions disseminated by the at least one service data processor when computing respective positions of the user devices upon receiving respective said measurements from GNSS and LEO satellites. Date Recue/Date Received 2021-04-28
| 10. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein (i) the precise orbit predictions are generated from pseudorange information accepted by the at least one service data processor received from at least one GNSS satellite by the at least one LEO satellite and (ii) the precise clock predictions are generated from pseudorange information received from at least one GNSS satellite and the at least one LEO satellite by the at least one ground reference station.
| 11. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein the pseudorange information accepted from the at least one other LEO satellite by the at least one ground reference station is from configurations wherein the at least one ground reference station is outside the footprint of the at least one LEO satellite.
| 12. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein pseudorange information received from LEO satellites by ground reference stations is unavailable.
| 13. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein pseudorange information received from GNSS satellites by LEO satellites is unavailable.
| 14. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein (i) said at least one LEO satellite includes an oscillator of known stability coupled coherently to a receiver for use in measuring carrier phase pseudorange information from said GNSS satellites or from other said LEO satellites and a transmitter for use in broadcasting carrier phase to be received by said ground reference stations and (ii) the at least one user device endures loss of one or more clock predictions Date Recue/Date Received 2021-04-28 due to disablement of satellites, ground reference stations, service data processors, or data dissemination means via which the clock predictions are received.
| 15. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, further comprising the step of employing Receiver Autonomous Integrity Monitoring (RAIM) to weight a fusion of other sensors selected from at least one camera, lidar receiver, or radar receiver.
| 16. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, further comprising the step of forming coherent cross- correlations across at least one pair of satellites to combat potential interference.
| 17. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein said GNSS and LEO satellites have known oscillator stabilities, and further comprising the step of receiving precise clock predictions of the GNSS and LEO satellites from the at least one service data processor and enduring subsequent loss of one or more clock predictions due to disablement of ground reference stations, service data processors, or data dissemination means via which the clock predictions are received.
| 18. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein the method is carried out despite enduring subsequent loss of one or more clock predictions due to disablement of ground reference stations, service data processors, or data dissemination means therebetween or therefrom.
| 19. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, wherein said at least one LEO satellite is included in a constellation of Date Recue/Date Received 2021-04-28 said LEO satellites that minimize the number of required PRN codes through PRN code re-use.
| 20. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, further comprising the steps of: (a) the user device, at such time as it is moving, receiving broadcasting signals from one or more terrestrial, free- running, pre- surveyed pseudolites of known oscillator stability and measuring carrier phase pseudorange information therefrom, and (b) incorporating the pre-surveyed locations and known oscillator stability of said pseudolites in said position computation.
| 21. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 20, wherein said pseudolites broadcast in the 5.9 GHz band.
| 22. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 20, wherein some or all of said pseudolites are mounted at street level.
| 23. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 20, wherein some or all of said pseudolites are mounted at an elevated position relative to said at least one user device.
| 24. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 9, further comprising the steps of: (a) receiving pseudorange information from multi-band LEO, single-band LEO, and GNSS satellites; (b) collecting service data processor precise orbit and clock predictions of both the LEO and GNSS satellites and road-specific ionosphere and troposphere estimates; Date Recue/Date Received 2021-04-28 (c) applying said road-specific estimates to correct said single- band LEO satellite pseudoranges.
| 25. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 24, wherein one or more of said single-band LEO satellite signals are broadcast in the band centered at 1,575,420,000 Hz.
| 26. A method for supporting resilient carrier phase positioning of user devices as claimed in claim 24, wherein one or more of said single-band LEO satellite signals are broadcast in the band spanning 1,616,000,000 to 1,626,500,000 Hz.
| 27. A service data processor for supporting resilient carrier phase positioning of user devices utilizing at least one service data processor connected to the user devices by respective communication links, measurements received from GNSS satellites, and measurements received from LEO satellites, said measurements including carrier phase pseudorange information, comprising: (a) means for accepting said measurements from (i) at least one of said GNSS satellites by at least one LEO satellite (ii) at least one of said GNSS satellites and said at least one LEO satellite by at least one ground reference station and/or (iii) at least one other LEO satellite to the at least one LEO satellite via a LEO-to-LEO crosslink transmission; (b) means for generating precise orbit and clock predictions for the at least one LEO satellite from available said pseudorange information received by the at least one LEO satellite; and (c) means for disseminating said predictions to the user devices over the communications links to enable the user devices to take into account the precise orbit and clock predictions when computing respective positions of Date Recue/Date Received 2021-04-28 the user devices upon receiving respective said measurements from GNSS and LEO satellites. 28. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein (a) the accepted measurements are received from (i) at least one of said GNSS satellites by the at least one LEO satellite and (ii) at least one of said GNSS satellites and the at least one LEO satellite by the at least one ground reference station; and (b) the generated orbit predictions are from said pseudorange information received from at least one of said GNSS satellites by the at least one LEO satellite, and the generated clock predictions are from said pseudorange information received from at least one of said GNSS satellites and the at least one LEO satellite by the at least one ground reference station. 29. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein the measurements received from the at least one other LEO satellite by the at least one ground reference station are from configurations wherein the at least one ground reference station is outside the footprint of the at least one LEO satellite. 30. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein measurements received from LEO satellites by ground reference stations are unavailable. 31. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein measurements received from GNSS satellites by LEO satellites are unavailable. Date Recue/Date Received 2021-04-28
| 32. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein (i) said at least one LEO satellite includes an oscillator of known stability coupled coherently to a receiver for use in measuring carrier phase pseudorange information from said GNSS satellites or from other LEO satellites and a transmitter for use in broadcasting carrier phase to be received by said ground reference stations and (ii) the at least one user device endures loss of one or more clock predictions due to disablement of satellites, ground reference stations, service data processors, or data dissemination means via which the clock predictions are channeled.
| 33. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein said service data processor is spaceborne.
| 34. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 33, further including coupled transmitters and receivers provided in an integrated circuit chipset hosted by said LEO satellite.
| 35. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein said at least one service data processor is integrated into a WAAS master station or a precise point positioning network operations center.
| 36. A service data processor for supporting resilient carrier phase positioning of user devices as claimed in claim 27, wherein said disseminating means utilizes SBAS satellites, Inmarsat Narrowband, NDGPS data broadcast, VHF aviation radio, 4G LTE, DOT ITS V2I 5.9 GHz standard broadcast, or said LEO satellites.
| 37. A user device supported by at least one service data processor, the at least one service data processor connected to a plurality of user devices by respective Date Recue/Date Received 2021-04-28 communication links, to utilize measurements received from GNSS satellites and measurements received from LEO satellites in order to compute a position of the user device, said measurements including carrier phase pseudorange information, comprising: (a) accepting means for accepting precise orbit and clock predictions disseminated by the at least one service data processor for at least one LEO satellite, the precise orbit and clock predictions being generated from available pseudorange information accepted by the at least one service data processor received from (i) at least one GNSS satellite by the at least one LEO satellite, (ii) at least one GNSS satellite and the at least one LEO satellite by at least one ground reference station, and/or (iii) at least one other LEO satellite by the at least one LEO satellite as a LEO-to-LEO crosslink transmission; and (b) computing means for computing the position of the user device by taking into account the precise orbit and clock predictions when computing the position upon receiving said measurements from GNSS and LEO satellites.
| 38. A user device supported by at least one service data processor as claimed in claim 37, wherein (i) the precise orbit predictions are generated from pseudorange information accepted by the at least one service data processor received from at least one GNSS satellite by the at least one LEO satellite and (ii) the precise clock predictions are generated from pseudorange information accepted by the at least one service data processor received from the at least one LEO satellite by the at least one ground reference station.
| 39. A user device supported by at least one service data processor as claimed in claim 37, wherein the pseudorange information received from the at least one other LEO satellite by the at least one ground reference station is from configurations wherein the at least one ground reference station is outside the footprint of the at least one LEO satellite. Date Recue/Date Received 2021-04-28
| 40. A user device supported by at least one service data processor as claimed in claim 37, wherein pseudorange information received from LEO satellites by ground reference stations is unavailable.
| 41. A user device supported by at least one service data processor as claimed in claim 37, wherein received from GNSS satellites by LEO satellites is unavailable.
| 42. A user device supported by at least one service data processor as claimed in claim 37, said at least one LEO satellite includes an oscillator of known stability coupled coherently to a receiver for use in measuring carrier phase pseudorange information from said GNSS satellites or from other LEO satellites and a transmitter for use in broadcasting carrier phase to be received by said ground reference stations and (ii) the at least one user device endures loss of one or more clock predictions due to disablement of satellites, ground reference stations, service data processors, or data dissemination means via which the clock predictions are received.
| 43. A user device supported by at least one service data processor as claimed in claim 37, wherein said computing means is coupled to a Receiver Autonomous Integrity Monitoring (RAIIVI) device.
| 44. A user device supported by at least one service data processor as claimed in claim 37, wherein said computing means is coupled to means for employing said RAIM device to weight the fusion of other sensors.
| 45. A user device supported by at least one service data processor as claimed in claim 44, wherein said other sensors include at least one of a camera and a lidar or radar receiver. Date Recue/Date Received 2021-04-28
| 46. A user device supported by at least one service data processor as claimed in claim 37, wherein LEO signals broadcast from each said LEO satellite to each said ground reference station and said user device use frequency bands that are the same as those used by GNSS satellites.
| 47. A user device supported by at least one service data processor as claimed in claim 46, wherein said LEO signals are consistent with legacy or modern GNSS PRN codes.
| 48. A user device supported by at least one service data processor as claimed in claim 47, wherein said GNSS PRN codes are selected from the following GNSS PRN codes: GPS C/A, GPS P(Y), GPS M, GPS M', GPS L5, GPS L2C, GPS Ll C, Galileo El, Galileo E5a, Galileo E5b, Galileo E5, and Galileo E6.
| 49. A user device supported by at least one service data processor as claimed in claim 46, wherein said LEO satellite signals are codes generated by a 128-bit AES counter producing a chipping rate of an integer multiple of 1,023,000 chips per second.
| 50. A user device supported by at least one service data processor as claimed in claim 37, further comprising means for: (a) the user device in motion receiving signals broadcast by one or more terrestrial, free-running, pre-surveyed pseudolites of known oscillator stability, the signals from the pseudolites including carrier phase pseudorange information and (b) incorporating the pre-surveyed locations and oscillator stabilities of said pseudolites in said position calculation.
| 51. A user device supported by at least one service data processor as claimed in claim 50, wherein said pseudolites broadcast in the 5.9 GHz band. Date Recue/Date Received 2021-04-28
| 52. A user device supported by at least one service data processor as claimed in claim 50, wherein some or all of said pseudolites are mounted at street level.
| 53. A user device supported by at least one service data processor as claimed in claim 50, wherein some or all of said pseudolites are mounted above where street vehicles operate. Date Recue/Date Received 2021-04-28 | The method involves providing a service data processor accepting measurements received from one of the global navigation satellite system (GNSS) satellites and one of the low earth orbit (LEO) satellites. The precise orbit and clock predictions for the LEO satellite are generated. The predictions to the user device is disseminated to enable the user device to take into account the precise orbit and clock predictions, when computing the position upon receiving signals and measuring additional carrier phase pseudo-ranges from GNSS and LEO satellites. INDEPENDENT CLAIMS are included for the following:a service data processor for supporting resilient carrier phase of user device;a user device;a method for carrier phase positioning of user device;a method for localizing emitter;a method for GNSS signal authentication;a method for user position authentication;a method for fielding positioning service for one or more users;a method for generating regional, high-power navigation signals;a system for thermal control of high-power regional navigation satellite system;a method for providing agile, robust, and cost-effective services; anda method of beam forming space-borne distributed aperture. Method for supporting resilient carrier phase of user device such as subscriber vehicle (claimed), manned aircraft and unmanned aircraft. The performance of the GNSS is improved. The performance metrics including the accuracy, integrity, time to acquire, interference rejection, and spoofing protection for supporting resilient carrier phase of user device is improved. The disseminating process conforms to 4G LTE. The drawing shows a schematic view illustrating the process for supporting resilient carrier phase of user device. | Please summarize the input |
METHOD AND APPARAUTE FOR IDENTIFYING AUTONOMOUS VEHICLES IN AUTONOMOUS DRIVING ENVIRONMENTDisclosed is an autonomous vehicle identification method and apparatus for identifying autonomous vehicles using V2X communication data in an autonomous driving environment. An autonomous vehicle identification method performed by an autonomous vehicle identification device installed in a roadside device includes data indicating an autonomous driving state in a probe vehicle data (PVD) message received from a first vehicle that has entered a V2I communication area of a roadside device. Determining whether a frame or a data element representing an autonomous driving level exists, and if the data frame or data element does not exist, identifying a vehicle that has transmitted the PVD message as a general vehicle.|1. An autonomous vehicle identification method performed by an autonomous vehicle identification device installed in a roadside device, comprising: receiving a probe vehicle data (PVD) message from a vehicle that has entered a vehicle to infrastructure (V2I) communication area of the roadside device;
determining whether a data element indicating an autonomous driving level exists in the PVD message; and if the data element does not exist in the PVD message, identifying a vehicle that has transmitted the PVD message as a general vehicle, wherein a data element indicating the autonomous driving level is DE_AutonomousLevel and includes the data element. The data frame is a specific data frame representing an autonomous driving state, and the specific data frame is any one of a data element (DE_ODDinfo) defining operation design domain (ODD) information and a data element (DE_FallbackStatus) defining fallback information. A self-driving vehicle identification method further comprising the above.
| 2. The method of claim 1, further comprising identifying a vehicle that has transmitted the PVD message as an autonomous vehicle when the data element indicates an autonomous driving level of one or more.
| 3. The autonomous driving method according to claim 2, further comprising identifying a vehicle that has transmitted the PVD message as a non-autonomous vehicle or a connected car without an autonomous driving function, if the data element indicates an autonomous driving level of less than 1. Vehicle identification method.
| 4. The method of claim 1, wherein the specific data frame is an AutonomousStatus indicating an autonomous driving state.
| 5. An autonomous vehicle identification method performed by an autonomous vehicle identification device installed in a roadside device, comprising: receiving a probe vehicle data (PVD) message from a vehicle that has entered a vehicle to infrastructure (V2I) communication area of the roadside device;
determining whether a data element indicating an autonomous driving level exists in the PVD message;
determining whether the data element indicates one or more autonomous driving levels; and identifying a vehicle transmitting the PVD message as an autonomous vehicle when the data element indicates one or more autonomous driving levels.
, wherein the data element indicating the autonomous driving level is DE_AutonomousLevel, the data frame including the data element is a specific data frame representing an autonomous driving state, and the specific data frame defines ODD (operation design domain) information. The autonomous vehicle identification method further includes any one or more of a data element (DE_ODDinfo) that defines fallback information and a data element (DE_FallbackStatus) that defines fallback information.
| 6. The method according to claim 5, further comprising: identifying a vehicle transmitting the PVD message as a non-autonomous vehicle or a connected car without an autonomous driving function when the data element indicates an autonomous driving level of less than 1; and if the data element does not exist in the PVD message, identifying the vehicle transmitting the PVD message as a normal vehicle.
| 7. The method of claim 5, wherein the specific data frame is Autonomous indicating an autonomous driving state.
| 8. An autonomous vehicle identification device installed in a roadside device to identify an autonomous vehicle, comprising: a wireless communication module supporting vehicle to everything (V2X) communication; and at least one processor connected to the wireless communication module, wherein the at least one processor receives a probe vehicle data (PVD) message from a first vehicle that has entered a vehicle to infrastructure (V2I) communication area of the roadside device. Receiving through the wireless communication module, determining whether a data element indicating an autonomous driving level exists in the PVD message, determining whether the data element indicates one or more autonomous driving levels, and determining whether the data element indicates one or more autonomous driving levels, and When the above autonomous driving level is indicated, a step of identifying a vehicle having transmitted the PVD message as an autonomous vehicle is performed, a data element indicating the autonomous driving level is DE_AutonomousLevel, and a data frame including the data element is It is a specific data frame representing the autonomous driving state, The specific data frame further includes any one or more of a data element (DE_ODDinfo) defining operation design domain (ODD) information and a data element (DE_FallbackStatus) defining fallback information.
| 9. The method according to claim 8, wherein the at least one processor, when the data element indicates an autonomous driving level of less than 1, the vehicle transmitting the PVD message to a non-autonomous vehicle or a connected car not equipped with an autonomous driving function. Identifying, and if the data element does not exist in the PVD message, identifying a vehicle that has transmitted the PVD message as a normal vehicle.
| 10. The apparatus of claim 8, wherein the specific data frame is an AutonomousStatus indicating an autonomous driving state. | The method involves receiving a probe vehicle data (PVDD) message from a vehicle, and determining whether a data element indicating an autonomous driving level exists in the PVD message. A specific data frame is provided for representing the autonomous driving state, where the data frame includes the data element that defines operation design domain (ODD) information and data element defines fallback status. The data element is provided with a data frame that represents an autonomous status. An INDEPENDENT CLAIM is included for a autonomous vehicle identification device for identify autonomous vehicle. Method for identifying autonomous vehicle i.e. car using autonomous vehicle identification device installed in roadside device in autonomous driving environment. The method enables effectively identifying whether a target vehicle within a communication area is an autonomous vehicle or a vehicle equipped with an autonomous driving function. The method enables defining an identification factor in a message frame in compliance with a V2X communication data standard defined in a SAE J2735, so that scenarios for future driving negotiations can be effectively responded. The drawing shows a block diagram of method for identifying autonomous vehicle in autonomous driving environment. 100Roadside device110Processor120Memory130Transceiver150Communication module | Please summarize the input |
The driving negotiation method and an apparatusPROBLEM TO BE SOLVED: To provide a driving negotiation method and apparatus for supporting stability against a blind spot, an unexpected situation, etc., and a rapid judgement and response of an autonomous vehicle in various driving environments.
SOLUTION: A driving negotiation apparatus includes a wireless communication module configured to support V2X (vehicle to everything) communication and at least one processor connected to the wireless communication module. The at least one processor receives a cooperative request message from a first vehicle, broadcasts a cooperative request message and additional information required for a negotiation to surrounding vehicles, receives a cooperative response message from at least one second vehicle among the surrounding vehicles and transmits a message indicating that the negotiation is possible or impossible to the first vehicle on the basis of the cooperative response message.
SELECTED DRAWING: Figure 3|1. The wireless communication module which assists V2X (vehicle to everything) communication; The device includes at least one processor connected to the wireless communication module; the at least one processor receives a cooperation request message (CooperativeRequestMsg) from the first vehicle; and the like, and the communication module is provided with the above-mentioned processor. The cooperation relay message including the type code of the additional information required for negotiation in the cooperation request message is broadcasted to the surrounding vehicle; and the cooperation relay message is performed to the surrounding vehicle. A cooperative response message (CooperativeReponseMsg) corresponding to the cooperative relay message is received from at least one second vehicle out of the surrounding vehicles; and the cooperative reply message is received from the second vehicle. A message for negotiable or non-negotiable state is transmitted to the first vehicle based on the cooperation response message; and the travel negotiation device is provided.
| 2. The cooperation request message or the cooperation response message includes a vehicle speed; a data frame (data frames, DFs) for lane change and lane merging; and The first data frame relative to the vehicle speed is overpassed as negotiation message information about the speed adjustment plan, and includes a first data element for deceleration and stop. A second data frame for the lane change is avoided as negotiation message information for a lane change plan; an accident; and a second data element for an interruption and a pedestrian; and a second data frame is included in the lane change plan. A third data frame for the lane merging is used as a negotiation message for a merging plan; a merging path; an intersection; and a third data element with respect to the rotation intersection; and a traveling negotiation device described in claim 1.
| 3. The cooperation request message or the cooperation response message is a time stamp (timestamp), a vehicle identifier (id or TemporaryID), a message identifier (UniqueMSG_ID, messangeid or MsgID), and a message identifier (ID). The traveling negotiation device includes a data element for a previous message identifier (previousmessageid or preMsgID) and message information (infoMsg), and is described in claim 1.
| 4. The message identifier that is the data element of the cooperation request message is the present travel negotiation session identification value, and is used to define a process from the time of the travel negotiation request to the response as one session. The traveling negotiation device is described in claim 3.
| 5. The message information that is the data element of the cooperation request message is the sequence of message lists or message types required for the negotiation information (SequenceofMessageType); The traveling negotiation device is received from the first vehicle in a null state in the first travel negotiation, and is described in claim 3.
| 6. The at least one processor is configured to generate a related message related to the message type by the message type of the negotiation request message in relation to the additional information before broadcasting the cooperative relay message to the surrounding vehicle. A preset type code of the related message is included in the cooperation request message; and the related message is included in the cooperation request message. The at least one processor, when broadcasting the cooperative relay message, broadcasted the relevant message together with the cooperative relay message; and the traveling negotiation device described in the claim 1.
| 7. The cooperation response message includes a response value to the negotiation of the second vehicle itself and a data field of a response type including information necessary for negotiation, and includes a data element for the message identifier of the first vehicle and the previous message identifier. The traveling negotiation device is described in claim 1.
| 8. The response value for the negotiation includes a data element for consent (agree) or rejection (refuse); the radio communication module is a roadside base station or a roadside device (road side) (road side) It is installed in unit, RSU); the travel negotiation device described in Claim 7.
| 9. The message for the negotiable or non-negotiable message is transmitted to the first vehicle in the form of a broadcast, and the traveling negotiation device described in the claim 1 is provided.
| 10. The at least one processor transmits a message for the negotiable or non-negotiable message to the first vehicle after the negotiation. A message for renegotiation for making an identification value of a present travel negotiation session from the first vehicle to an identification value of a previous travel negotiation session or further a cooperation request message is further received; the travel negotiation device described in the claim 1. .
| 11. A step of receiving a cooperation request message (CooperativeRequestMsg) from a first vehicle; A step for generating a cooperative relay message including a type code of additional information required for negotiation in the cooperation request message is generated. The cooperative relay message is broadcasted to the surrounding vehicle. A step of receiving a cooperative response message (CooperativeReponseMsg) corresponding to the cooperative relay message is received from at least one second vehicle out of the surrounding vehicles. A message for negotiable or non-negotiable state is transmitted to the first vehicle, based on the cooperation response message. ; the traveling negotiation method.
| 12. The cooperation request message or the cooperation response message includes a vehicle speed; a data frame (data frames, DFs) for lane change and lane merging; and The first data frame relative to the vehicle speed is overpassed as negotiation message information about the speed adjustment plan, and includes a first data element for deceleration and stop. A second data frame for the lane change is avoided as negotiation message information for a lane change plan; an accident; and a second data element for an interruption and a pedestrian; and a second data frame is included in the lane change plan. A third data frame for the lane merging is used as a negotiation message for the merging plan; and a merging path; an intersection; and a third data element with respect to the rotation intersection, and the traveling negotiation method described in the claim 11.
| 13. The cooperation request message or the cooperation response message is a time stamp (timestamp), a vehicle identifier (id or TemporaryID), a message identifier (UniqueMSG_ID, messangeid or MsgID), and a message identifier (ID). The traveling negotiation method includes a data element to a former message identifier (previousmessageid or preMsgID) and message information (infoMsg); and a traveling negotiation method described in claim 11.
| 14. The message identifier that is the data element of the cooperation request message is the present travel negotiation session identification value, and is used to define a process from the time of the travel negotiation request to the response as one session. The travel negotiation method described in claim 13.
| 15. The message information that is the data element of the cooperation request message is the sequence of message lists or message types required for the negotiation information (SequenceofMessageType); The traveling negotiation method is received from the first vehicle in a null state during the first travel negotiation, and the traveling negotiation method described in the claim 13.
| 16. Before the cooperation relay message is broadcasted to the surrounding vehicle, it further includes a stage for generating related messages related to the message type by the message type of the cooperation request message in relation to the additional information. When the cooperative relay message is broadcasted to the surrounding vehicle, the relevant message is broadcasted together with the cooperative relay message; and the traveling negotiation method described in the claim 11.
| 17. The cooperation response message includes a response value to the negotiation of the second vehicle itself and a data field of a response type including information necessary for negotiation, and includes a data element for the message identifier of the first vehicle and the previous message identifier. The travel negotiation method described in claim 11.
| 18. The response value for the negotiation includes a data element for consent (agree) or rejection (refuse), and the travel negotiation method described in claim 17.
| 19. The message for the negotiable or non-negotiable message is transmitted to the first vehicle in the form of a broadcast, and the traveling negotiation method described in the claim 11 is disclosed.
| 20. After transmitting a message to the first vehicle for the negotiable or non-negotiable message, the message is transmitted to the vehicle. The method further includes a step of receiving a message for renegotiation of identifying a current travel negotiation session identification value from the first vehicle to an identification value of a previous travel negotiation session or further a further request for cooperation request message. The travel negotiation method described in claim 11. | The device (100) has a wireless communication module supporting vehicle to everything (V2X) communication. A processor (110) is connected to the wireless communication module, and receives a cooperation request message (CooperativeRequestMsg) from the first vehicle, and a type code of additional information required for negotiation in the cooperation request message Broadcasting a cooperative relay message including a to surrounding vehicles, receiving a cooperative response message (CooperativeReponseMsg) corresponding to the cooperative relay message from a second vehicle among the surrounding vehicles, and negotiation based on the cooperative response message. The cooperation request message or the cooperation response message includes a timestamp, a vehicle identifier (id or TemporaryID), a message identifier (UniqueMSG-ID, messangeid, or MsgID). The message identifier is a data element of the cooperation request message. An INDEPENDENT CLAIM is included for a driving negotiation method. Driving negotiation device for supporting the stability of blind spots or unexpected situations. The stability and ability of autonomous vehicles to cope with blind spots or unexpected situations are improved. The driving stability of vehicles such as autonomous vehicles is improved by providing a message set and negotiation process definition for V2I communication-based driving negotiation in a C-ITS environment. The drawing shows a block diagram for explaining the main configuration of a driving negotiation apparatus for executing the driving negotiation method. (Drawing includes non-English language text) 100Driving negotiation device110Processor120Memory130Transceiver150Wireless communication module | Please summarize the input |
Systems And Methods Using Artificial Intelligence For Routing Electric VehiclesThe present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.The embodiments of the invention in which an exclusive property or privilege is claimed are defined as follows:
| 1. An artificial intelligence (AI) Electric Vehicle (EV) route optimization method comprising:
an electronic, specifically programmed, communication computer AI system performing EV route optimization for travel of said EV from a designated origin location or EV present location to an EV designated destination location with intermediate stops at intervening battery charging stations to maintain battery charge levels;
storing in memory one or more EV attribute parameters comprising EV operational status parameters, EV location parameters, or EV battery status parameters;
derivation of EV potential route condition parameters for said EV based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional relationships between two or more of said EV attribute parameters and EV potential route condition parameters, and with expert defined individual parameter degree of danger value ranges;
AI evaluation and assignment of expert defined value ranges to selected of said EV attribute parameters and selected of said EV potential route condition parameters and wherein said expert defined value ranges depend on individual parameter importance to EV route optimization;
storing in memory expert defined propositional logic inference rules defining multiple range dependent conditional relationships between two or more interrelated multidimensional parameters comprising selected said EV attribute parameters and selected said EV potential route condition parameters;
AI evaluation of EV potential routes of travel from said EV designated origin location or EV present location to said EV designated destination location based on said EV attribute parameters, said EV potential route condition parameters and said expert defined propositional logic inference rules, and further wherein EV potential routes of travel include visiting battery charging stations as necessary to maintain proper EV battery charge levels to reach said EV designated destination location; and,
AI expert system optimization of selection of a particular route of travel based on said AI evaluation of said EV potential routes of travel comprising expert system analysis of one or more multidimensional combinations of said two or more interrelated multidimensional parameters of said EV attribute parameters and said EV potential route condition parameters.
| 2. The AI EV route optimization method of claim 1 further comprising accessing said EV potential route condition parameters using Internet telecommunications technology.
| 3. The AI EV route optimization method of claim 1 further comprising accessing of said EV potential route condition parameters using cellular communication technology to receive or transmit information between said EV and said external information sources.
| 4. The AI EV route optimization method of claim 1 further comprising exchanging selected of said EV attribute parameters of said EV with other motor vehicles or via remote information source facilities.
| 5. The AI EV route optimization method of claim 1 wherein said EV route optimization is further based upon battery charging station usage and actual or probable requests for route guidance from other EVs traveling within a defined distance from said EV present location, and further wherein such information that is accessed from said other EV's affects the expected waiting times or queues encountered at battery charging stations on possible routes of travel.
| 6. The AI EV route optimization method of claim 1 wherein said EV present location information is derived from motor vehicle GPS (Global Positioning System) signal sensors or from determination of the distance of said EV from cellular telephone towers or other known fixed locations transmitting signals received by one of the EV receivers.
| 7. The AI EV route optimization method of claim 1 wherein said potential route condition parameters comprise dynamic roadway conditions further comprising one or more of traffic congestion, weather conditions, police reported concerns, or other dynamic roadway condition information received from external information source database or data processing units.
| 8. The AI EV route optimization method of claim 1 wherein EV route selection decisions comprise consideration of potential dynamically changing charging requirements from other vehicles within a defined radius or distance from said EV present location.
| 9. The AI EV route optimization method of claim 1 wherein communicating with said external information sources further comprises operating an RFID (radio frequency identification) tag device used to identify the EV and communicate information with RFID tag readers located along highways tollways or roadways along which the EV is traveling.
| 10. The AI EV route optimization method of claim 7 wherein said external information source database or data processing units are cloud based and are accessed through the Internet or cellular telephone communication networks.
| 11. The AI EV route optimization method of claim 1 further comprising Bluetooth, Wi-Fi or other voice or data telecommunication capabilities for communicating with charging stations or other nearby vehicles present in ongoing traffic or waiting for use of charging stations.
| 12. The AI EV route optimization method of claim 1 wherein said EV potential route condition parameters from external information sources comprise pedestrian or crowd information.
| 13. The AI EV route optimization method of claim 1 wherein said EV accesses information from communication network applications.
| 14. The AI EV route optimization method of claim 13 wherein EV access of said communication network applications further comprising one or more of a Navigation System Application, Traffic Database Application, EV Account Application, Battery Charger/Replacement Station Application, Weather Data Application, Police Report Application, Special Event Application, or Road Condition Application.
| 15. The AI EV route optimization method of claim 14 wherein said Traffic Database Application comprises EV vehicle traffic congestion or density data.
| 16. The AI EV route optimization method of claim 14 wherein said Special Event Application comprises traffic or crowd congestion arising from special events along potential routes of travel.
| 17. The AI EV route optimization method of claim 1 wherein said EV attribute parameters and said EV potential route condition parameters from external information sources are stored in a remote database and wherein said remote database may be accessed and updated via vehicle-to-network connections.
| 18. The AI EV route optimization method of claim 1 wherein said EV is a driverless or autonomous driving vehicle.
| 19. An artificial intelligence (AI) Electric Vehicle (EV) route optimization system comprising:
an electronic, specifically programmed, communication computer AI system performing EV route optimization for travel of said EV from an EV designated origin location or EV present location to an EV designated destination location with intermediate stops at intervening battery charging stations to maintain battery charge levels;
a memory for storing one or more EV attribute parameters comprising EV operational status parameters, EV location parameters, or EV battery status parameters;
derivation of EV potential route condition parameters for said EV based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
evaluating and assigning AI expert defined value ranges to selected of said EV attribute parameters and selected of said EV potential route condition parameters and wherein said expert defined value ranges depend on individual parameter importance to EV route optimization;
a memory for storing expert defined propositional logic inference rules defining multiple range dependent conditional relationships between two or more interrelated multidimensional parameters comprising selected said EV attribute parameters and selected said EV potential route condition parameters;
AI evaluation of EV potential routes of travel from said EV designated origin location or EV present location to said EV designated destination location based on said EV attribute parameters, said EV potential route of travel parameters and said expert defined propositional logic inference rules, and further wherein said EV potential routes of travel include visiting battery charging stations as necessary to maintain proper EV battery charge levels to reach said EV designated destination location; and,
AI expert system optimization of selection of a particular route of travel based on said AI evaluation of said EV potential routes of travel comprising expert system analysis of one or more multidimensional combinations of said two or more interrelated multidimensional parameters of said EV attribute parameters and said EV potential route condition parameters.
| 20. The artificial intelligence (AI) Electric Vehicle (EV) route optimization system of claim 19 further comprising accessing said EV potential route condition parameters using cellular or Internet telecommunications technology. | The method involves performing electric vehicle (EV) route optimization for travel of EV from a designated origin location or EV present location to an EV designated destination location with intermediate stops at intervening battery charging stations (104) to maintain battery charge levels. One or more EV attribute parameters comprising EV operational status parameters, EV location parameters, or EV battery status parameters are stored in memory. The EV potential route condition parameters for EV are derivation based on information exchanges with at least two of communication network connections with application servers. The AI expert system optimization of selection of a particular route of travel based on artificial intelligence (AI) evaluation of EV potential routes of travel is performed. An INDEPENDENT CLAIM is included for an AI EV route optimization system. AI EV route optimization method. The method enables providing efficient routing algorithms that can be employed in real-time without excessive and complex computation and that consider multiple factors such as battery charging-replacement station locations, required time of travel, roadway conditions, traffic congestion, weather conditions and/or emergency traffic considerations, thus improving EV operational usefulness through determination of preferred routes of travel where the preferred routes include intermediate charging or replacement of EV batteries as required. The drawing shows a schematic diagram illustrating configuration of a driving situation with recharging stations benefiting from a routing and control system without limitation.101Driving area 102GPS satellite 103Destination 104Charging station 105Particular automotive vehicle | Please summarize the input |
Method and apparatus for vehicle-mounted enhanced visualization of sensor range and field of viewSome embodiments of the methods disclosed herein may include: receiving the predicted driving route, the sensor range of the sensor on the autonomous vehicle (AV) and the sensor field of view (FOV) data; determining whether a minimum sensor visibility requirement is met along the predicted driving route; predicting a blind area along the predicted driving route, wherein the predicted blind area is determined to have potentially reduced sensor visibility; and AR visualization using augmented reality (AR) display devices to display blind areas.|1. A method, comprising: receiving sensor range and sensor field FOV data of the sensor on the first vehicle; receiving blind area information from the second vehicle; predicting a blind area along the predicted driving path, wherein the predicted blind area is determined to have a potentially weakened sensor visibility; and using the augmented reality AR display device to display the AR visualization of the blind area, the method further comprising: receiving the predicted driving route; and determining whether the minimum sensor visibility requirement is satisfied along the predicted driving route, wherein determining whether the minimum sensor visibility requirement is satisfied comprises: determining a percentage of a minimum visibility region covered by the field of view FOV of the one or more sensors; and determining whether the percentage exceeds a visibility region threshold.
| 2. The method according to claim 1, wherein the first vehicle is a partially autonomous vehicle having at least one of a manual mode or a driver-assisted mode.
| 3. The method according to claim 1 or 2, wherein the first vehicle is a fully autonomous vehicle.
| 4. The method according to claim 1, further comprising: receiving map data; and updating the blind area, wherein updating the blind area comprises comparing the received map data with local dynamic map data.
| 5. The method according to claim 1 or 2, further comprising: The second vehicle is determined to be in a blind area in the predicted blind area based on the blind area information.
| 6. The method according to claim 5, further comprising: In response to determining that the second vehicle is in the blind area in the predicted blind area, an indication that the second vehicle is in the blind area is displayed.
| 7. The method according to claim 1 or 2, further comprising: identification blind area reduction technology; and moving the first vehicle from a first position to a second position in response to an identification blind area reduction technique.
| 8. The method according to claim 7, wherein the blind area reduction technique includes at least one of repositioning the first vehicle or adjusting an orientation of one of the sensors.
| 9. The method according to claim 1 or 2, wherein predicting a blind area comprises determining a visibility region at a plurality of locations along the predicted driving route.
| 10. The method according to claim 9, wherein determining a visibility region comprises simulating sensor visibility at the plurality of locations along the predicted driving route using three-dimensional 3D map data.
| 11. The method according to claim 1 or 2, wherein predicting the blind area comprises continuously estimating the location of the blind area based on a plurality of sensor readings.
| 12. The method according to claim 1 or 2, further comprising: The orientation of the first vehicle is tracked, wherein a prediction blind area is based on the orientation of the first vehicle.
| 13. The method according to claim 1 or 2, wherein displaying the AR visualization of the blind area comprises projecting the AR visualization using a vehicle-mounted augmented reality projection system to display the AR visualization.
| 14. The method according to claim 1 or 2, wherein displaying the AR visualization of the blind area comprises overlaying a highlighted display indicating the blind area on a map.
| 15. The method according to claim 1 or 2, wherein displaying the AR visualization of the blind area comprises displaying a contour of an area indicative of the blind area on a map.
| 16. A device, the device comprises: a processor; and a non-temporary computer-readable medium storing instructions that, when executed by the processor, are operable to cause the device to perform the method according to any one of claims 1 to 15.
| 17. The apparatus according to claim 16, further comprising: a group of sensors; a blind area prediction module configured to identify a potential blind area; a driving mode selection module configured to select a driving mode; a communication module configured to receive a vehicle-to-vehicle V2V message; and an augmented reality AR display device.
| 18. A method, comprising: receiving the predicted driving route, the sensor range of the sensor on the vehicle and the sensor field FOV data; determining whether the minimum sensor visibility requirement is satisfied along the driving route; predicting a blind area along the predicted driving route; wherein the predicted blind area is determined to have a potentially weakened sensor visibility; and displaying an AR visualization of the blind area using an augmented reality AR display device, wherein determining whether a minimum sensor visibility requirement is met comprises: determining a percentage of a minimum visibility region covered by the field of view FOV of the one or more sensors; and determining whether the percentage exceeds a visibility region threshold.
| 19. The method according to claim 18, wherein predicting a blind area along the driving route comprises determining an area where a sensor visibility range along the driving route is less than a minimum sensor visibility range requirement, and wherein the step of determining a blind area along the driving route comprises determining an area where the sensor visibility range along the driving route is less than a minimum sensor visibility range requirement. The minimum sensor visibility requirement includes the minimum sensor visibility range requirement.
| 20. The method according to claim 18, further comprising determining the minimum sensor visibility requirement along the driving route of the autonomous vehicle AV.
| 21. A device, the device comprises: a processor; and a non-temporary computer-readable medium storing instructions that, when executed by the processor, are operable to cause the device to perform the method according to any one of claims 18 to 20.
| 22. A method, comprising: predicting a blind area along the predicted driving route of the autonomous vehicle AV based on the expected limit of the sensor; and when the AV travels along the driving route, using an enhanced display AR visualization to provide an indication of the predicted blind area, the method further comprising: receiving the predicted driving route; and determining whether the minimum sensor visibility requirement is satisfied along the predicted driving route, wherein determining whether the minimum sensor visibility requirement is satisfied comprises: determining a percentage of a minimum visibility region covered by the field of view FOV of the one or more sensors; and determining whether the percentage exceeds a visibility region threshold. | The method (1700) involves receiving (1702) a predicted driving route, sensor ranges of sensors on a vehicle, and sensor field-of-view data, determining (1704) whether minimum sensor visibility requirements are met along the predicted driving route, predicting (1706) blind areas along the predicted driving route, in which the predicted blind areas are determined to have potentially diminished sensor visibility, and displaying (1708) an augmented reality visualization of the blind areas using an augmented reality display device. An INDEPENDENT CLAIM is also included for an apparatus for in-vehicle augmented reality visualization of sensor. Method for in-vehicle augmented reality visualization of sensor. Provides blind area reduction techniques, and responsive to identifying blind area reduction techniques, moving the vehicle from a first position to a second position which can help reduce significant traffic collisions. The drawing shows the flow diagram illustrating an example process for predicting blind areas and displaying a visualization corresponding to the predicted blind areas 1700Method1702Receiving predicted route sensor ranges of sensors1704Determining whether minimum sensor visibility requirement are met along predicted driving route1706Predicting blind areas along predicted driving route1708Displaying augmented reality visualization of blind areas | Please summarize the input |
System and/or method for platooningThe system can include a dispatcher and a plurality of cars. However, the system 100 can additionally or alternatively include any other suitable set of components. The system 100 functions to enable platooning of the plurality of cars (e.g., by way of the method S100).We claim:
| 1. A method for coordinated braking within a rail platoon, comprising:
determining, at an autonomous rail vehicle within the rail platoon, a compressive force at a leading end of the autonomous rail vehicle in a direction of traversal;
determining a coordinated braking event; and
in response to determining the coordinated braking event, braking at the autonomous rail vehicle while maintaining compression at the leading end, comprising autonomously controlling an independent set of brakes of the autonomous vehicle based on the compressive force at the leading end.
| 2. The method of claim 1, wherein braking at the autonomous rail vehicle comprises: based on the compressive force, independently controlling a regenerative braking of a battery-electric powertrain of the autonomous vehicle.
| 3. The method of claim 1, wherein the leading end comprises an abutment surface of a bumper which is suspended relative to a chassis of the autonomous rail vehicle.
| 4. The method of claim 1, wherein determining the coordinated braking event comprises receiving a braking command via a vehicle-to-vehicle (V2V) communication from a rail vehicle within the rail platoon.
| 5. The method of claim 4, further comprising: in response to receiving the V2V communication, relaying the braking command to a second autonomous rail vehicle within the platoon.
| 6. The method of claim 1, wherein the coordinated braking event is determined based on the compressive force.
| 7. The method of claim 1, further comprising: separating from the platoon based on a track geometry or a location of a level crossing.
| 8. The method of claim 1, wherein the compressive force is determined with a load cell.
| 9. A method, comprising:
determining, at a rail vehicle within a rail platoon, a contact force at a leading end of the rail vehicle in a direction of traversal; and
autonomously controlling the rail vehicle within the rail platoon, comprising:
determining a platoon control target; and
based on the contact force, controlling an independent powertrain of the rail vehicle to achieve the platoon control target.
| 10. The method of claim 9, wherein the independent powertrain comprises a battery-electric powertrain.
| 11. The method of claim 10, wherein autonomously controlling the rail vehicle comprises regeneratively braking with the battery-electric powertrain.
| 12. The method of claim 9, wherein the leading end comprises an abutment surface of a bumper which is damped relative to a chassis of the autonomous rail vehicle.
| 13. The method of claim 9, wherein the platoon control target comprises a target contact force.
| 14. The method of claim 13, wherein the target contact force is based on a relative energy distribution of the rail platoon.
| 15. The method of claim 9, wherein the platoon control target comprises a speed setpoint.
| 16. The method of claim 9, further comprising:
receiving, at the autonomous rail vehicle, a set of dispatch instructions associated with a coordinated separation of the rail platoon; and
based on the set of dispatch instructions, controlling the independent powertrain of the rail vehicle to separate from a leading portion of the rail platoon at the leading end.
| 17. The method of claim 16, wherein the coordinated separation is based on a track geometry or a location of a level crossing.
| 18. The method of claim 16, wherein the set of dispatch instructions comprises a warrant for the autonomous rail vehicle, wherein the control target is determined based on the warrant.
| 19. The method of claim 9, wherein autonomously controlling the rail vehicle within the rail platoon comprises: at the leading end of the rail vehicle, pushing an adjacent rail vehicle.
| 20. The method of claim 19, wherein no components span between the rail vehicle and the adjacent rail vehicle. | The method involves determining a compressive force at a leading end of an autonomous rail vehicle within the rail platoon in a traversal direction. A coordinated braking event is determined. Autonomous rail vehicle braking process is performed while maintaining compression at the leading end in response to determining the coordinated braking event. An independent set of brakes of the autonomous vehicle is autonomously controlled based on the compressive force at the leading end. An abutment surface of a bumper is suspended relative to a chassis of the autonomous rail vehicle. Method for realizing coordinated braking of self-propelling rail cars within rail platoon. The method enables reducing the risk of injury to the driver of the vehicle, preventing the vehicle from colliding with the other vehicles, and improving the operating reliability by eliminating components subject to failure and removing possibilities for human error. The drawing shows a schematic view of a structure for realizing coordinated braking of self-propelling rail cars within rail platoon.100Self-propelling rail cars coordinated braking system 110Disptacher 120Cars | Please summarize the input |
SYSTEM AND/OR METHOD FOR PLATOONINGThe system can include a dispatcher and a plurality of cars. However, the system 100 can additionally or alternatively include any other suitable set of components. The system 100 functions to enable platooning of the plurality of cars (e.g., by way of the method S100).We claim:
| 1. A method comprising:
positioning a first railway vehicle along a track;
providing a first set of instructions to a second railway vehicle; and
based on the first set of instructions, controlling traversal of the second railway vehicle until the second railway vehicle abuts the first railway vehicle;
providing a second set of instructions to the first railway vehicle;
based on the second set of instructions, controlling traversal of the first railway vehicle in a direction of transit; and
while controlling traversal of the first railway vehicle based on the second set of instruction, maintaining abutment between the second railway vehicle and the first railway vehicle by controlling the second railway vehicle to push the first railway vehicle in the direction of transit.
| 2. The method of claim 1, wherein the second railway vehicle is dynamically controlled to push the first railway vehicle in the direction of transit.
| 3. The method of claim 2, wherein the second railway vehicle is dynamically controlled based on a motion of the first railway vehicle.
| 4. The method of claim 2, wherein the second railway vehicle is controlled with a feedback controller based on a push force of the second railway vehicle applied on the first railway vehicle in the direction of transit.
| 5. The method of claim 1, wherein the second set of instructions is received from a remote dispatch system.
| 6. The method of claim 5, wherein controlling the second railway vehicle to push the first railway vehicle in the direction of transit comprises receiving, at the second railway vehicle, a third set of instructions from the remote dispatch system.
| 7. The method of claim 1, wherein the second set of instructions corresponds to both the first and second rail vehicles.
| 8. The method of claim 1, wherein positioning a first railway vehicle along a track comprises: controlling a powertrain of the first railway vehicle with an autonomous controller of the first railway vehicle based on a location of the railway vehicle.
| 9. The method of claim 1, wherein the second railway vehicle initially contacts the first railway vehicle while the first railway vehicle is substantially stationary.
| 10. The method of claim 1, wherein the first railway vehicle is autonomous vehicle.
| 11. The method of claim 1, wherein the second railway vehicle comprises an autonomous electric bogie.
| 12. A method comprising:
forming a platoon of rail vehicles comprising: independently controlling each rail vehicle of the platoon to arrange the rail vehicles in series along a track, with abutment between each pair of adjacent rail vehicles of the platoon; and
controlling traversal of the platoon in a first direction, comprising: for each pair of adjacent rail vehicles, controlling a trailing rail vehicle of the pair to push against a leading vehicle of the pair in the first direction.
| 13. The method of claim 12, wherein the abutment between each pair of adjacent rail vehicles of the platoon is continuously maintained during traversal of the platoon.
| 14. The method of claim 12, wherein forming the platoon comprises simultaneously maneuvering a plurality of the rail vehicles within a rail yard.
| 15. The method of claim 12, wherein controlling traversal of the platoon comprises, at a forwardmost rail vehicle of the platoon relative to the first direction: controlling traversal of the forwardmost rail vehicle according to a set of commands, wherein the set of commands are propagated rearwardly through the rail vehicles in series based on the motion of the forwardmost rail vehicle.
| 16. The method of claim 15, wherein the forwardmost rail vehicle is controlled via a velocity controller or torque controller.
| 17. The method of claim 12, further comprising: while controlling traversal of the platoon in the first direction, executing a coordinated deceleration of the platoon.
| 18. The method of claim 17, wherein the coordinated deceleration is based on a plurality of wireless vehicle-to-vehicle (V2V) communications.
| 19. The method of claim 12, wherein each rail vehicle of the platoon comprises a pair of electric bogies.
| 20. The method of claim 19, wherein each electric bogie is autonomous and configured to be independently maneuverable. | The method involves positioning first railway vehicle (120) along a track, and providing first set of instructions to second railway vehicle. Traversal of the second railway vehicle is controlled based on the first set of instructions until the second railway vehicle abuts the first railway vehicle. Second set of instructions is provided to the first railway vehicle. Traversal of the first railway vehicle is controlled in direction of transit based on the second set of instructions. Abutment is maintained between the second railway vehicle and the first railway vehicle, while controlling traversal of the first railway vehicle based on the second set of instructions by controlling the second railway vehicle to move the first railway vehicle in direction of transit. The first railway vehicle is autonomous vehicle. The second railway vehicle comprises an autonomous electric bogie. Method for platooning a vehicle e.g. autonomous vehicle such as self-propelling railcar, and autonomous electric bogie (all claimed), in a payload transportation field. The method enables reducing risk of injury to the driver of the vehicle and preventing the vehicle from colliding with other vehicles. The method enables maintaining an energy source of the lead vehicle to facilitate continuous autonomous protection at the lead vehicle and maintain continuous energy supply without power contributions from a powertrain of the lead vehicle. The drawing shows a schematic block diagram of a system for platooning a vehicle.100System for platooning vehicle 110Dispatcher 120Railway vehicle V2IVehicle-to-infrastructure V2VVehicle-to-vehicle communications | Please summarize the input |
Driving auxiliary method, road photographic image collecting method and road side deviceWithout significantly increasing the load of ITS communication, the load of control for detecting an event of interest to perform a danger avoidance action can be reduced, thereby appropriately assisting the driving control of an automatic driving vehicle. a vehicle-mounted terminal (4) loaded on a vehicle (1) that has ended the passage in the object interval of the road as an information providing source, when an event of interest is detected during the passage in the object interval, sending a photographic image and additional information related to the event of interest to a roadside machine (6) on the end point side of the end point of the object section, the roadside machine (6) on the end point side sends the photographic image and additional information related to the event of interest to a roadside machine (6) on the start point side of the start point of the object section, The road side machine (6) on the starting point side sends the photographic image and additional information related to the attention event to the vehicle-mounted terminal which is carried as the information providing destination to start to pass in the object interval, The vehicle terminal as the information providing destination performs the processing related to the driving control of the vehicle based on the shooting image related to the attention event and the additional information.|1. A driving assistance method, wherein a vehicle-mounted device mounted on a vehicle passing in an object section of a road as an information providing source transmits vehicle-mounted information indicating that the vehicle is mounted with a driving recorder and an image issuing function to a road-side device. the vehicle-mounted device sends a photographic image and additional information related to the interest event to the road side device when the interest event is detected during the passage of the object interval, when the road side device does not receive the vehicle-mounted information within a predetermined time from the vehicle-mounted information received by the vehicle-mounted device, the detection of the attention event is started, when the attention event is detected, the photographic image and additional information related to the attention event are collected, the collected captured image and the additional information are transmitted directly or via other road side devices to a vehicle-mounted device mounted on a vehicle to be started to pass in the object interval as an information providing destination, the road side device receives the vehicle-mounted information from the vehicle-mounted device, stopping the detection of the attention event and the transmission of the photographic image and additional information collected by the device, sending the photographic image and additional information related to the attention event received from the vehicle-mounted device directly or via other road side device to the vehicle-mounted device as the information providing destination of the vehicle to be started to pass in the object interval, The vehicle-mounted device as the information providing destination performs processing related to the travel control of the vehicle based on a photographic image related to the event of interest and additional information.
| 2. The travel assistance method according to claim 1, wherein the additional information comprises the interval information of the interval where the vehicle is currently located, the information related to the travel direction of the vehicle, the information related to the content of the attention event, and the position information of the vehicle on the map when the attention event is detected. Position information of a place where an event of interest occurs on a photographic image and time information when the event of interest is detected.
| 3. The driving assistance method according to claim 1, wherein the vehicle-mounted device as the information providing destination is based on the similarity between the shot image related to the attention event and the shot image at the current time point. to make a determination that the vehicle is approaching the place of occurrence of the event of interest.
| 4. The travel assistance method according to claim 3, wherein the vehicle-mounted device as the information providing destination outputs a risk avoidance operation instruction to the travel control device when it is determined that the vehicle is approaching the occurrence point of the event of interest.
| 5. The travel assistance method according to claim 1, wherein the on-vehicle device as the information supply source transmits the photographic image and additional information related to the event of interest to a road side device located at the end point side of the end point of the object section. the road side device at the end point side sends the photographic image and additional information related to the attention event to the road side device at the starting point side of the starting point of the object interval, The road side device at the starting point side sends the photographic image and additional information related to the attention event to the vehicle-mounted device as the information providing destination.
| 6. The travel assistance method according to claim 1, wherein the vehicle-mounted device as an information supply source is mounted on a vehicle passing in the first direction in the object section. The vehicle-mounted device as an information providing destination is mounted on a vehicle passing in a second direction opposite to the first direction in the object section, the road side device extracts the photographic image of the moving object at the front side of the shielding object from the photographic image obtained from the vehicle-mounted device as the information providing source passing to the first direction, calculating the position of the moving object on the photographic image in the second direction obtained by the vehicle-mounted device of the vehicle passing from the past to the second direction, generating a composite image obtained by superposing the photographic image of the moving object on the photographic image in the second direction based on the calculated position, The roadside device transmits the composite image as a photographic image related to the event of interest to the on-vehicle device as an information providing destination.
| 7. The driving assistance method according to claim 1, wherein the attention event is a traffic accident, the road side device accumulates the captured image and additional information related to the attention event obtained in the vehicle-mounted device or the device as the information providing source in the device. In addition to the latest photographic image and additional information related to the event of interest, the roadside device also transmits the photographic image and additional information related to the event of interest accumulated in the local device to the on-vehicle device as an information providing destination.
| 8. A road photographic image collecting method, wherein the vehicle-mounted device loaded on the vehicle passing in the object interval of the road sends the vehicle-mounted information indicating that the vehicle is loaded with the driving recorder and the image issuing function to the road side device, the vehicle-mounted device sends the shot image and additional information related to the interest event to the road side device when the interest event is detected during the passage of the object interval, when the road side device does not receive the vehicle-mounted information within a predetermined time from the vehicle-mounted information received by the vehicle-mounted device, the detection of the attention event is started, when the attention event is detected, the photographic image and additional information related to the attention event are collected, the collected photographic image and the additional information are transmitted to the server device, the road side device stops the detection of the attention event and the transmission of the photographic image and the additional information collected by the device under the condition that the vehicle carrying information is received from the vehicle-mounted device, sending the photographic image and additional information related to the event of interest received from the vehicle-mounted device to the server device, the server device accumulates the photographic image and additional information related to the event of interest, the server device according to the browsing request of the designated place from the user, A photographic image and additional information relating to the event of interest of the designated location is presented to the user.
| 9. A road side device, comprising: a road-to-vehicle communication part for communicating with the vehicle-mounted device; and a processor, wherein the processor receives vehicle-mounted information indicating that the vehicle is mounted with a driving recorder and an image publishing function from a vehicle-mounted device which is used as an information providing source and is mounted on a vehicle which has passed in an object interval of the road, when the processor does not receive the vehicle loading information within a predetermined time from the last receiving of the vehicle loading information, the detection of the attention event is started, when the attention event is detected, the photographic image and additional information related to the attention event are collected, sending the collected photographic image and the additional information directly or via other road side device to the vehicle-mounted device loaded on the vehicle to be started to pass in the object interval as the information providing destination, the processor receives the vehicle loading information, stopping the detection of the attention event and the transmission of the photographic image and additional information collected by the device, receiving a photographic image and additional information related to an event of interest detected by the vehicle-mounted device as the information providing source during the passage of the object interval from the vehicle-mounted device as the information providing source through the road-to-vehicle communication part, The processor transmits the received photographic image and additional information related to the attention event to the vehicle-mounted device as the information providing destination of the vehicle to be started to pass in the object interval directly or via other road-side devices via the road-room communication part.
| 10. The road side device according to claim 9, wherein when the device is located at the end point of the object area, the road side communication part receives the shot image and additional information related to the attention event from the vehicle-mounted device as the information providing source. when the device is located at the starting point of the object interval, the road-to-vehicle communication part sends the shot image and additional information related to the attention event to the vehicle-mounted device as the information providing destination.
| 11. The roadside device according to claim 9, wherein the road-to-vehicle communication unit receives a photographic image and additional information related to the event of interest from the on-vehicle device as an information providing source mounted on a vehicle passing in the object section in the first direction. the road-to-vehicle communication part sends the shot image and additional information related to the attention event to the vehicle-to-vehicle device as the information providing destination of the vehicle passing in the second direction opposite to the first direction in the object interval, the processor extracts the photographic image of the moving object at the front side of the current shielding object from the photographic image obtained from the vehicle-mounted device as the information providing source passing to the first direction, calculating the position of the moving object on the photographic image in the second direction obtained by the vehicle-mounted device of the vehicle passing from the past to the second direction, generating a composite image obtained by superposing the photographic image of the moving object on the photographic image in the second direction based on the calculated position, The processor transmits the composite image as a photographic image related to the event of interest to the on-vehicle device as an information providing destination.
| 12. The roadside device according to claim 9, wherein the attention event is a traffic accident, and the processor accumulates the captured image and additional information related to the attention event obtained from the on-vehicle device as the information providing source in the storage unit of the device. In addition to the latest photographic image and additional information related to the event of interest, the processor also transmits the photographic image and additional information related to the event of interest accumulated in the storage unit to the on-vehicle device as the information providing destination.
| 13. A road side device, comprising: a road-to-vehicle communication part for communicating with the vehicle-mounted device; and a processor, wherein the processor receives vehicle-mounted information indicating that the vehicle is mounted with a driving recorder and an image publishing function from a vehicle-mounted device which is used as an information providing source and is mounted on a vehicle which has passed in an object interval of the road, when the processor does not receive the vehicle loading information within a predetermined time from the last receiving of the vehicle loading information, the detection of the attention event is started, when the attention event is detected, the photographic image and additional information related to the attention event are collected, sending the collected photographic image and the additional information to the server device, the processor stops the detection of the attention event and the sending of the photographic image and the additional information collected by the device, receiving a photographic image and additional information related to an event of interest detected by the vehicle-mounted device as the information providing source during the passage of the object interval from the vehicle-mounted device as the information providing source through the road-to-vehicle communication part, The processor transmits the received photographic image and additional information relating to the event of interest to a server device. | The method involves transmitting the photographed image and additional information regarding the noteworthy event to a roadside device, and the roadside device sends the photographed image and additional information when a noteworthy event is detected while passing through the target section. The captured image and additional information related to the attention event are transmitted directly or through another roadside device to the in-vehicle device of the information providing destination mounted on a vehicle (1) that starts the passage of the target section. The process related to travel control of the own vehicle is performed based on a photographed image and additional information related to the attention event. INDEPENDENT CLAIMS are included for the following:a road picked-up image collection method; anda roadside apparatus. Driving assistance method for assisting traveling control of vehicle. The safe driving of an automatic drive vehicle can be assisted appropriately. The load of the control performs danger avoidance movement can be reduced. The drawing shows a schematic view of the driving assistance system. (Drawing includes non-English language text) 1Vehicle2Camera4Vehicle-mounted terminal6Traveling control apparatus | Please summarize the input |
METHOD FOR DETERMINING TRAFFIC VIOLATION VEHICLE WHICH EXISTS ON DRIVING ROUTE OF AUTONOMOUS VEHICLE AND THE DEVICE USING THE SAMEAccording to the present invention, there is provided a method for determining whether a vehicle violating traffic laws is present on a driving path of an autonomous vehicle in motion, comprising (a) at least one of (i) a camera, RADAR, and LIDAR in the autonomous vehicle A state in which a first information acquisition module, (ii) a second information acquisition module including a V2X (Vehicle to Everything) communication module, and (iii) a third information acquisition module including a GPS (Global Positioning System) module is mounted In the above, the offending vehicle determination device corresponding to the autonomous vehicle (i) analyzes the first data acquired by the first information acquisition module or causes the first information acquisition module to analyze, A process of acquiring detection information about at least one other vehicle driving in the vicinity, (ii) analyzing the second data acquired by the second information acquisition module or causing the second information acquisition module to analyze, a process of acquiring signal information of at least one traffic light existing in the vicinity of the autonomous vehicle, and (iii) analyzing the third data acquired by the third information acquisition module or causing the third information acquisition module to analyze performing a process of acquiring first location information of the autonomous vehicle; (b) the violating vehicle judging device determines whether the traffic law violating vehicle exists on the driving path of the autonomous vehicle by referring to at least a part of the detection information, the signal information, and the first location information to do; and (c) when there is a vehicle violating traffic laws, the violating vehicle judging device, among the detection information, the signal information, and the first location information, specific detection information related to the traffic law violation vehicle, specific signal information and allowing the traffic law violation evidence collection module to store and manage traffic law violation evidence information including at least specific first location information; There is provided a method comprising a.|1. A method for determining whether a vehicle violating traffic laws is present on a driving route of an autonomous driving vehicle in driving, comprising: (a) a first information acquisition module including (i) a camera module, a RADAR module, and a LIDAR module in the autonomous driving vehicle , (ii) a second information acquisition module including a V2X (Vehicle to Everything) communication module, and (iii) a third information acquisition module including a GPS (Global Positioning System) module are mounted, the autonomous driving The offending vehicle judging device corresponding to the vehicle (i) analyzes the first data acquired by the first information acquisition module or causes the first information acquisition module to analyze at least while driving in the vicinity of the autonomous vehicle A process of acquiring detection information for one other vehicle, (ii) analyzing the second data acquired by the second information acquisition module or causing the second information acquisition module to analyze, a process of obtaining signal information of at least one existing traffic light; and (iii) analyzing the third data acquired by the third information acquisition module or performing a process of acquiring the first location information of the autonomous vehicle by causing the third information acquisition module to analyze;
(b) the violating vehicle judging device determines whether the traffic law violating vehicle exists on the driving path of the autonomous vehicle by referring to at least a part of the detection information, the signal information, and the first location information to do; and (c) when there is a vehicle violating traffic laws, the violating vehicle judging device, among the detection information, the signal information, and the first location information, specific detection information related to the traffic law violation vehicle, specific signal information and allowing the traffic law violation evidence collection module to store and manage traffic law violation evidence information including at least specific first location information;
and, when image data photographing the surrounding conditions of the autonomous vehicle is obtained from the camera module, the offending vehicle determination device causes the deep learning module interlocked with the offending vehicle determination device with respect to the image data to perform a predetermined to detect the other vehicle using an image object detection algorithm of If present, support to use a Single Shot Multibox Detector (SSD) as the image object detection algorithm, and (ii) the specific unit area area in which the other vehicle exists over the first threshold in the entire area of the image data It is characterized in that it supports YOLO (You Only Look Once) to be used as the image object detection algorithm when it does not exist, in the step (a), The offending vehicle determination device fuses each of the detection information of the other vehicle obtained from each of the first information acquisition module using a Kalman filter-based sensor fusion algorithm, (i) an environment factor applying the sensor fusion algorithm In a situation where it is determined that the weight of the time factor is high, the EKF (Extended Kalman Filter) algorithm is supported to be used, and (ii) it is determined that the weight of the accuracy factor is high among the environmental factors to which the sensor fusion algorithm is applied. is characterized in that it supports the UKF (Unscented Kalman Filter) algorithm to be used, and in step (b), the offending vehicle determination device includes (i) the detection information, the signal information, and the first location information a process of generating a first LDM (Local Dynamic Map) in which dynamic driving information of each of the autonomous vehicle and the other vehicle is linked with predetermined map data with reference to at least a part of the information; (ii) a process of obtaining each of the second LDMs generated by each of the other vehicles using the V2X communication module; and (iii) combining each of the first LDM and the second LDM to generate one expanded map; characterized in that to perform, characterized in that the violating vehicle determination device, with additional reference to the expansion map, to determine whether the traffic law violation vehicle exists, in the step (a), the violating vehicle determination The device allows the V2X communication module to transmit each SPAT (Signal Phase and Timing) message from each RSU (Rode Side Unit) interlocked with each of the at least one traffic light located in the vicinity of the autonomous vehicle as 2-1 data. It is characterized in that the signal information is obtained by receiving and analyzing or directly analyzing, and the offending vehicle determination device causes the V2X communication module to obtain a BSM (Basic Safety Message) of each of the other vehicles from each of the other vehicles. It is characterized in that the driving information of each of the other vehicles is additionally acquired by receiving and analyzing as the 2-2 data, or directly analyzing it, and in the step (b), the offending vehicle determining apparatus By additionally referring to each of the driving information of each of the other vehicles, it is determined whether there is a specific collision-anticipated other vehicle expected to collide on the driving path of the autonomous vehicle among the other vehicles, and the specific collision-anticipated other vehicle is determined to be the vehicle violating traffic laws when there is, and in step (c), the traffic law violation evidence information corresponds to the traffic law violating vehicle among the driving information of each of the other vehicles. When it is possible to receive each of the BSMs of each of the other vehicles using the V2X communication module, the offending vehicle determination device may further include specific driving information that is By predicting the trajectory of each of the other vehicles using a constant turn rate and acceleration (CTRA) model assuming that the (yaw rate) value is constant, It is characterized in that it is determined whether each of the other vehicles has a risk of colliding with the autonomous vehicle, and after step (c), (d) the offending vehicle determination device causes the traffic law violation evidence collection module to The traffic law violation evidence information corresponding to the traffic law violation vehicle is transmitted to a report module, so that the report module sends the report module to a specific enforcement agency corresponding to the traffic law violation type of the traffic law violation vehicle among a plurality of enforcement agencies. Supporting to report at least a part of the traffic law violation evidence information;
It characterized in that it further comprises, the report module, characterized in that the information on each traffic law violation report interface provided from each of the enforcement agencies is stored in advance, the violation vehicle judging device, the report Support the module to automatically input and report specific traffic law violation evidence information that can be entered into the specific traffic law violation report interface among the traffic law violation evidence information through the specific traffic law violation report interface provided by the specific enforcement agency A method characterized in that
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| 3. The method according to claim 1, wherein when image data photographing the surrounding conditions of the autonomous vehicle is obtained from the camera module, the offending vehicle determination device uses a deep learning module interlocked with the offending vehicle determination device for the image data. to detect the other vehicle using a predetermined image object detection algorithm, but before the step (a), the offending vehicle determining device performs learning of the deep learning module using predetermined learning data characterized in that, the learning of the deep learning module causes the weight to be increased for at least one specific first class from which the correct rate of the class prediction value is less than the second threshold is derived, and the class prediction value is the correct rate above the second threshold For at least one specific second class from which this is derived, a loss value is calculated using a focal loss that causes the weight to be reduced, The process of optimizing a plurality of parameters included in the deep learning module is repeated by performing backpropagation so that the loss value is minimized, and the class prediction value is the object included in the image data of the corresponding class. A method characterized in that it is a probability value predicted whether it is an object.
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| 6. The MLE (Maximum Likelihood) of claim 1, wherein when the offending vehicle determination device combines each of the first LDM and the second LDM, when there is a specific other vehicle having different positional coordinates among the other vehicles. Estimation) method, characterized in that the correction in one absolute position coordinates.
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| 11. A vehicle judging device for judging whether a vehicle violating traffic laws exists on a driving route of an autonomous vehicle in motion, comprising: at least one memory for storing instructions; and at least one processor configured to execute the instructions. including, wherein the processor includes (I) a first information acquisition module including (i) a camera module, a RADAR module, and a LIDAR module in the autonomous vehicle, (ii) a V2X (Vehicle to Everything) communication module. In a state in which a third information acquisition module including a second information acquisition module and (iii) a global positioning system (GPS) module is mounted, (i) analyzes the first data acquired by the first information acquisition module or a sub-process of obtaining detection information for at least one other vehicle driving in the vicinity of the autonomous vehicle by causing the first information acquisition module to analyze; (ii) the second information acquisition module acquired by the second information acquisition module. 2 A sub-process of analyzing data or causing the second information acquisition module to analyze to acquire signal information of at least one traffic light existing in the vicinity of the autonomous vehicle; and (iii) analyzing the third data acquired by the third information acquisition module or having the third information acquisition module analyze to perform a sub-process of acquiring the first location information of the autonomous vehicle; (II) a process of determining whether the traffic law violation vehicle exists on the driving path of the autonomous vehicle by referring to at least a part of the detection information, the signal information, and the first location information; and (III) when the traffic law violation vehicle exists, specific detection information related to the traffic law violation vehicle among the detection information, the signal information, and the first location information, the specific signal information, and the specific first location information a process for allowing the traffic law violation evidence collection module to store and manage traffic law violation evidence information including at least; characterized in that, when the image data photographing the surrounding situation of the autonomous vehicle is obtained from the camera module, the processor causes the deep learning module interlocked with the offending vehicle determination device with respect to the image data The other vehicle is detected using a predetermined image object detection algorithm, and the processor is configured to: (i) the other vehicle corresponding to the number equal to or greater than the first threshold within a specific unit area area among the entire area of the image data. In this case, a Single Shot Multibox Detector (SSD) can be used as the image object detection algorithm, and (ii) the specific unit area area in which the other vehicle exists above the first threshold exists in the entire area of the image data. If not, it is characterized in that it supports YOLO (You Only Look Once) to be used as the image object detection algorithm, and in the (I) process, The processor fuses each of the detection information of the other vehicle obtained from each of the first information acquisition module using a Kalman filter-based sensor fusion algorithm, (i) a time factor among environmental factors to which the sensor fusion algorithm is applied In a situation where the weight of is determined to be high, the EKF (Extended Kalman Filter) algorithm is supported to be used, and (ii) in a situation where the weight of the accuracy factor is determined to be high among the environmental factors to which the sensor fusion algorithm is applied, the UKF ( Unscented Kalman Filter) algorithm is supported to be used, and in the process (II), the processor, (i) referring to at least a part of the detection information, the signal information, and the first location information , a process of generating a first LDM (Local Dynamic Map) in which dynamic driving information of each of the autonomous vehicle and the other vehicle is linked with predetermined map data; (ii) a process of obtaining each of the second LDMs generated by each of the other vehicles using the V2X communication module; and (iii) combining each of the first LDM and the second LDM to generate one expanded map; characterized in that to perform, characterized in that the processor, with further reference to the extension map, characterized in that the determination of whether the traffic violation vehicle exists, in the (I) process, the processor, the V2X communication Let the module receive and analyze each of the Signal Phase and Timing (SPAT) messages as 2-1 data from each of the RSUs (Rode Side Units) interlocked with each of the at least one traffic light located in the vicinity of the autonomous vehicle, or Direct analysis, characterized in that the signal information is obtained, and the processor causes the V2X communication module to receive the BSM (Basic Safety Message) of each of the other vehicles from each of the other vehicles as 2-2 data. It is characterized in that each of the driving information of each of the other vehicles is additionally obtained by analyzing it or directly analyzing it, and in the process (II), the processor, By additionally referring to each of the driving information of each of the other vehicles, it is determined whether there is a specific collision-anticipated other vehicle expected to collide on the driving path of the autonomous vehicle among the other vehicles, and the specific collision-anticipated other vehicle is determined as the traffic law-violating vehicle when there is, and in the process (III), the traffic law violation evidence information corresponds to the traffic law-violating vehicle among the driving information of each of the other vehicles. It is characterized in that it further includes specific driving information to be used, and when each of the BSMs of each of the other vehicles can be received using the V2X communication module, the processor, the yaw rate included in the BSM ) by predicting the trajectory of each of the other vehicles using a constant turn rate and acceleration (CTRA) model assuming that the value is constant, It is characterized in that it is determined whether each of the other vehicles has a risk of colliding with the autonomous vehicle, and after the (III) process, (IV) the processor causes the traffic law violation evidence collection module to cause the traffic law The traffic law violation evidence information corresponding to the violating vehicle is transmitted to the report module, and the report module causes the report module to transmit the traffic law violation to a specific enforcement agency corresponding to the traffic law violation type of the traffic law violating vehicle among a plurality of enforcement agencies. a process of supporting to report at least some of the evidence information; characterized in that it further performs, the report module, characterized in that the information on each traffic law violation report interface provided from each of the enforcement agencies is stored in advance, and the processor causes the report module to Through the specific traffic law violation report interface provided by the specific enforcement agency, the specific traffic law violation evidence information that can be inputted into the specific traffic law violation report interface among the traffic law violation evidence information is automatically input to support reporting Violation vehicle judgment system.
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| 12. The method of claim 11, wherein when the image data photographing the surrounding conditions of the autonomous vehicle is obtained from the camera module, the processor causes the deep learning module interlocked with the offending vehicle determination device to set a predetermined value on the image data. Detect the other vehicle using an image object detection algorithm, characterized in that before the (I) process, the processor performs learning of the deep learning module using predetermined learning data, The learning of the learning module is such that the weight is increased for at least one specific first class from which the correct rate of the class prediction value is less than the second threshold value is derived, and the class prediction value is at least one from which the correct rate higher than the second threshold value is derived For a specific second class, the loss value is calculated using the focal loss that causes the weight to be reduced, The process of optimizing a plurality of parameters included in the deep learning module is repeated by performing backpropagation so that the loss value is minimized, and the class prediction value is the object included in the image data of the corresponding class. Violation vehicle determination device, characterized in that the probability value predicted whether the object is.
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| 12. The method of claim 11, wherein when the processor combines each of the first LDM and the second LDM, if there is a specific other vehicle having different positional coordinates among the other vehicles, a Maximum Likelihood Estimation (MLE) technique Violation vehicle determination device, characterized in that the correction to one absolute position coordinates through.
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| 20. delete | The method involves analyzing data acquired by an information acquisition module by an offending vehicle judging device (100) corresponding to a vehicle. Signal information of an existing traffic light is acquired, and location information of the autonomous vehicle is acquired. A determination is made whether a traffic law violating vehicle exists on a driving path of an autonomous vehicle by referring to a part of the detection information, signal information and the location information. A traffic law violation evidence collection module (150) is utilized to store and manage traffic law violation evidence information including specific first location information. An INDEPENDENT CLAIM is also included for vehicle judging device for judging whether a vehicle violating traffic laws exists on a driving route of an autonomous vehicle in motion. The method is useful for determining whether a vehicle violating traffic laws exists on a driving path of an autonomous vehicle. The information on a vehicle violating traffic laws existing on a driving path of an autonomous vehicle is automatically reported to an enforcement agency. The drawing shows a diagram schematically illustrating an overall system in which an offending vehicle determination device for determining whether a vehicle violating traffic laws exists on a driving path of an autonomous vehicle (Drawing includes non-English language text).100Offending vehicle judging device150Traffic law violation evidence collection module | Please summarize the input |
Method and monitoring server for verifying the operation of autonomous vehicles using THE Quality Control verification appIn the method of verifying the operation of an autonomous vehicle using a QC (Quality Control) verification app, a specific verification scenario for a specific road section is divided into a plurality of verification sections, and the autonomous vehicle for each verification section In the state that at least one reference PVD (Probe Vehicle Data) for each operation event to be operated is stored in the database, and the driving PVD (Prove Vehicle Data) of the autonomous vehicle is being transmitted to the control server, (a) the Specific driving PVD (Prove Vehicle Data) transmitted from the autonomous driving vehicle by the control server - The specific driving PVD performs a specific operation event included in a specific verification section in which the autonomous driving vehicle is one of the plurality of verification sections acquiring - which is the driving PVD of the autonomous vehicle corresponding to one; (b) the control server, (i) the first verification result information transmitted by the user riding in the autonomous vehicle - The first verification result information is the specific operation input by the user through the QC verification app A process of obtaining - which is information on whether the operation performed by the autonomous driving vehicle for the event is successful performing a process of obtaining second verification result information, which is a result of determining whether the specific reference PVD is matched; and (c) the control server determines whether the first verification result information and the second verification result information match, and a third information that is final verification result information of the operation performed by the autonomous vehicle with respect to the specific operation event A method and a control server for verifying the operation of an autonomous vehicle, comprising: obtaining verification result information; are disclosed.|1. In the method of verifying the operation of an autonomous vehicle using a QC (Quality Control) verification app, a specific verification scenario for a specific road section is divided into a plurality of verification sections, and the autonomous vehicle for each verification section In a state that at least one reference PVD (Probe Vehicle Data) for each operation event to be operated is stored in the database, and the driving PVD (Prove Vehicle Data) of the autonomous vehicle is being transmitted to the control server, (a) the Specific driving PVD (Prove Vehicle Data) transmitted from the autonomous driving vehicle by the control server - The specific driving PVD performs a specific operation event included in a specific verification section in which the autonomous driving vehicle is one of the plurality of verification sections acquiring - which is the driving PVD of the autonomous vehicle corresponding to one;
(b) the control server, (i) the first verification result information transmitted by the user riding in the autonomous vehicle - The first verification result information is the specific operation input by the user through the QC verification app A process of obtaining - which is information on whether the operation performed by the autonomous driving vehicle for the event is successful performing a process of obtaining second verification result information, which is a result of determining whether the specific reference PVD is matched; and (c) the control server determines whether the first verification result information and the second verification result information match, and a third information that is final verification result information of the operation performed by the autonomous vehicle with respect to the specific operation event obtaining verification result information;
A method of verifying the operation of an autonomous vehicle comprising a.
| 2. The method according to claim 1, wherein each of the reference PVD and the driving PVD is used for verification of (i) a standard field indicating a data item of the autonomous vehicle standardized for V2X communication and (ii) each of the plurality of operation events A method for verifying the operation of an autonomous vehicle, characterized in that it includes a non-standard field indicating a data item of the autonomous vehicle.
| 3. The method according to claim 1, wherein the control server additionally acquires traffic signal information transmitted from a plurality of traffic signal controllers installed in the specific road section, (i) the autonomous vehicle communicates with the plurality of traffic signal controllers V2X an indirect acquisition method of acquiring the traffic signal information through A method of verifying the operation of an autonomous vehicle, characterized in that the traffic signal information is acquired by at least one of direct acquisition methods for acquiring the traffic signal information through direct communication with a traffic signal controller of
| 4. According to claim 3, wherein the control server, with reference to the specific driving PVD to display or support to display on the control display of the control system a screen on which the location of the autonomous vehicle is displayed on a map including the specific road section However, the second connection information allowing the manager of the control system to access (i) the vehicle status information of the autonomous driving vehicle and (ii) the verification status information of the autonomous driving vehicle A method of verifying the operation of an autonomous vehicle, comprising at least one of connection information, and displaying or supporting display on the control display.
| 5. According to claim 4, When the first connection information is selected by the manager, the control server, (i) information related to the state of the internal device of the autonomous vehicle included in the vehicle state information and the autonomous driving at least one of information related to the driving state of the vehicle is displayed or supported on the control display; The method of verifying the operation of an autonomous vehicle, characterized in that the display or support to display the traffic signal information including at least one of specific speed limit information and specific traffic sign information on the control display.
| 5. The method of claim 4, wherein when the second connection information is selected by the manager, the control server, the information on the specific verification section, the information on the specific operation event, the first verification result information, the second verification The method of verifying the operation of the autonomous vehicle, characterized in that the verification state information including at least one of result information and the third verification result information is displayed or supported on the control display.
| 7. According to claim 1, wherein the first verification result information is specific information input by the user - The specific information is detailed information about the failure (fail) with respect to the specific operation event of the autonomous vehicle Information corresponding to - A method of verifying the operation of an autonomous vehicle comprising:
| 8. According to claim 1, wherein the control server, (i) When obtaining the third verification result information corresponding to the case where the first verification result information and the second verification result information match, the control server responds to the specific operation event the verification is completed, and (ii) when the third verification result information corresponding to the case where the first verification result information and the second verification result information do not match is obtained, the first verification result information, the A method of verifying the operation of the autonomous vehicle, characterized in that the second verification result information and the third verification result information are reported and stored.
| 9. The method of claim 1, wherein the control server repeats steps (a) to (c) for each of the plurality of verification sections.
| 10. The method of claim 9, wherein the verification of the operation of the autonomous vehicle is repeated for each of the plurality of verification sections to count the cycle order for the specific road section, and the control server sets as a mission for the specific road section With reference to the mission order data, it is determined whether the circulating order satisfies the mission order data, and if the order does not satisfy the mission order data, verification of the operation of the autonomous vehicle is performed on the plurality of A method of verifying the operation of an autonomous vehicle, characterized in that the number of cycles is increased for the specific road section by repeating for each verification section.
| 11. In the control server that verifies the operation of an autonomous vehicle using a QC (Quality Control) verification app, in a state in which the driving PVD (Prove Vehicle Data) of the autonomous vehicle is being transmitted to the control server, in a specific road section a database in which a specific verification scenario is divided into a plurality of verification sections, and a reference PVD (Probe Vehicle Data) for each operation event in which the autonomous vehicle must operate for each verification section is stored; at least one memory storing instructions; and at least one processor configured to execute the instructions.
, wherein the processor includes: (1) specific driving PVD (Prove Vehicle Data) transmitted from the autonomous driving vehicle - The specific driving PVD is included in a specific verification section in which the autonomous driving vehicle is one of the plurality of verification sections The process of acquiring - which is the driving PVD of the autonomous vehicle corresponding to the execution of a specific motion event; (2) (i) First verification result information transmitted by the user riding in the autonomous vehicle - The first verification result information is the autonomy for the specific operation event input by the user through the QC verification app A process of obtaining - which is information on the success or failure of the operation performed by the driving vehicle; A process of obtaining second verification result information, which is a result of determining whether or not matching, and (3) determining whether the first verification result information and the second verification result information match are performed by the autonomous vehicle for the specific operation event A control server for verifying the operation of an autonomous vehicle, characterized in that it performs a process of obtaining third verification result information, which is the final verification result information of one operation.
| 12. The method of claim 11 , wherein each of the reference PVD and the driving PVD is used for (i) a standard field indicating a data item of the autonomous vehicle standardized for V2X communication and (ii) verifying each of the plurality of operation events A control server for verifying the operation of the autonomous vehicle, characterized in that it includes a non-standard field indicating the data item of the autonomous vehicle.
| 13. The method of claim 11, wherein the processor additionally acquires traffic signal information transmitted from a plurality of traffic signal controllers installed in the specific road section, (i) the autonomous vehicle performs V2X communication with the plurality of traffic signal controllers. an indirect acquisition method of acquiring the traffic signal information through the A control server for verifying the operation of an autonomous vehicle, characterized in that the traffic signal information is acquired by at least one of direct acquisition methods for acquiring the traffic signal information through direct communication with a traffic signal controller.
| 14. The method according to claim 13, wherein the processor supports displaying or displaying a screen on which the location of the autonomous vehicle is displayed on a map including the specific road section with reference to the specific driving PVD on the control display of the control system. , (i) first connection information allowing the manager of the control system to access vehicle status information of the autonomous driving vehicle, and (ii) second connection allowing access to verification status information of the autonomous driving vehicle A control server for verifying the operation of an autonomous vehicle, comprising at least one of information and displaying or supporting display on the control display.
| 15. The method of claim 14, wherein the processor, when the first connection information is selected by the manager, (i) information related to the state of the internal device of the autonomous vehicle included in the vehicle state information and the autonomous vehicle at least one of information related to the driving state of the vehicle is displayed or supported on the control display; The control server for verifying the operation of the autonomous vehicle, characterized in that the display or support to display the traffic signal information including at least one of speed limit information and specific traffic sign information on the control display.
| 16. The method of claim 14, wherein the processor, when the second connection information is selected by the manager, information on the specific verification section, information on the specific operation event, the first verification result information, and the second verification result The control server for verifying the operation of the autonomous vehicle, characterized in that the display or support to display the verification state information including at least one of information and the third verification result information on the control display.
| 12. The method of claim 11, wherein the first verification result information is specific information input by the user - the specific information is detailed information about the failure of the specific operation event of the autonomous vehicle It is information corresponding to - A control server that verifies the operation of the autonomous vehicle, characterized in that it includes.
| 18. The method of claim 11 , wherein the processor (i) obtains the third verification result information corresponding to a case in which the first verification result information and the second verification result information are identical to each other, When the verification is processed as completed, and (ii) the third verification result information corresponding to the case where the first verification result information and the second verification result information do not match is obtained, the first verification result information, the second verification result information A control server that verifies the operation of the autonomous vehicle, characterized in that the second verification result information and the third verification result information are reported and stored.
| 19. The control server of claim 11 , wherein the processor repeats steps (1) to (3) for each of the plurality of verification sections.
| 20. The method of claim 19, wherein the processor repeats the verification of the operation of the autonomous vehicle for each of the plurality of verification sections, counts the circulation order for the specific road section, and is set as a mission for the specific road section With reference to the existing mission order data, it is determined whether the circulating order satisfies the mission order data, and if the order does not satisfy the mission order data, the verification of the operation of the autonomous vehicle is performed by the plurality of verifications. A control server for verifying the operation of an autonomous driving vehicle, characterized in that by repeating for each section and increasing the circulation order for the specific road section. | The method involves transmitting the specific driving prove vehicle data (PVD) from an autonomous driving vehicle (600) by a control server (100). The second verification result information which determines whether the operation performed by the autonomous driving vehicle for the event is successful is obtained and a second verification result information, which is a result of determining whether the specific reference PVD is matched is obtained. A determination is made to check whether the first verification result information and the second verification result information match, and a third information that is final verification result information of the operation performed by the autonomous vehicle with respect to the specific operation event obtaining verification result information. An INDEPENDENT CLAIM is included for a control server for verifying operation of autonomous vehicle using quality control verification app. Method for verifying operation of autonomous vehicle using quality control verification app. The method enables verifying the operation of the autonomous driving vehicle by using data directly confirmed by a user riding in the autonomous vehicle, so that monitoring of information related to verifying operation of an autonomous vehicle through a control display of a control system can be enabled. The drawing shows a schematic view of a control server that verifies the operation of an autonomous vehicle. 100Control server110Memory600Autonomous driving vehicle700Terminal900Database | Please summarize the input |
METHOD FOR PREVENTING POSSIBLE MALFUNCTIONS OF DCU OCCURING DURING AUTONOMOUS DRIVING BY REFERRING TO ADS USING OUTPUTS OF HETEROGENEOUS DCUS AND METHOD USING THE SAMEDisclosed is a method of supporting the analysis of the DCUs in order to prevent a misjudgment situation of the DCUs that may occur in autonomous driving by using an Anomaly Detection System (ADS) for a heterogeneous Domain Control Unit (DCU). That is, (a) the computing device operating in conjunction with the target vehicle that performs autonomous driving, a predetermined first to Nth time point corresponding to the autonomous driving state-N is an integer greater than or equal to 1-to the target vehicle. Allowing the mounted sensor module to acquire first situation information to Nth situation information about a situation around the target vehicle; (b) the computing device allows at least some of the first DCU to M-th DCU operating in conjunction with the computing device-M is an integer of 2 or more-which is one of the first context information to the N-th context information. Generating at least a part of K_1th determination information to K_Mth determination information with reference to K context information-K is an integer of 1 or more and N or less; And (c) the computing device causes the ADS to operate in conjunction with the K_1-th determination information to at least part of the first DCU to the M-th DCU with reference to at least a part of the K_1th determination information to the K_Mth determination information. At least a part of the first DCU to the M-th DCU can be analyzed by calculating the K-th determination match degree for and by causing an edge logger to tag and store the K-th situation information with reference to the K-th decision match degree. Disclosed is a method comprising the step of assisting to perform.|1. (a) A computing device that operates in conjunction with a target vehicle performing autonomous driving, a predetermined first point to an Nth point in time corresponding to the autonomous driving state-N is an integer greater than or equal to 1-a sensor mounted on the target vehicle for each Allowing the module to acquire first situation information to Nth situation information about a situation around the target vehicle;
(b) the computing device allows at least some of the first DCU to M-th DCU operating in conjunction with the computing device-M is an integer of 2 or more-which is one of the first context information to the N-th context information. Generating at least a part of K_1th determination information to K_Mth determination information with reference to K context information-K is an integer of 1 or more and N or less; And (c) the computing device causes the ADS operating in conjunction therewith to refer to at least a portion of the K_1th determination information to the K_Mth determination information to determine at least a portion of the first DCU to the Mth DCU. Calculate the K-th decision match degree, and cause the edge logger to tag and store the K-th situation information with reference to the K-th decision match degree so that at least a portion of the first DCU to the M-th DCU can be analyzed. A method comprising the step of supporting.
| 2. The method of claim 1, wherein in step (c), the computing device causes the ADS to apply a Dynamic Time Warping algorithm according to the following equation to at least a part of the K_1th determination information to the K_Mth determination information Calculate the degree of agreement of the K-th determination for at least some of the first DCU to the M-th DCU, In the above formula, Denotes a first specific time series vector including information on at least one of the K_1th determination information to the K_Mth determination information, Means a second specific time series vector including information on the other one of the K_1th determination information to the K_Mth determination information.
| 3. The method of claim 1, wherein (d) the computing device causes the edge logger to analyze a predetermined log when tag information of the K-th context information indicates that at least a part of the matching degree of the K-th determination is less than a threshold value. The Kth situation information and the K_1th determination information to the K_Mth determination information are transmitted to the system, and the log analysis system causes the Kth situation information and the K_1th determination information to the Kth determination information through a predetermined display device. The step of supporting the manager to analyze at least some of the process processes of the first DCU to the M-th DCU at the K-th point in time corresponding to the K-th situation information by transmitting K_M determination information to the manager. How to characterize.
| 4. The method of claim 3, wherein (e) when the computing device obtains analysis information about a problem in a specific DCU among the first DCU to the Mth DCU from the log analysis system, the analysis information is referred to And modifying the algorithm of the specific DCU.
| 5. The method of claim 1, wherein in the step (a), the computing device causes the sensor module including at least some of a camera, a radar, a lidar, a GPS, and a V2X communication module to cause the first situation information to the Nth situation. A method, characterized in that to obtain information.
| 6. The method of claim 1, wherein at least some of the first DCU to the M-th DCU are implemented in the form of a neural network consisting of a plurality of layers each including a plurality of virtual neurons, and the other part is in the form of a rule-based algorithm. Implemented, wherein each of the first DCU to the Mth DCU outputs results according to different logics.
| 7. The method of claim 1, wherein the step (b) comprises: at least one of K_1th determination information to K_Mth determination information generated from at least one preset main DCU among the first DCU to the Mth DCU. And transmitting one main determination information to an actuator of the target vehicle to support the target vehicle to perform the autonomous driving according to the main determination information.
| 8. In a computing device that supports analysis of the DCUs to prevent a misjudgment situation of the DCUs that may occur in autonomous driving by using an Anomaly Detection System (ADS) for heterogeneous Domain Control Units (DCUs), instructions are provided. One or more memories to store; And one or more processors configured to perform the instructions, wherein the processor includes: (I) a predetermined first to Nth time point corresponding to the autonomous driving state-N is an integer greater than or equal to 1-each mounted on the target vehicle A process of causing the sensor module to acquire first situation information to Nth situation information about a situation around the target vehicle; (II) The first DCU to the M-th DCU operating in conjunction with the computing device-M is an integer greater than or equal to 2-allowing at least some of the K-th context information, which is one of the first context information to the N-th context information-K Is an integer equal to or greater than 1 and equal to or less than N; a process of generating at least some of the K_1th determination information to the K_Mth determination information with reference to; And (III) the ADS operating in conjunction therewith, with reference to at least a part of the K_1-th determination information to the K_M-th determination information, and the K-th determination match degree with respect to at least a portion of the first DCU to the M-th DCU. A process for supporting at least some of the first DCU to the M DCU to be analyzed by calculating and storing the K-th context information by tagging and storing the K-th situation information with reference to the K-th determination matching degree by an edge logger. Device characterized in that performing.
| 9. The method of claim 8, wherein in the (III) process, the processor causes the ADS to apply a Dynamic Time Warping algorithm according to the following equation to at least a part of the K_1th determination information to the K_Mth determination information 1 DCU to calculate the K-th determination agreement for at least a portion of the M-th DCU, In the above formula, Denotes a first specific time series vector including information on at least one of the K_1th determination information to the K_Mth determination information, Means a second specific time series vector including information on the other one of the K_1-th determination information to the K_M-th determination information.
| 10. The system of claim 8, wherein (IV) the processor, when the edge logger causes the tag information of the K-th context information to indicate that at least a part of the matching degree of the K-th determination is less than or equal to a threshold value, The K-th situation information and the K_1-th determination information to the K_M-th determination information are transmitted to the K-th situation information and the K-th determination information to the K_Mth determination information through a predetermined display device to the log analysis system. By transmitting the determination information to the manager, further performing a process of supporting the manager to analyze at least some of the process processes of the first DCU to the M-th DCU at the K-th time point corresponding to the K-th situation information. Device.
| 11. The method of claim 10, wherein, when the processor (V) obtains analysis information about a problem in a specific DCU among the first DCU to the Mth DCU from the log analysis system, the analysis information is referred to An apparatus, characterized in that further performing a process of modifying an algorithm of a specific DCU.
| 12. The method of claim 8, wherein in the (I) process, the processor causes the sensor module including at least some of a camera, a radar, a lidar, a GPS, and a V2X communication module to cause the first situation information to the Nth situation information. Device, characterized in that to obtain.
| 13. The method of claim 8, wherein at least some of the first DCU to the M-th DCU are implemented in the form of a neural network consisting of a plurality of layers each including a plurality of virtual neurons, and the other part is in the form of a rule-based algorithm. Embodied, wherein each of the first DCU to the Mth DCU outputs results according to different logics.
| 14. The method of claim 8, wherein in the (II) process, the processor comprises at least one of K_1th determination information to K_Mth determination information generated from at least one preset main DCU among the first DCU to the Mth DCU. And transmitting the main determination information of the target vehicle to an actuator of the target vehicle to support the target vehicle to perform the autonomous driving according to the main determination information. | The method involves allowing a sensor module (210) mounted on the target vehicle to obtain first situation information to N-th situation information about a situation around the target vehicle where N is integer number. The portion of first domain control unit (DCU) to M DCU-M (220-1-220-M) is caused to operate in conjunction with the computing device to generate portion of the K-1 determination information to the K-M determination information where K is an integer of 1 or more and M is integer of 2 or more. The anomaly detection system (ADS) (130) is allowed to calculate a K decision match for portion of the first DCU to the M DCU with reference to portion of the K-1 determination information to the K-M determination information. The edge logger (140) is supported to tag the K state information with reference to the K determination match so that portion of the first DCU to the M DCU is analyzed. An INDEPENDENT CLAIM is included for a device for preventing misjudgment situation of DCU occurring during autonomous driving of vehicle. Method for preventing misjudgment situation of DCU occurring during autonomous driving of vehicle on road. The misjudgment situation of DCU occurring during autonomous driving of vehicle is prevented effectively by using ADS for heterogeneous DCU. The problem of a specific DCU is checked to correct the algorithm of a specific DCU. The drawing shows a schematic diagram of the computing device. (Drawing includes non-English language text) 130ADS140Edge logger210Sensor module220-1-220-MDCU230Actuator | Please summarize the input |
SYSTEMS AND METHODOLOGY FOR VOICE AND/OR GESTURE COMMUNICATION WITH DEVICE HAVING V2X CAPABILITYA system includes a first communication module for receiving a user message, a processing unit for converting the user message to a vehicle-to-everything (V2X) message, and a second communication module. The first communication module, the processing unit, and the second communication modules are implemented in a first vehicle. The second communication module is configured to transmit the V2X message from the first vehicle via a wireless communication link. The first vehicle may be a drone configured to communicate with a user device positioned on or near a user, and the user message may be an audible message or user gestures. Alternatively, the first vehicle may be inhabited by the user, with the user message being an audible message. The system may enable communication with an autonomous vehicle or another device equipped with V2X capability.|1. A system comprising:
* a first communication module configured to receive a user message;
* a processing unit configured to convert the user message to a vehicle-to-everything (V2X) message; and
* a second communication module, wherein the first communication module, the processing unit, and the second communication module are implemented in a first vehicle, and the second communication module is configured to transmit the V2X message from the first vehicle via a wireless communication link.
| 2. The system of claim 1 wherein the wireless communication link is a first wireless communication link, and the system further comprises an electronic device configured to be positioned proximate a user, the electronic device including a third communication module, wherein the first and third communication modules are configured to enable a second wireless communication link between first vehicle and the electronic device for communication of the user message from the user to the first vehicle.
| 3. The system of claim 2 wherein the first vehicle is an unmanned vehicle, and:
* the electronic device comprises:
* a first wearable structure configured to be positioned on the user, the first wearable structure including the third communication module, wherein the first and third communication modules are configured to enable the second wireless communication link between the unmanned vehicle and the first wearable structure; and
* a second wearable structure configured to be positioned on the user, the second wearable structure being physically displaced away from the first wearable structure, the second wearable structure including a fourth communication module, wherein the first and fourth communication modules are configured to enable a third wireless communication link between the unmanned vehicle and the second wearable structure;
* the processing unit implemented in the unmanned vehicle is further configured to determine a current location of the unmanned vehicle relative to the user in response to the second and third wireless communication links; and
* the system further comprises a drive control unit in communication with the processing unit and configured to adjust a speed and a position of the unmanned vehicle to move the unmanned vehicle from the current location to a predefined location relative to the user.
| 4. The system of claim 3 wherein the predefined location is included in the user message, the user message is an audible message from the user, and at least one of the first and second wearable structures comprises a microphone configured to capture the audible message from the user and at least one of the third and fourth communication modules is configured to communicate the audible message with the predefined location via at least one of the second and third communication links.
| 5. The system of any preceding clam further comprising a camera implemented in the first vehicle and configured to capture motion of a user and provide visual information of the user to the processing unit, wherein the processing unit is further configured to determine the user message from the visual information.
| 6. The system of any preceding claim wherein the first vehicle is an unmanned vehicle, and the system further comprises a camera implemented in the unmanned vehicle and configured to capture an ambient environment visible from the camera and provide visual information of the ambient environment to the user, and the user message is an audible message from the user responsive to the visual information.
| 7. The system of any preceding claim wherein the user message is a first user message, the V2X message is a first V2X message, and:
* the second communication module is further configured to receive a second V2X message via the first wireless communication link;
* the processing unit is further configured to convert the second V2X message to a second user message for communication of the second user message from the first vehicle to the electronic device; and
* the electronic device further comprises a speaker configured to output the second user message as an audible message to the user.
| 8. The system of any preceding claim wherein a user is positioned in the first vehicle and the first communication system comprises a microphone for capturing the user message as an audible message from the user, wherein the processing unit is configured to convert the user message to the V2X message for transmission via the wireless communication link.
| 9. The system of claim 8 wherein the V2X message is configured for transmission to a second vehicle having at least semi-autonomous motion capability, the user message includes a voice command from the user configured to influence navigation of the second vehicle, and the V2X message includes the voice command for commanding navigation of the second vehicle.
| 10. The system of claim 8 or 9 wherein the user message is a first user message, the V2X message is a first V2X message, and:
* the second communication module is further configured to receive a second V2X message via the wireless communication link;
* the processing unit is further configured to convert the second V2X message to a second user message; and
* the system further comprises a speaker implemented in the first vehicle configured to output the second user message as an audible message to the user.
| 11. The system of any preceding claim wherein:
* the first communication module is configured to implement a first wireless communication technology to enable receipt of the user message; and
* the second communication module is configured to implement a second wireless communication technology to enable transmission of the V2X message, the second wireless communication technology differing from the first wireless communication technology.
| 12. A method comprising:
* receiving a user message at a first vehicle;
* converting the user message to a vehicle-to-everything (V2X) message at the first vehicle; and
* transmitting the V2X message from the first vehicle via a wireless communication link.
| 13. The method of claim 12 wherein the wireless communication link is a first wireless communication link, and the method further comprises:
* enabling a second wireless communication link between the first vehicle and an electronic device positioned proximate a user for communication of the user message from the user to the first vehicle.
| 14. The method of claim 13 wherein the first vehicle is an unmanned vehicle, and the method further comprises:
* positioning first and second wearable structures of the electronic device on the user, the first and second wearable structures being physically displaced away from one another;
* enabling a second wireless communication link between the first wearable structure and the unmanned vehicle;
* enabling a third wireless communication link between the second wearable structure and the unmanned vehicle;
* determining a current location of the unmanned vehicle relative to the target in response to the second and third wireless communication links; and
* adjusting a speed and a position of the unmanned vehicle to move the unmanned vehicle from the current location to a predefined location relative to the user.
| 15. The method of claim 14 further comprising:
* capturing an audible message from the user at the electronic device, the predefined location being included in the audible message; and
* communicating the audible message with the predefined location via at least one of the second and third communication links. | The system comprises a first communication module to receive a user message. A processing unit is provided to convert the user message to a vehicle-to-everything message. The first and second communication modules, the processing unit, and the second communication module are implemented in a first vehicle. The second communication is provided for transmitting the V2X message from the first vehicle through a wireless communication link. A camera implemented in vehicle and provided to capture motion of user and provide visual information of user to processing unit. An INDEPENDENT CLAIM is included for a method for enabling communication between human users and vehicles. System for enabling communication between human users and vehicles. System for enabling communication between human users and vehicles having semi-autonomous motion capability and other devices equipped with V2X capability by conversion of user messages e.g. voice and gesture, to vehicle-to-everything messages and vice versa. The drawing shows a schematic view of a system for enabling communication between human users and vehicles.22Electronic device 24Appropriate user 26On-board drone 30First wearable structure 41First location | Please summarize the input |
IDENTIFYING A STOPPING PLACE FOR AN AUTONOMOUS VEHICLEAmong other things, a vehicle is caused to drive autonomously through a road network toward a defined goal position. Current information is analyzed about potential stopping places in the vicinity of the goal position, to make a choice of a currently selected stopping place that is acceptable and feasible. The vehicle is caused to drive autonomously toward the currently selected stopping place. The activities are repeated until the vehicle stops at a currently selected stopping place. | The method involves causing a vehicle (10) to drive autonomously through a road network toward a defined goal position (102). Current information about potential stopping places in vicinity of the goal position is analyzed to make choice of a currently selected stopping place that is acceptable and feasible by applying a predefined strategy for choosing the currently selected stopping place. The vehicle is caused to drive autonomously toward the currently selected stopping place until the vehicle stops at the currently selected stopping place. An INDEPENDENT CLAIM is also included for an autonomous vehicle. Method for identifying stopping places for an autonomous vehicle (claimed). The method enables providing the passenger with option of switching the autonomous vehicle from an autonomous mode to a partially or fully manual mode, so that the passenger can locate an acceptable feasible stopping place. The method enables providing autonomous driving capability to safely and reliably drive through a road environment to the goal position while avoiding vehicles, pedestrians, cyclists, and other obstacles and obeying rules of the road. The drawing shows a schematic view of a map. 10Vehicle100Selected stopping point102Defined goal position104Passenger132Obstacle | Please summarize the input |
Identifying a stopping place for an autonomous vehicleAmong other things, stored data is maintained indicative of potential stopping places that are currently feasible stopping places for a vehicle within a region. The potential stopping places are identified as part of static map data for the region. Current signals are received from sensors or one or more other sources current signals representing perceptions of actual conditions at one or more of the potential stopping places. The stored data is updated based on changes in the perceptions of actual conditions. The updated stored data is exposed to a process that selects a stopping place for the vehicle from among the currently feasible stopping places.The invention claimed is:
| 1. A computer-implemented method comprising:
receiving, by one or more processors, static map data for a region, wherein the static map data identifies one or more potential stopping places for a vehicle within the region;
maintaining, by the one or more processors, stored data indicative of one or more currently feasible stopping places for the vehicle within the region, wherein the one or more currently feasible stopping places area subset of the one or more potential stopping places, and wherein at least one potential stopping place of the one or more potential stopping places is determined to be a currently feasible stopping place based on:
an amount of time elapsed since the potential stopping place was determined to be infeasible for parking stopping exceeding a first threshold value,
a reason for the determination that the potential stopping place is infeasible for stopping, and
at least one of a historical level of demand for parking in a vicinity of the potential stopping place being less than a second threshold value or traffic volume in the vicinity of the potential stopping place being less than a third threshold value;
receiving from one or more sensors or one or more other sources current signals representing perceptions of actual conditions at the one or more currently feasible stopping places;
updating, by the one or more processors, the stored data based on the perceptions of actual conditions to include one or more updated currently feasible stopping places; and
exposing, by the one or more processors, the updated stored data to a process that selects a stopping place for the vehicle from among the one or more updated currently feasible stopping places.
| 2. The method of claim 1 comprising:
discretizing, by the one or more processors, the one or more potential stopping places as a finite number of points within the region.
| 3. The method of claim 2 comprising: defining, by the one or more processors, the potential stopping place as a shape containing at least one of the points, the potential stopping place configured to accommodate a footprint of the vehicle.
| 4. The method of claim 3 comprising: attributing, by the one or more processors, an orientation to the shape, the orientation corresponding to a direction of traffic flow at the potential stopping place.
| 5. The method of claim 2 comprising:
initializing, by the one or more processors, the one or more potential stopping places as one or more stopping places expected to be feasible based on prior signals from the one or more sensors, the prior signals representing past perceptions of past actual conditions at some of the one or more potential stopping places.
| 6. The method of claim 1 in which the one or more sensors comprise at least one sensor that is physically located on the vehicle.
| 7. The method of claim 1 in which the one or more sensors comprise at least one sensor that is physically remote from the vehicle,
wherein the at least one sensor is located inside a parking garage.
| 8. The method of claim 1 in which the current signals received from the one or more sensors are received through vehicle-to-vehicle or vehicle-to-infrastructure communication.
| 9. The method of claim 1 in which the one or more other sources comprise crowd-sourced data sources.
| 10. The method of claim 1 in which the vehicle is part of a fleet of vehicles managed from a central server and the method comprises the server distributing information received from sensors at one of the vehicles to other vehicles of the fleet.
| 11. An autonomous vehicle, comprising:
one or more processors;
one or more sensors; and
one or more data storage devices including instructions that when executed by the one or more processors, cause the autonomous vehicle to perform functions comprising:
receiving static map data for a region, wherein the static map data identifies one or more potential stopping places for a vehicle within the region;
maintaining stored data indicative of one or more currently feasible stopping places for the vehicle within the region, wherein the one or more currently feasible stopping places are a subset of the one or more potential stopping places, and wherein a at least one potential stopping place of the one or more potential stopping places is determined to be a currently feasible stopping place based on:
an amount of time elapsed since the potential stopping place was determined to be infeasible for stopping exceeding a first threshold value,
a reason for the determination that the potential stopping place is infeasible for stopping, and
at least one of a historical level of demand for parking in a vicinity of the potential stopping place being less than a second threshold value or traffic volume in the vicinity of the potential stopping place being less than a third threshold value;
receiving from the one or more sensors or one or more other sources current signals representing perceptions of actual conditions at the one or more currently feasible stopping places;
updating the stored data based on the perceptions of actual conditions to include one or more updated currently feasible stopping places; and exposing the updated stored data to a process that selects a stopping place for the vehicle from among the one or more updated currently feasible stopping places.
| 12. The autonomous vehicle of claim 11, wherein the functions comprise:
initializing the potential stopping places as all of the potential stopping places identified as part of the static map data for the region.
| 13. The autonomous vehicle of claim 11, wherein the functions comprise:
discretizing the potential stopping places as a finite number of points within the region corresponding to potential stopping places.
| 14. The autonomous vehicle of claim 13, wherein the functions comprise:
defining a potential stopping place as a shape containing one of the points, the shape corresponding to a footprint of the vehicle.
| 15. The autonomous vehicle of claim 14, wherein the functions comprise:
attributing an orientation to the shape, the orientation corresponding to a direction of traffic flow.
| 16. The autonomous vehicle of claim 13, wherein the functions comprise:
initializing the potential stopping places as potential stopping places expected to be feasible based on prior signals from the one or more sensors representing perceptions of actual conditions at one or more of the potential stopping places.
| 17. The autonomous vehicle of claim 11 in which the current signals received from the one or more sensors are received through vehicle-to-vehicle or vehicle-to-infrastructure communication.
| 18. A non-transitory computer readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to perform functions comprising:
receiving static map data for a region, wherein the static map data identifies one or more potential stopping places for a vehicle within the region;
maintaining stored data indicative of one or more currently feasible stopping places for the vehicle within the region, wherein the one or more currently feasible stopping places are a subset of the one or more potential stopping places, and wherein at least one potential stopping place of the one or more potential stopping places determined to be a currently feasible stopping place based on:
an amount of time elapsed since the potential stopping place was determined to be infeasible for stopping exceeding a first threshold value,
a reason for the determination that the potential stopping place is infeasible for stopping, and
at least one of a historical level of demand for parking in a vicinity of the potential stopping place being less than a second threshold value or traffic volume in the vicinity of the potential stopping place being less than a third threshold value;
receiving from one or more sensors or one or more other sources current signals representing perceptions of actual conditions at the one or more currently feasible stopping places;
updating the stored data based on the perceptions of actual conditions to include one or more updated currently feasible stopping places; and
exposing the updated stored data to a process that selects a stopping place for the vehicle from among the one or more updated currently feasible stopping places. | The computer-based method involves maintaining stored data indicative of potential stopping places that are currently feasible stopping places for a vehicle within a region. The sensors (24) or several other sources current signals are received that represents perceptions of actual conditions at several potential stopping places. The stored data is updated based on changes in the perceptions of actual conditions. The updated stored data is exposed to a process that selects a stopping place for the vehicle from among the currently feasible stopping places. Method for identifying stopping places for autonomous vehicle. The information received from the device of the passenger includes an indication that the time spent searching for an acceptable stopping place is acceptable to the passenger. The set of elements or components located on an autonomous vehicle or at other locations that enables an autonomous vehicle to operate. The stopping place that are closer to curbs are generally preferred as they allow the passenger to access the activity AV more easily. The drawing shows a block diagram of the method of identifying stopping places for autonomous vehicle. 10Automonus vehicle12Road environment14Global position24Sensors34Data base | Please summarize the input |
INTERVENTION IN OPERATION OF A VEHICLE HAVING AUTONOMOUS DRIVING CAPABILITIESAmong other things, a determination is made that intervention in an operation of one or more autonomous driving capabilities of a vehicle is appropriate. Based on the determination, a person is enabled to provide information for an intervention. The intervention is caused in the operation of the one or more autonomous driving capabilities of the vehicle.|1. A vehicle comprising:
at least one processor; and
a non-transitory computer-readable storage medium storing instructions which when executed by the at least one processor cause the at least one processor to:
operate the vehicle in an autonomous mode;
receive a command using a vehicle-to-infrastructure (V2I) communication device of the vehicle, the command instructing the vehicle to maneuver to a goal location;
determine that the vehicle is unable to convert the command into machine instructions to operate the vehicle to maneuver to the goal location; and
responsive to determining that the vehicle is unable to convert the command into machine instructions, transmit a teleoperation request to a teleoperation server.
| 2. The vehicle of claim 1, wherein the teleoperation request comprises a current location of the vehicle.
| 3. The vehicle of claim 1, wherein the teleoperation request comprises one or more trajectory sampling points for the vehicle.
| 4. The vehicle of claim 1, wherein maneuvering to the goal location comprises:
treating a current location of the vehicle as prior knowledge; and
using an inference algorithm to update the a current location of the vehicle based on the command.
| 5. The vehicle of claim 1, wherein maneuvering to the goal location comprises inferring a speed profile from the command.
| 6. The vehicle of claim 1, wherein maneuvering to the goal location comprises inferring a steering angle from the command using a learning algorithm.
| 7. The vehicle of claim 1, wherein converting the command into machine instructions comprises enabling, editing or disabling a hardware component or a software process.
| 8. The vehicle of claim 1, wherein converting the command into machine instructions comprises overwriting a travel preference or a travel rule.
| 9. The vehicle of claim 1, wherein converting the command into machine instructions comprises editing data comprising one or more of a map, sensor data in the vehicle or a related AV system, trajectory data in the vehicle or a related AV system, vision data in the vehicle or a related AV system, or any past data in the vehicle or a related AV system.
| 10. A non-transitory computer-readable storage medium storing instructions, which when executed by one or more processors cause the one or more processors to:
operate a vehicle in an autonomous mode;
receive a command using a vehicle-to-infrastructure (V2I) communication device of the vehicle, the command instructing the vehicle to maneuver to a goal location;
determine that the vehicle is unable to convert the command into machine instructions to operate the vehicle to maneuver to the goal location; and
responsive to determining that the vehicle is unable to convert the command into machine instructions, transmit a teleoperation request to a teleoperation server.
| 11. The non-transitory computer-readable storage medium of claim 10, wherein the teleoperation request comprises a current location of the vehicle.
| 12. The non-transitory computer-readable storage medium of claim 10, wherein the teleoperation request comprises one or more trajectory sampling points for the vehicle.
| 13. The non-transitory computer-readable storage medium of claim 10, wherein maneuvering to the goal location comprises:
treating a current location of the vehicle as prior knowledge; and
using an inference algorithm to update the a current location of the vehicle based on the command.
| 14. The non-transitory computer-readable storage medium of claim 10, wherein maneuvering to the goal location comprises inferring a speed profile from the command.
| 15. The non-transitory computer-readable storage medium of claim 10, wherein maneuvering to the goal location comprises inferring a steering angle from the command using a learning algorithm.
| 16. The non-transitory computer-readable storage medium of claim 10, wherein converting the command into machine instructions comprises enabling, editing or disabling a hardware component or a software process.
| 17. The non-transitory computer-readable storage medium of claim 10, wherein converting the command into machine instructions comprises overwriting a travel preference or a travel rule.
| 18. The non-transitory computer-readable storage medium of claim 10, wherein converting the command into machine instructions comprises editing data comprising one or more of a map, sensor data in the vehicle or a related AV system, trajectory data in the vehicle or a related AV system, vision data in the vehicle or a related AV system, or any past data in the vehicle or a related AV system.
| 19. A method comprising:
operating, by one or more processors, a vehicle in an autonomous mode;
receiving, by the one or more processors, a command using a vehicle-to-infrastructure (V2I) communication device of the vehicle, the command instructing the vehicle to maneuver to a goal location;
determining, by the one or more processors, that the vehicle is unable to convert the command into machine instructions to operate the vehicle to maneuver to the goal location; and
responsive to determining that the vehicle is unable to convert the command into machine instructions, transmitting, by the one or more processors, a teleoperation request to a teleoperation server.
| 20. The method of claim 19, wherein the teleoperation request comprises a current location of the vehicle. | The vehicle (10) comprises one processor. A non-transitory computer-readable storage medium stores instructions which is executed by the one processor. A command is received using a vehicle-to-infrastructure (V2I) communication device of the vehicle. The command instructs the vehicle to maneuver to a goal location. Determines that the vehicle is unable to convert the command into machine instructions to operate the vehicle to maneuver to the goal location. A teleoperation request is transmitted to a teleoperation server. The teleoperation request provides a current location of the vehicle and multiple trajectory sampling points for the vehicle. INDEPENDENT CLAIMS are included for the following:a non-transitory computer-readable storage medium storing instructions; anda method involves operating a vehicle in an autonomous mode. Vehicle. Vehicle ensures the resulting transition exhibits smooth and gradual changes in driving orientations. The drawing shows a block diagram of the AV system. 10Vehicle24Sensor28Communication devices40Computing device42Processor44Interface devices | Please summarize the input |
V2V latency measurement reporting to traffic server for optimizing the inter vehicle distance for self-driving carsMethods and apparatus, including computer program products, are provided for autonomous vehicles. In one aspect there is provided a method. The method may include detecting, at an autonomous vehicle, at least one vehicle within a certain range of the autonomous vehicle; measuring a latency representative of a time to communicate via a wireless link to the at least one detected vehicle; reporting the measured latency to the network; and receiving, by the autonomous vehicle, information to enable the autonomous vehicle to determine an intervehicle distance for configuration at the autonomous vehicle. Related apparatus, systems, methods, and articles are also described.What is claimed:
| 1. A method, comprising:
detecting, at an autonomous vehicle, at least one vehicle within a certain range of the autonomous vehicle;
measuring a latency representative of a time for the at least one detected vehicle to respond to a message sent by the autonomous vehicle via a wireless link;
reporting, to a network, the measured latency; and
receiving, at the autonomous vehicle, information from the network, the information including an intervehicle distance for configuration at the autonomous vehicle, the intervehicle distance being determined at the network, and the intervehicle distance being determined based at least on the measured latency reported to the network.
| 2. The method of claim 1, wherein the measuring of the latency includes sending, by the autonomous vehicle, the message to the at least one detected vehicle, and wherein the latency is determined based at least on a first time when the autonomous vehicle sent the message and a second time when the autonomous vehicle receives, from the at least one detected vehicle, a response to the message.
| 3. The method of claim 2, further comprising:
in response to the at least one detected vehicle failing to respond to the message within a threshold quantity of time, reporting, to the network, an indication that the at least one detected vehicle is non-autonomous, wherein the intervehicle distance being determined further based on the reported indication.
| 4. The method of claim 1, wherein the information includes a value representative of the intervehicle distance for configuration at the autonomous vehicle.
| 5. The method of claim 1, further comprising:
configuring, by the autonomous vehicle, operation based on the intervehicle distance.
| 6. The method of claim 1, wherein the intervehicle distance represents a minimum and/or an optimum distance between the autonomous vehicle and the at least one vehicle.
| 7. An apparatus, comprising:
at least one processor; and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
detect, at the apparatus, at least one vehicle within a certain range of the apparatus;
measure a latency representative of a time for the at least one detected vehicle to respond to a message sent by the apparatus via a wireless link;
report, to a network, the measured latency; and
receive, at the apparatus, information from the network, the information including an intervehicle distance for configuration at the apparatus, the intervehicle distance being determined at the network, and the intervehicle distance being determined based at least on the measured latency reported to the network.
| 8. The apparatus of claim 7, wherein the apparatus measures the latency by at least sending, to the at least one detected vehicle, the message.
| 9. The apparatus of claim 8, wherein the latency is determined based at least on a first time when the apparatus sent the message and a second time when the apparatus receives, from the at least one detected vehicle, a response to the message.
| 10. The apparatus of claim 9, wherein the apparatus is further configured to at least:
in response to the at least one detected vehicle failing to respond to the message within a threshold quantity of time, report, to the network, an indication that the at least one detected vehicle is non-autonomous, the intervehicle distance being determined further based on the reported indication.
| 11. The apparatus of claim 8, wherein the apparatus reports, to the network, the measured latency in response to receiving the response from the at least one detected vehicle.
| 12. The apparatus of claim 7, wherein the received information includes a value representative of the intervehicle distance for configuration at the apparatus.
| 13. The apparatus of claim 7, wherein the apparatus is further configured to at least configure, based on the intervehicle distance, an operation of the apparatus.
| 14. The apparatus of claim 7, wherein the intervehicle distance represents a minimum and/or an optimum distance between the apparatus and the at least one detected vehicle.
| 15. An apparatus, comprising:
at least one processor; and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
receive, at the apparatus, a latency measurement representative of a time for at least one vehicle to respond to a message sent by an autonomous vehicle, via a wireless link, the at least one vehicle detected at the autonomous vehicle to be within a certain range of the autonomous vehicle, the latency measurement being determined by the autonomous vehicle;
determine, based at least on the received latency measurement, an intervehicle distance; and
send, to the autonomous vehicle, information including the intervehicle distance for configuration at the autonomous vehicle.
| 16. The apparatus of claim 15, wherein the intervehicle distance is determined further based on an indication that the at least one detected vehicle is non-autonomous.
| 17. The apparatus of claim 16, wherein the apparatus is further configured to a least receive, from the autonomous vehicle, the indication that the at least one detected vehicle is non-autonomous, and wherein the autonomous vehicle sends the indication in response to the at least one detected vehicle failing to respond to a message from the autonomous vehicle within a threshold quantity of time.
| 18. The apparatus of claim 15, wherein the intervehicle distance is determined further based on a road condition, a weather condition, a characteristic of the autonomous vehicle, and/or a characteristic of the at least one detected vehicle.
| 19. The apparatus of claim 15, wherein the information includes a value representative of the intervehicle distance for configuration at the autonomous vehicle. | The method (400) involves detecting (405) a vehicle within a certain range of an autonomous vehicle and measuring (415) a latency representative of a time to communicate through a wireless link to the detected vehicle. The measured latency is reported (420) to the network. The information is received (425) to enable the autonomous vehicle to determine an inter-vehicle distance for configuration at the autonomous vehicle. INDEPENDENT CLAIMS are included for the following:an apparatus for controlling autonomous vehicles; anda non-transitory computer-readable storage medium with program code for controlling autonomous vehicles. Method for controlling autonomous vehicles. The information is received to enable the autonomous vehicle to determine an inter-vehicle distance for configuration at the autonomous vehicle, thus traffic congestion is alleviated while improving road safety. The drawing shows a flowchart of a process for latency measurement reporting. 400Autonomous vehicle controlling method405Detecting a vehicle415Measuring a latency420Reporting the measured latency425Receiving the information | Please summarize the input |
Positioning system based on geofencing frameworkThis provides methods and systems for the global navigation satellite system (GNSS) combined with the dead-reckoning (DR) technique, which is expected to provide a vehicle positioning solution, but it may contain an unacceptable amount of error due to multiple causes, e.g., atmospheric effects, clock timing, and multipath effect. Particularly, the multipath effect is a major issue in the urban canyons. This invention overcomes these and other issues in the DR solution by a geofencing framework based on road geometry information and multiple supplemental kinematic filters. It guarantees a road-level accuracy and enables certain V2X applications which does not require sub-meter accuracy, e.g., signal phase timing, intersection movement assist, curve speed warning, reduced speed zone warning, and red-light violation warning. Automated vehicle is another use case. This is used for autonomous cars and vehicle safety, shown with various examples/variations.The invention claimed is:
| 1. A method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle, said method implemented by one or more processors, said method comprising:
receiving vehicle states for said vehicle, said vehicle states including at least data of a position, a speed, a heading angle and a yaw rate of said vehicle, said yaw rate having a yaw rate bias;
removing said yaw rate bias of said yaw rate;
responsive to removing said yaw rate bias of said yaw rate, determining whether a reference road exists, said reference road providing data of at least a road heading angle and a road curvature;
in case said reference road exists, determining whether said existing reference road is valid;
in case said reference road does not exist or said existing reference road is invalid, searching for said reference road;
in case said reference road is found based on said search or said existing reference road is valid, determining whether a lane change is detected for said vehicle;
in case said lane change is detected for said vehicle, performing retrospective integrations of said speed and yaw rate for said vehicle;
determining a reference yaw rate based on said road curvature and said speed of said vehicle;
determining whether a yaw rate error between said yaw rate and said reference yaw rate is less than a yaw rate threshold;
in case said yaw rate error is less than said yaw rate threshold, forcing said heading angle of said vehicle to said road heading angle;
updating said vehicle states;
determining geofencing conditions of said position, speed, heading angle and yaw rate of said vehicle;
determining whether said geofencing conditions are met;
in case said geofencing conditions are met, applying geofencing to limit said position of said vehicle between road boundaries of said reference road;
updating said data of said vehicle's position; and
outputting said data of said vehicle's position to an upper layer of said road vehicle navigation system for said vehicle.
| 2. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said road vehicle navigation system works with or communicates with a global navigation satellite system.
| 3. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said vehicle is interior to said reference road.
| 4. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein a distance from said vehicle to a next intersection is greater than a first threshold.
| 5. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said vehicle's speed is greater than a second threshold.
| 6. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein applying geofencing comprises: timely geofencing.
| 7. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein applying geofencing comprises: predicted geofencing.
| 8. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining an incorrect position of said vehicle and correcting the determined position for said vehicle based on reducing a lateral error.
| 9. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein in case said reference road does not exist or said existing reference road is invalid, said search for said reference road comprises:
determining candidate reference roads where said vehicle's position is interior to end points of said candidate reference roads;
determining, from said candidate reference roads, a candidate reference road satisfying a heading error below a threshold; and
adjusting an order of said end points to be consistent with a travel direction of said vehicle.
| 10. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining a lateral error for said vehicle.
| 11. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: in case said reference road is not found based on said search, outputting said data of said vehicle's position to said upper layer of said road vehicle navigation system for said vehicle.
| 12. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining a longitudinal error for said vehicle.
| 13. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining a predicted position for said vehicle based on at least one of lateral correction and longitudinal correction.
| 14. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining an average value of said yaw rate bias within a moving time window, and correcting said yaw rate bias based on said determined average value.
| 15. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining a sensor temperature, and determining said yaw rate bias that varies with said sensor temperature.
| 16. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: determining vibration or noise for removing said yaw rate bias.
| 17. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: applying a security layer for said road vehicle navigation system for said vehicle.
| 18. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: applying an application layer for said road vehicle navigation system for said vehicle.
| 19. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: applying a network layer for said road vehicle navigation system for said vehicle.
| 20. The method for positioning based on geofencing framework for a road vehicle navigation system for a vehicle as recited in claim 1, wherein said method comprises: applying a physical layer for said road vehicle navigation system for said vehicle. | The method involves determining whether a yaw rate error is small by a processor. A vehicle's heading angle is forced to a road heading angle when the yaw rate error is small. Vehicle states are updated by the processor. Geo-fencing conditions are evaluated by the processor. Determination is made whether the geo-fencing conditions are met. Geo-fencing for a vehicle is applied in case the geo-fencing conditions are met. Vehicle's position data is updated. The vehicle's position data is outputted to an upper layer of a road vehicle navigation system for the vehicle. Method for positioning a vehicle i.e. autonomous car, based on a geo-fencing framework for a road vehicle navigation system for safety. The method enables identifying vehicle movements accurately and generating better results in a shorter time period. The method enables guaranteeing road-level accuracy and providing vehicle-to-everything (V2X) applications to eliminate need to require signal phase timing, intersection movement assist, curve speed warning, reduced speed zone warning and red-light violation warning. The method enables providing a reference road i.e. line or curve, for connecting two adjacent intersections to correct a vehicle position and heading, thus providing necessary information for coordinates of end points of the reference road, road heading angle, curvature and road width. The method enables performing weighted-averaging process based on redundancies between coverage of different units, weighted-average of data for accurate results and more weights for more reliable units or sources or higher weights for results that are closer to a center of curve representing distribution of values, thus eliminating or reducing fringe results or erroneous data. The drawing shows a flow diagram illustrating a development of fully automated vehicles. | Please summarize the input |
DRIVE CONTROL METHOD AND DRIVE CONTROL DEVICEIn a drive control method for using a drive control device to control the operation of a host vehicle using at least two autonomous driving modes that have different levels of driving assistance. The drive control method includes shifting the autonomous driving mode from a first mode to a second mode in which the driving assistance level of the second mode is higher than the driving assistance level of the first mode upon detecting a preceding vehicle in front of a host vehicle while traveling in the first mode. In this drive control method, a detectable distance to the preceding vehicle for shifting to the second mode is greater than a followable distance to the preceding vehicle when following travel is permitted in the first mode.|1. A drive control method having at least two autonomous driving modes having different driving assistance levels, the drive control method comprising:
shifting the autonomous driving mode from a first mode to a second mode in which the driving assistance level of the second mode is higher than the driving assistance level of the first mode upon detecting a preceding vehicle traveling in front of the host vehicle while traveling in the first mode, wherein
a detectable distance to the preceding vehicle for shifting to the second mode is greater than a followable distance to the preceding vehicle when following travel is permitted in the first mode.
| 2. The drive control method according to claim 1, further comprising
calculating a reliability of the preceding vehicle using the drive control device based on a behavior of the preceding vehicle upon detecting the preceding vehicle while the operation of the host vehicle is controlled using the first mode, and
the drive control device not shifting the autonomous driving mode to the second mode upon determining the reliability of the preceding vehicle is less than a predetermined defined value.
| 3. The drive control method according to claim 2, wherein
the drive control device maintains the autonomous driving mode in the first mode upon determining the reliability of the preceding vehicle is less than the defined value.
| 4. The drive control method according to claim 2, wherein
the calculating of the reliability of the preceding vehicle using the drive control device is based on at least one of a lateral displacement amount of the preceding vehicle, a frequency of acceleration or deceleration, and a frequency of an illumination of brake lights.
| 5. (canceled)
| 6. The drive control method according to claim 1, wherein
an upper limit distance of the followable distance is a distance at which the host vehicle and the preceding vehicle can carry out vehicle-to-vehicle communication.
| 7. The drive control method according to claim 1, wherein
the drive control device detects another vehicle as a preceding vehicle traveling in front of the host vehicle when travel history information about the other vehicle is received and the travel history information that is received includes information indicating that the other vehicle was traveling at a point in front of the host vehicle within a prescribed period of time.
| 8. The drive control method according to claim 1, wherein
the drive control device does not shift the autonomous driving mode to the second mode when the host vehicle is traveling in the first mode and a vehicle speed of the host vehicle is greater than or equal to a prescribed speed.
| 9. The drive control method according to claim 1, wherein
the first mode is an autonomous driving mode that requires a driver to visually monitor the surrounding conditions of the host vehicle, and
the second mode is an autonomous driving mode in which the drive control device executes monitoring of the surrounding conditions of the host vehicle.
| 10. The drive control method according to claim 1, wherein
the first mode is a hands-on mode in which steering control by the control device does not operate when the driver is not holding the steering wheel, and
the second mode is a hands-off mode in which steering control by the drive control device operates even if the driver's hands leave the steering wheel.
| 11. The drive control method according to claim 1, wherein
another vehicle is excluded as a preceding vehicle when a ride height of the other vehicle traveling in front of the host vehicle is greater than a ride height of the host vehicle.
| 12. The drive control method according to claim 1, wherein
another vehicle is excluded as a preceding vehicle when the other vehicle traveling in front of the host vehicle is a two-wheeled vehicle.
| 13. The drive control method according to claim 1, wherein
the drive control device is configured to
shift the autonomous driving mode from the first mode to the second mode when a first preceding vehicle is present as the preceding vehicle in a first lane in which the host vehicle travels while the operation of the host vehicle is controlled using the first mode,
determine whether or not the first preceding vehicle and a second preceding vehicle are traveling in the first lane upon detecting a second preceding vehicle traveling in front of the first preceding vehicle is also present in the first lane,
cause the host vehicle to travel behind the second preceding vehicle upon determining that the first preceding vehicle has changed lanes to another lane that is different from the first lane and that the second preceding vehicle continues to travel in the first lane, and
cause the vehicle to travel behind the first preceding vehicle and to change lanes to the other lane upon determining that the first preceding vehicle and the second preceding vehicle changed lanes to the other lane.
| 14. A drive control device comprising:
a control unit configured to control an operation of a host vehicle using at least two autonomous driving modes including a first mode and a second mode that has a driving assistance level that is higher than that of the first mode; and
a preceding vehicle detection unit configured to detect a preceding vehicle traveling in front of the host vehicle,
the control unit being configured to shift the autonomous driving mode from the first mode to the second mode when the operation of the host vehicle is controlled using the first mode and the preceding vehicle detection unit detects the preceding vehicle, wherein
the control unit is configured to use a detectable distance to the preceding vehicle for shifting to the second mode that is greater than a followable distance to the preceding vehicle when following travel is permitted in the first mode. | The method involves controlling the driving of the own vehicle by automatic driving mode in which driving assistance levels differ using an operation-control apparatus. The automatic driving mode contains a first mode and a second mode in which a driving assistance level is higher than first mode, when the operation-control apparatus is controlling the driving of the own vehicle using first mode. The automatic driving mode is changed to second mode from first mode, when the preceding vehicle which drives the front of the own vehicle is detected. An INDEPENDENT CLAIM is included for an operation-control apparatus. Operation-control method of vehicle. The apparatus is effective in the ability to make many environments where the apparatus is made to drive the own vehicle in the automatic driving mode in which a driving assistance level is relatively high. The operation-control apparatus can pull down automatic driving mode in a first mode from a second mode, when the reliability of the second preceding vehicle is less than regulation value. The drawing shows a flowchart illustrating the operation-control process. (Drawing includes non-English language text) S1Step for determining whether automatic driving mode of own vehicle is first modeS2Step for determining whether vehicle speed of own vehicle is more than prescribed speedS3Step for determining whether preceding vehicle in which preceding vehicle detection unit drives front of own vehicle is detectedS4Step for calculating reliability of preceding vehicleS5Step for determining regulation value by which reliability of preceding vehicle is defined previously | Please summarize the input |