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Please summarize the input | FULL-DUPLEX COMMUNICATION METHODS AND APPARATUSTechniques are described for a full duplex communication method and apparatus for inter-vehicle communication (V2V). A communication apparatus includes one or more transmit antennas, one or more receive antennas, and a processor. For cases where a single transmit antenna and multiple receive antennas are used, a distance between the transmit and receive antennas is greater than a pre-determined value. Further, the transmit antenna is located on or in a central region of a top surface of the vehicle and the receive antennas are evenly distributed located on the vehicle. The processor configured to generate one or more messages to be transmitted via the transmit antenna, where the one or more messages includes vehicle condition information, operational information about a driver of the vehicle, or information associated with one or more sensors of the vehicle. | The communication apparatus has transmit antenna (104) and receive antenna (106a-106d) that are located on or in first side and second side of the vehicle (102). A processor generates one or more messages to be transmitted via transmit antenna. The messages include vehicle condition information, operational information about the driver of the vehicle or information associated with one or more sensors of the vehicle. An INDEPENDENT CLAIM is included for a wireless communication method. Communication apparatus for full-duplex vehicle-to-vehicle (V2V) or device-to-device (D2D) communication. Assists driver of the vehicle as the vehicle transmits information to or receives information from the vehicles surrounding the driver, and as well as assists vehicle to operate in autonomous driving mode. The drawing is the schematic view of the communication apparatus for full-duplex vehicle-to-vehicle (V2V) or device-to-device (D2D) communication. 102Vehicle104Transmit antenna106a-106dReceive antenna |
Please summarize the input | Method, system and vehicle for controlling over-vehicle on ice and snow road of automatic driving vehicleThe invention claims an automatic driving vehicle ice and snow road overtaking control method, system and vehicle, firstly detecting whether the surrounding vehicle has overtaking intention; detecting the front and back position vehicle on the borrowing lane is a non-automatic driving vehicle or an automatic driving vehicle, and a non-automatic driving vehicle or automatic driving vehicle located on the to-be-executed overtaking vehicle overtaking route position, and the safe distance of the front vehicle position and the rear vehicle position on the borrowing lane, then sending signal to the surrounding vehicle, after executing the first lane change of the overtaking vehicle, judging whether the detecting road is the ice film road, if not, performing the second lane changing to finish the overtaking after the overtaking vehicle speed change driving exceeds the original lane, sending the over-vehicle signal to the surrounding vehicle, the surrounding vehicle recovers the original driving state, the invention controls the automatic driving vehicle and the surrounding automatic driving vehicle, reduces the uncertainty of the non-automatic driving vehicle in the vehicle process, improves the super-vehicle safety performance of the ice and snow road.|1. An automatic driving vehicle control method for snowy and icy road of automobile, wherein it comprises the following steps: firstly comparing the to-be-executed overtaking vehicle and the front vehicle speed, if it is greater than the front vehicle speed, carrying out overtaking, and then detecting whether the surrounding vehicle has overtaking intention; if there is, then to be executed overtaking vehicle deceleration or original state driving; if not, then detecting the front and back position vehicle on the borrowing lane is a non-automatic driving vehicle or an automatic driving vehicle, and a non-automatic driving vehicle or an automatic driving vehicle located on the vehicle overtaking route to be executed on the position, and the safety distance of the front vehicle position and the rear vehicle position on the borrowing lane, It includes the following three cases: The first situation: if the vehicle located at the front vehicle position and the rear vehicle position of the overtaking vehicle borrowing lane is a non-automatic driving vehicle, then the to-be-executed overtaking vehicle sends a first signal for prompting the non-automatic driving vehicle having an overtaking intention, detecting the front vehicle and the rear vehicle speed and does not change or decelerate, executing the first lane changing for the overtaking vehicle; The second situation: if the non-automatic driving vehicle and the automatic driving vehicle are respectively located at the front vehicle position and the rear vehicle position of the overtaking vehicle to be executed, the priority selection sends the second signal to prompt the automatic driving vehicle to change the speed, the overtaking vehicle is to be executed for the first lane change; The third scenario: if the automatic driving vehicle is respectively located at the front vehicle position and the rear vehicle position of the overtaking vehicle to be executed, detecting whether the road surface of the front vehicle and the rear vehicle is the ice film road surface, preferably selecting the second signal to prompt as the automatic driving vehicle changing speed of the non-ice film road surface, performing the first lane changing for the overtaking vehicle, in the three cases, when the first signal or the second signal is selected, the overtaking vehicle sends the third signal to the automatic driving vehicle located at the front of the front vehicle position or the automatic driving vehicle after the rear vehicle position, after the vehicle receives the third signal of the overtaking vehicle to be executed, detecting the current position of the vehicle, if the rear vehicle at the rear vehicle position is slowly decelerated, and sending the variable speed driving warning to the front and rear vehicles, if the vehicle in the front of the front vehicle position is slowly accelerated, and sending the variable speed driving early warning to the surrounding vehicle ; after executing the first lane change of the overtaking vehicle, judging whether the detecting road is ice film road, if so, performing the original state driving of the overtaking vehicle, if not, detecting whether the surrounding vehicle state is changed, if there is no change, performing the second lane changing to finish the overtaking after the overtaking vehicle speed changing running exceeds the original lane front vehicle, sending the over-vehicle signal to the surrounding vehicle, the surrounding vehicle recovers the original driving state.
| 2. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the judging process of the icy road surface is as follows: the camera of the automatic driving car is matched with the sensor to judge; the distance of the surrounding vehicle passes through the laser radar, the matching of the millimeter-wave radar and the camera can realize measurement; ground adhesion coefficient through the road passing state, the camera sensing and sensor measuring cooperation for judging.
| 3. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the safe distance of the vehicle and the surrounding vehicle is calculated by the following formula: wherein t1 and t2 are the time of the brake, S is the braking process driving distance, v driving speed, g gravity acceleration, μ road adhesion coefficient, s0 after braking distance from the front vehicle. when the automatic driving vehicle is to perform the overtaking operation, the condition that the vehicle should satisfy the vehicle in the super-vehicle borrowing lane is as follows: the vehicle driving condition with the super-vehicle borrowing lane: △S1 ?S; v is not less than v1; and the super-vehicle driving condition of the front vehicle borrowing lane: DELTA S2 IS NOT LESS THAN S; v is less than or equal to v2; wherein ΔS1 is the horizontal distance with the borrowing lane rear vehicle; v1 is the speed of borrowing lane back vehicle; wherein delta S2 is the horizontal distance with the borrowing lane front vehicle; v2 is the speed of borrowing lane front vehicle.
| 4. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein said borrowing traffic lane can be the adjacent left side or the adjacent right side traffic lane.
| 5. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein said first signal provides lane changing information to the surrounding vehicle in the same way as the non-automatic driving vehicle lane changing process.
| 6. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the second signal is the interactive vehicle information between the overtaking vehicle to be executed and the automatic driving vehicle at the upper front vehicle position or the rear vehicle position of the borrowing lane, the position and the speed of the vehicle. steering speed and steering time and so on.
| 7. The method for controlling overtaking of ice and snow road surface of automatic driving automobile according to claim 1, wherein the third signal is the interactive vehicle information between the to-be-executed overtaking vehicle and the automatic driving vehicle located in front of the front vehicle or the automatic driving vehicle after the rear vehicle position, comprising a position, to be executed overtaking vehicle speed, front vehicle position speed or back vehicle position speed and steering time and so on.
| 8. An automatic driving vehicle ice and snow road overtaking control system, comprising a vehicle controller, a V2V communication unit and a combined instrument; the vehicle controller is adapted to collect the position information of the vehicle, vehicle speed information and steering information; the V2V communication unit is adapted to transmit position information, vehicle speed information and steering information; the vehicle controller is adapted to according to the position information of each vehicle, vehicle speed information and steering information to generate super-vehicle intention signal; the combined instrument is suitable for displaying the corresponding overtaking information according to the intention of the overtaking.
| 9. An automatic driving vehicle, comprising the automatic driving vehicle ice and snow road overtaking control system. | The self-driving car overtaking control method involves comparing the speed of a vehicle to be overtaken with the vehicle in front, overtaking if it is greater than the speed of the vehicle in front, and detecting whether the surrounding vehicles have overtaking intentions. The overtaking vehicle is to be decelerated or driven in its original state. Determination is made to detect whether the front and rear position vehicles on the borrowed lane are non-autonomous driving vehicles or automatic driving vehicles. The first signal is sent to remind the non-autonomous vehicle that the vehicle has an overtaking intention if the vehicles in the front and rear positions of the borrowed lane of the vehicle to be overtaken are non-autonomous vehicles. The second signal is preferred to prompt the automatic driving vehicle to change speed if the non-autonomous and automatic driving vehicles are respectively located in the front and rear positions of the borrowed lane of the overtaking vehicle. INDEPENDENT CLAIMS are included for:(1) an overtaking control system for an automatic driving vehicle on icy and snowy roads;(2) an automatic driving vehicle. Self-driving car overtaking control method on icy and snowy roads. The method reduces the uncertainty of the non-automatic driving vehicle in the process of driving through the cooperative control of the automatic driving vehicle and the surrounding automatic driving vehicles, and improves the safety performance of overtaking on ice and snow roads. The drawing shows a flow chart of a self-driving car overtaking control method on icy and snowy roads. (Drawing includes non-English language text). |
Please summarize the input | A non-human bus wire control chassis and automatic driving system thereofThe invention claims a non-human bus wire control chassis and automatic driving system thereof, a unmanned bus wire control chassis, comprising a chassis main body. In the application, the image and the 3 D laser front fusion sensing, for pedestrian and lane line environment detection to ensure the correct understanding and corresponding decision of the vehicle body surrounding environment of the automatic driving vehicle. identifying the road red street lamp by V2X intelligent network connection technology based on the 5 G communication, the technology by installing the signal emitter on the traffic light to continuously transmit the state information of the traffic light, automatically driving the vehicle by receiving the signal sent by the signal emitter to judge the state of the traffic light, using the MPC track tracking can make the vehicle travel along the predetermined track, the algorithm has excellent performance, the track of the track exhibits stable and accurate tracking capability, at the same time, it has enough real-time performance.|1. A non-human bus wire control chassis, comprising a chassis main body, wherein the chassis main body front side is top part mounted with 32-line laser radar, providing a horizontal 360 degrees, vertical 40 degrees of view, four corners of the chassis main body are fixedly mounted with 16-line laser radar, for making up the view blind area caused by 32-line laser radar height, finishing the monitoring area covering 360 degrees, the back side of the chassis main body, the outer side of the two groups of 16-line laser radar is fixedly installed with two groups of blind area auxiliary millimeter wave radar, the front side of the chassis main body is fixedly installed with a preposed millimeter wave radar, three surfaces of the chassis main body outer wall adjacent are distributed with 12 groups of ultrasonic radar, and the ultrasonic radar on the same surface are distributed in equal distance, the front side of the chassis main body is fixedly mounted with an industrial camera 1 and an industrial camera 2, the industrial camera is used for identifying the lane line and the traffic identification, the bottom of the chassis main body is fixedly mounted with an automatic driving calculation platform ADU, comprising an automatic driving operation platform controller, the bottom of the chassis main body is fixedly installed with a 5 G host and a 5 G antenna, and four sides of the chassis main body are respectively fixedly installed with four groups of 5 G cameras, one group of outer side of two groups of the 5 G antennas is provided with a combined navigation host fixedly connected with the chassis main body; 16-line laser radar, 32-line laser radar, preposed millimeter wave radar blind area auxiliary millimeter wave radar and ultrasonic radar can provide millions of data points per second, so as to create a three-dimensional map of surrounding object and environment, combining the auxiliary of combined navigation host and industrial camera, constructing high precision map needed by bus operation.
| 2. The unmanned bus automatic driving system according to claim 1, wherein the unmanned bus control chassis according to claim 1, wherein it comprises a module software interface and hardware interface, further comprising: sensing algorithm module, which is detected by laser radar, camera detection, laser radar tracking, camera tracking and predicting five sub-modules. The camera detection mainly uses the laser radar obtain the high quality barrier information and detects the final result. the tracking result of each sensor will be fused by the filter algorithm, and the prediction module through the fusion tracking result, inputting and outputting the future track of each type of road participant; a locating module, the locating algorithm is mainly composed of a laser radar milemeter, combined navigation calculation and fusion three sub-modules. after outputting the relative positioning information of the fixed frequency, the fusion module combines the positioning information of different frequencies by filtering algorithm, finally outputting the global position of the fixed frequency, providing absolute positioning capability; a global path planning module, responding to the external routing request, giving an optimal route from the current position to the end point of the request; planning control module, the sensor receives the external information, and through the locating and sensing algorithm module, obtaining the state of the vehicle itself and the surrounding vehicle, and receiving the state of the external information and the vehicle, responsible for autonomous vehicle movement planning and trajectory tracking control; a decision planning module, receiving the real-time location, prediction information, planning according to the global path, combining obstacle avoidance, multiple factors, real time planning the vehicle future a collision-free track of a period of time, and sending to the track tracking controller to execute the vehicle; action prediction module, receiving the input of sensing and positioning module, responsible for giving the action of surrounding other participants 5s-7s the specific motion track, for decision planning module, track tracking control module, after the decision planning module gives the track of safety without collision, the track tracking control module is responsible for calculating the proper control command according to the current vehicle state and the planned track, so that the vehicle can move along the planned track.
| 3. The unmanned bus automatic driving system according to claim 2, wherein the output frequency of the positioning module is the fixed frequency and is processed in the ADU of the automatic driving calculation platform.
| 4. The unmanned bus automatic driving system according to claim 2, wherein the hardware interface comprises: a communication data interface: for interactive scheduling command, vehicle positioning, posture; sensor data interface: the combined inertial navigation system IMU and the automatic driving calculation platform ADU, using the USART interface of the IMU to transmit data; multi-line laser radar interface, millions of point cloud data per second, using UDP protocol for data transmission; the ultrasonic radar is a near-distance obstacle detection, the output result is a barrier distance, the data reading is performed by the CAN interface on the ultrasonic radar control box; a control data interface, an automatic driving operation platform ADU and vehicle control chassis interface, using the mode of CAN to transmit.
| 5. The unmanned bus automatic driving system according to claim 2, wherein said module software interface comprises: sensor abstract layer service interface, providing two types of service interface, one is the information service interface of the intelligent sensor, and the other one is other vehicle sensor interface.
| 6. The unmanned bus automatic driving system according to claim 2, wherein the laser radar mileage meter in the positioning module uses the GNSS data to finish the initialization, and the point cloud data generated by the laser radar is matched with the high precision map, and the absolute positioning information of the fixed frequency is output. combined navigation calculating module combined with GNSS data and IMU data, then outputting the relative positioning information of the fixed frequency.
| 7. The non-human bus wire control chassis and automatic driving system thereof according to claim 2, wherein the radar detection and camera detection in the sensing algorithm module can be decoupled and used for tracking. | The chassis has a chassis main body whose front side is fixedly mounted with first and second industrial cameras. A bottom of the chassis main body is fixedly mounted with an Automatic Drive Unit (ADU). A bottom of the chassis main body is fixedly installed with a fifth generation (5G ) host and a 5G antenna. Four sides of the chassis main body are fixedly installed with four groups of 5G cameras. Groups of the 5G antennas is provided with a combined navigation host fixedly connected with the chassis main body. An INDEPENDENT CLAIM is included for an automatic driving system. Non-human bus wire control chassis for an automatic driving system (claimed). The chassis enables Model predictive control (MPC) tracking to make the vehicle travel along the predetermined track, thus obtaining excellent performance, and stable and accurate tracking capability. The drawing shows a schematic view of a non-human bus wire control chassis. (Drawing includes non-English language text). |
Please summarize the input | lane recognition system, method and automatic driving automobileThe invention embodiment claims a lane recognition system, method and automatic driving vehicle, the lane recognition system, comprising: at least one camera, at least one radar, data processing unit and a lane recognition unit; the weight ratio of the road edge feature the lane recognition unit is connected with the data processing unit for determining the road edge feature obtained by processing the image information and obtained by processing the radar information, and according to the weight proportion, the road edge feature obtained by processing the image information; road edge feature obtained by processing the radar information and the lane mark feature to identify the lane position. the technical solution of the invention realizes the camera and radar device data on the lane recognition fusion, so as to better deal with complex road condition and environmental interference, avoid the occurrence of dangerous accident and improve the safety of driving.|1. A lane recognition system, wherein, comprising: at least one camera for obtaining vehicle driving image information of lane in the path, at least one radar, for obtaining vehicle running radar information of the area, and a processing unit for: processing the image information to obtain the lane line feature and the road edge feature, processing the radar information to acquire the road edge feature, determining the weight ratio of road edge feature by the road edge feature processing obtained by the image information and information obtained by processing the radar; according to said weight proportion, road edge feature obtained by processing the image information, by processing road edge feature obtained by the radar information and the lane mark feature to identify the lane position.
| 2. The system according to claim 1, wherein, further comprising: at least one illumination device, used for obtaining the vehicle driving the illumination intensity information of the area, wherein the processing unit is used for determining the weight proportion according to the illumination intensity information.
| 3. The system according to claim 2, wherein it further comprises a vehicle-to-vehicle communication device, which is used for obtaining the vehicle driving road traffic information and auxiliary lane information of the area, wherein: the processing unit is used for according to the road traffic information and the illumination information to determine the road edge feature by processing the weight rate of said image information, obtained by processing the weight ratio of road edge feature obtained by the radar information, the auxiliary lane information of the weight proportion; road edge feature and according to the weight proportion, obtained by processing the image information by processing the road edge feature obtained by the radar information, the lane line feature, the auxiliary lane information to identify lane position.
| 4. A lane recognition method, wherein the method comprises: according to the image information, obtaining the vehicle running lane in the lane route and lane road edge feature of the two side, wherein the image information collected by the camera by the installed on the vehicle, determining the weight ratio of the respectively obtained according to image information and radar information two sides of the lane of the road edge feature, wherein the radar information by mounting the radar acquisition of the vehicle according to the lane line feature. the weight proportion is, the road edge feature obtained by the image information and the road edge feature identifying lane position acquired by the radar information.
| 5. The method according to claim 4, wherein it further comprises the following steps: obtaining the vehicle driving the illumination intensity information of the area according to the illumination intensity information to determine the road edge feature by processing the image information to that obtained by processing the weight ratio of road edge feature obtained by the radar information.
| 6. The method according to claim 5, wherein it further comprises the following steps: obtaining the vehicle running road traffic information and auxiliary lane information of the region; The road traffic information and the illumination information to determine the road edge feature by processing weight ratio of the image information obtained by the weight ratio of the road edge feature obtained by processing the radar information, the auxiliary lane information of weight ratio, and the road edge feature according to the weight proportion, obtained by processing the image information, road edge feature obtained by processing the radar information, the lane line feature, the auxiliary lane information to identify lane position.
| 7. The method according to claim 4, wherein said according to the image information, obtaining the vehicle running lane and a lane in the lane path at two sides of road edge feature, is implemented as: for enhancing white balance processing to the image information. the said image information into area according to the RGB value of the pixel point, gray processing the image information of the divided area, extracting the road feature, the road characteristic input deep learning model trained in advance, output lane and road edge feature.
| 8. The method according to claim 4, wherein the radar information obtaining the lane road edge feature at two sides, is implemented as: performing filtering processing to the radar information, extracting the road edge feature.
| 9. The method according to claim 4, wherein said lane characteristic according to the weight proportion, the road edge feature obtained by the image information and the road edge feature identification vehicle acquired by the radar information of position, comprising: calculating the lane width according to the lane mark feature according to the weight proportion, the road edge feature obtained by the image information and the road edge feature acquired by the radar information of the calculated road width; according to the lane width and the width of the road lane number calculation, and based on the lane width and the lane number identifying the lane position.
| 10. An automatic driving automobile, comprising one of a lane recognition system according to any one of claims 1~3. | The system has a camera for obtaining vehicle driving image information of lane in a path. A radar obtains radar information of an area in which a vehicle is traveling. A processing unit processes image information to obtain lane line features and road edge features. The processing unit processes radar information to obtain road edge features. The processing unit determines a road edge feature obtained by processing image information and a weight ratio of a road edge feature obtained by processing radar information. The processing unit identifies a lane position by processing the road edge feature obtained by radar information and lane line feature. An INDEPENDENT CLAIM is also included for a lane recognition method. Lane recognition system. The system realizes data fusion of the camera and the radar device in lane recognition, so as to better deal with complex road conditions and environmental disturbances, avoids the occurrence of dangerous accidents, and improves the safety of driving. The drawing shows a block diagram of a lane recognition system. '(Drawing includes non-English language text)' |
Please summarize the input | A formation control system of automatic driving vehicleThe application model claims a formation control system of automatic driving vehicle, the formation control system of the automatic driving vehicle comprises a vehicle end and a road end, the vehicle end comprises a vehicle unit and a domain controller, the road end comprises a road side unit, a vehicle unit, for obtaining the sensor data of the vehicle end and the vehicle road cooperative data obtained by the V2X communication mode, and sending the sensor data of the vehicle end and the road end of the vehicle road cooperative data to the domain controller; domain controller, used for performing fusion processing for the received data, and performing formation control of automatic driving vehicle according to the fusion processing result; a road side unit, for obtaining the road cooperation data of the road end and sending to the vehicle end through the V2X communication mode. The application realizes the full link design of automatic driving vehicle formation control based on V2X communication, by combining the sensor data of the vehicle end with the road of the road end, providing more abundant, more reliable judging basis for the formation decision planning, improving the formation control precision.|1. A formation control system of automatic driving vehicle, wherein the formation control system of the automatic driving vehicle comprises a vehicle end and a road end, the vehicle end comprises a vehicle-mounted unit and a domain controller, the road end comprises a road side unit, the vehicle-mounted unit, for obtaining the sensor data of the vehicle end and the vehicle road cooperative data of the road end by the V2X communication mode, and sending the sensor data of the vehicle end and the vehicle road cooperative data of the road end to the domain controller; the domain controller is used for performing fusion processing to the sensor data of the vehicle end and the road end of the road end, and according to the fusion processing result for automatically driving the formation control of the vehicle; the road side unit is used for obtaining the road coordinate data of the road end and sending it to the vehicle end through the V2X communication mode.
| 2. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: performing target prediction according to the fusion processing result; and performing formation control of the automatic driving vehicle according to the target prediction result.
| 3. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: fusing the data of each sensor of the vehicle end, obtaining the sensor data after fusion; integrating the converged sensor data with the road-end vehicle-road cooperative data to obtain the final fusion processing result.
| 4. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: determining whether the bicycle can be used as a pilot vehicle according to the fusion processing result; under the condition that the bicycle can be used as a pilot vehicle, based on self-vehicle for formation decision planning, generating formation decision planning task.
| 5. The formation control system of automatic driving vehicle according to claim 4, wherein the domain controller is further used for: executing the formation decision planning task, and obtaining the self-vehicle fleet according to the execution result of the formation decision planning task; controlling the self-vehicle fleet according to the preset fleet driving strategy for driving.
| 6. The formation control system of automatic driving vehicle according to claim 4, wherein the vehicle unit is further used for: sending the formation request to the surrounding vehicle corresponding to the bicycle through the V2X communication mode, so that the surrounding vehicle according to the formation request application added to the self-vehicle fleet; according to the response result of the surrounding vehicle to the formation request, updating the to-be-processed state list of the vehicle end, the to-be-processed state list comprises adding the vehicle queue list, member list and leaving the vehicle queue list.
| 7. The formation control system of automatic driving vehicle according to claim 6, wherein the vehicle unit is further used for: according to the response result of the surrounding vehicle to the formation request, determining the candidate surrounding vehicle; obtaining the vehicle information of the candidate surrounding vehicle by V2X communication mode, and sending the vehicle information of the candidate surrounding vehicle to the domain controller.
| 8. The formation control system of automatic driving vehicle according to claim 7, wherein the domain controller is further used for: according to the vehicle information of the candidate surrounding vehicle, determining whether the candidate surrounding vehicle satisfy into the requirement of the fleet, under the condition that the candidate surrounding vehicle satisfy added with the requirement of the fleet, the candidate surrounding vehicle is used as the following vehicle to join the self-vehicle fleet.
| 9. The formation control system of automatic driving vehicle according to claim 4, wherein the formation decision planning task comprises a vehicle fleet driving track, the domain controller is further used for: determining a current lane where the bicycle is located; according to the current lane of the bicycle and the driving track of the train, determining whether the bicycle needs to change the lane; under the condition that the bicycle needs to be changed, generating and executing the lane-changing track planning task, so that the bicycle is changed from the current lane to the target lane.
| 10. The formation control system of the automatic driving vehicle according to any one of claims 1 to 9, wherein the vehicle road cooperation data is data obtained by sensing the surrounding environment in the preset range of the road side device, the vehicle road cooperation data comprises other traffic participation object data, traffic signal lamp data and road event data in the one kind of or more. | The system has a vehicle end provided with a vehicle-mounted unit and a domain controller. A road end is provided with a road side unit. The vehicle-mounted unit is used for obtaining the sensor data of the vehicle end and the vehicle road cooperative data of the road end by the V2X communication mode and sending the sensor data of the vehicle end and the vehicle road cooperative data of the road end to the domain controller. The domain controller is used for performing the fusion processing to the sensor data of the vehicle end and the road end. The road side unit is used for obtaining the road coordinate data of the road end and sending to the vehicle end through the V2X communication mode. The domain controller is used for performing the target prediction according to the fusion processing result and performing formation control of the automatic driving vehicle according to the target prediction result. Formation control system for an automatic driving vehicle e.g. automatic driving bus and automatic driving lorry. The application realizes the full link design of automatic driving vehicle formation control based on V2X communication, by combining the sensor data of the vehicle end with the road of the road end, providing more abundant, more reliable judging basis for the formation decision planning, improving the formation control precision. The drawing shows a structure schematic diagram of a formation control system for an automatic driving vehicle e.g. automatic driving bus and automatic driving lorry. (Drawing includes non-English language text). |
Please summarize the input | SYSTEM AND METHOD FOR LONGITUDINAL REACTION-CONTROL OF ELECTRIC AUTONOMOUS VEHICLEA PID automatic control system and method for speed control of an electric vehicle, according to an embodiment, design a commercial vehicle-based autonomous driving system and a controller for development of a semi-autonomous driving acceleration/deceleration controller, develop a driving priority determination algorithm on the basis of V2X communication, develop technologies for correcting autonomous navigation and improving position precision, develop a technology for recognizing autonomous driving road environments by using a cognitive sensor, conceive an application method of a driving habits improvement algorithm by using learning, develop a driving habits improvement algorithm by using learning, and develop an AEB function for a commercial vehicle to which a semi-autonomous driving technology is applied.|1. A PID automatic control system for semi-autonomous driving acceleration/deceleration control, comprising: a communication module for communicating with a nearby vehicle and a leading vehicle when a platooning group is formed around the vehicle;
a detection module for detecting obstacles on the front and rear sides of the vehicle, detecting surrounding vehicle information including the vehicle speed and driving path of the front vehicle, and detecting road information including stop lines, traffic lights, signs, and road curbs;
The vehicle speed is controlled according to the result of V2X communication with communication objects around the vehicle and information about surrounding vehicles and road information, and the amount of change in vehicle speed due to the change in pedal tilt is calculated and reflected in the proportional gain value and error calculation, and acceleration control response characteristics and deceleration control a semi-autonomous driving acceleration/deceleration control module for learning response characteristics and applying the result of learning response characteristics for each vehicle to a gain value calculation;
a deceleration/acceleration sensor that detects the inclination of the pedal and the amount of change in the inclination of the vehicle brake and accelerator; and driver-specific driving data and vehicle control data are collected and driver-specific learning data is applied to the vehicle, and driving habits are identified based on the learning data stored for each driver. a driving habit improvement learning module that improves driving habits through and the semi-autonomous driving acceleration/deceleration control module; is a gain calculator for calculating the difference between the target speed and the running speed, the amount of change in the running speed, and proportional gain (Kp), integral gain (Ki), and differential gain (Kd), which are proportional gains for PID calculation;
an error amount calculator for calculating an error with a target speed after controlling the motor according to the calculated gain value;
a feedback unit for feeding back motor control by applying the calculated error to each gain value; And The detection module implements an autonomous driving navigation position correction algorithm using sensor information to correct the current position of the vehicle from a sensor including a LiDAR and a camera by comparing it with global coordinates, , Through camera coordinate system calibration using camera coordinate system calibration, 1:1 pixel coordinates of external parameters and internal parameters are matched, and the driving habit improvement learning module; the amount of deceleration in the silver curve is small, or the habit of rapidly accelerating when waiting for a signal If monitored, it feeds back to the semi-autonomous driving acceleration/deceleration control module to decelerate further than the amount of deceleration caused by the brake by the driver. PID automatic control system for semi-autonomous driving acceleration/deceleration control, characterized in that it decelerates.
| 2. A PID automatic control method for semi-autonomous driving acceleration/deceleration control, comprising the steps of: (A) an autonomous driving vehicle communicating with a nearby vehicle and a leading vehicle when a platooning group is formed around the vehicle;
(B) detecting obstacles on the front and rear sides of the vehicle in the autonomous vehicle and detecting surrounding vehicle information including the vehicle speed and driving path of the vehicle in front, and road information including stop lines, traffic lights, signs, and curbs; and (C) controlling the vehicle speed according to the result of V2X communication with the communication object around the vehicle in the autonomous vehicle, and information about the surrounding vehicle and the road; and (D) the driver's driving data and vehicle control data in the autonomous vehicle. It collects and applies learning data for each driver to the vehicle, identifies driving habits based on the learning data stored for each driver, and implements autonomous driving to improve driving habits through semi-autonomous driving when high-risk driving habits are identified. Including; and the step of (B); Detecting the inclination of the pedal and the amount of change in the inclination of the vehicle brake and accelerator in the deceleration and acceleration sensor; and calculating a change in vehicle speed due to a change in pedal inclination in the autonomous vehicle and reflecting it in calculating a proportional gain value and an error; comprising the step of (B); implements an autonomous driving navigation position correction algorithm using sensor information to compare and correct the current position of the vehicle from sensors including LiDAR and Camera with global coordinates, and calibrate the camera coordinate system using The pixel coordinates of the external parameter and the internal parameter are matched 1:1 through the camera coordinate system calibration, and the step of (C); is the difference between the target speed and the running speed, the amount of change in the running speed, and the proportional gain for PID calculation calculating a gain (Kp), an integral gain (Ki), and a differential gain (Kd);
calculating an error with a target speed after controlling the motor according to the calculated gain value;
feeding back the motor control by applying the calculated error to each gain value;
learning acceleration control response characteristics and deceleration control response characteristics; And Applying the response characteristic learning result for each vehicle to the gain value calculation; Including; Step of (D); When the amount of deceleration in the curve is small or the habit of sudden acceleration when waiting for a signal is monitored, the driver's brake PID automatic control for semi-autonomous driving acceleration/deceleration control, which feeds back to make the vehicle decelerate further than the amount of deceleration by method. | The system has a communication module (110) that communicates with a nearby vehicle and a leading vehicle when platooning group is formed around the vehicle. A detection module (130) detects obstacles on the front and rear sides of the vehicle. The vehicle speed is controlled according to the V2X communication result with the communication object around the vehicle. A semi-autonomous driving acceleration and deceleration control module (150) learns acceleration control response characteristics and deceleration control response characteristics. A driving habit improvement learning module (170) improves driving habits through semi-autonomous driving. A feedback unit feeds back motor control by applying the calculated error to each gain value. The detection module implements autonomous navigation position correction algorithm using sensor information. An INDEPENDENT CLAIM is included for a proportional integral derivative (PID) automatic control method for semi-autonomous driving acceleration and deceleration control. PID automatic control system for semi-autonomous driving acceleration and deceleration control of 1-ton electric commercial vehicle. The fuel consumption caused by air resistance is reduced, thus improving fuel economy. The semi-autonomous speed control is more accurately performed according to the individual driving characteristics of the vehicle. The driver habit improvement module predicts collision by recognizing obstacle in front of the vehicle being driven by interlocking with the automatic emergency braking system and automatically applies the brake when the driver does not intervene to prevent collision. The drawing shows a block diagram of PID automatic control system for semi-autonomous driving acceleration and deceleration control. (Drawing includes non-English language text) 110Communication module130Detection module150Semi-autonomous driving acceleration and deceleration control module170Driving habit improvement learning module |
Please summarize the input | Interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusionThe invention claims an interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion, which mainly solves the problem that the existing automatic driving decision has low applicability under complex road structure and traffic light information condition. The method considers the multi-lane traffic environment under the world coordinate system, wherein there is the mixed traffic flow composed of the interconnected automatic driving vehicle and the human driving vehicle. Each CAV can obtain surrounding multimodal environmental features (such as lane information, HDV vehicle information, and traffic light information) through a vehicle-mounted sensor and an off-line high precision map. With the help of the vehicle-to-vehicle communication, the CAV can share its information and make a decision within a specified time step t. The aim of the method is to generate speed decision and steering angle decision for CAV. With such action decision, the automatic driving vehicle can safely and effectively drive according to the special route, at the same time, the comfort level of the passenger is greatly improved and the influence to the surrounding HDV is reduced.|1. An interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion, wherein it comprises the following steps: S1, collecting the vehicle dynamics information fA of the CAV through the vehicle GNSS and IMU sensor, and detecting the vehicle dynamics information fH of the HDV through the vehicle radar of the CAV; wherein CAV is automatic interconnected driving vehicle, HDV is human driving vehicle; S2, accurately locating the position and direction of the CAV, and identifying the road and traffic light near the CAV, so as to obtain the real-time road information of the CAV preset driving route; S3, each CAV respectively transmits its own vehicle dynamics information fA and the sensed vehicle dynamics information fH of HDV to an MLP, and splices the obtained result codes to form a vehicle code hi; and aggregating the CAV vehicle code based on the CAV communication link matrix M using the graph attention layer to obtain the vehicle flow information of the CAV preset travelling route; wherein MLP represents a multilayer perceptron; S4, adopting the multi-intelligent body strengthening learning algorithm MAPAC training parameterized CAV action structure to obtain the optimal action strategy, and adopting the random Gaussian strategy to improve the searching ability of the algorithm, so as to realize the action decision of CAV; S5, setting the self-central reward function for improving the safety, efficiency and comfort of the CAV and the social influence reward function for reducing the negative influence to the surrounding HDV to optimize the action decision of the CAV.
| 2. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 1, wherein in the step S1, the vehicle dynamics information fA of CAV comprises vehicle speed, direction, length, width, a lane ID and an offset from the lane centre line; The vehicle dynamics information fH of the HDV includes the relative distance, speed, direction of the HDV relative to the CAV and the lane mark and lane centre line offset of the HDV.
| 3. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 2, wherein in the step S2, the CAV preset driving route is composed of multiple lanes of roads. the characteristic fL of the road is represented by the lane track point, wherein each track point comprises the lane horizontal height, namely the lane gradient change, the direction and the lane ID; the traffic light information uses the detection technology based on the camera to detect the real-time state and the distance from the vehicle; wherein red is [1, 0, 0], green is [0, 1, 0], yellow is [0, 0, 1]; if the characteristic fL of the road and the traffic light state fTL are coded by a road encoder, Er, i= sigma (phi (fL, L, fTL, L) | phi (fL, C, fTL, C) | phi (fL, R, fTL, R)), wherein Er, i represents road coding, phi represents MLP layer, sigma is ReLU activation function, fL, L, fTL, L represents the left side lane code and traffic light code of the lane where the vehicle is located, fL, C, fTL, C represents the lane code and traffic light code of the lane where the vehicle is located, fL, R, fTL, R represents the right lane code and the traffic light code of the lane where the vehicle is located, and the absolute value is the connection operation; for each intelligent agent, the attention point is only limited to the lane characteristic of the current lane, the red-green lamp and two adjacent lanes; finally connecting the traffic flow code Et, i and the road code Er, i to obtain the state code Es, i for the final road information code operated by the subsequent module.
| 4. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 3, wherein in the step S3, in the CAV vehicle coding based on CAV communication link matrix M, according to the attention mechanism, Each intelligent agent i in the vehicle communication network calculates the query vector qi, the key vector ki and the value vector vi are listed as follows: qi = Wqhi, ki = Wkhi, vi=Wvhi, in the formula, Wq represents query matrix, Wv represents value matrix, Wk represents key matrix, hi is vehicle code; Assuming that the intelligent agent i has Ni adjacent intelligent agents, the attention score a ij of the intelligent agent to the adjacent intelligent agent j can be calculated as: wherein sigma is the activation function ReLU; LeakyReLU represents a LeakyReLU activation function, exp represents an exponential operation operation, and l represents one of Ni adjacent intelligent bodies; Due to the change of the traffic environment, the intelligent body which lost the communication connection with it in the current time step is filtered, and the final traffic flow code Et is calculated in combination with the CAV link matrix, and i is listed as follows: wherein phi represents the MLP layer, Mi, j are the values of the link matrix, Mi, j = 0 represents that there is no connection between the agent i and the agent j in the current time step, and vice versa; Wherein, the intelligent agent is CAV.
| 5. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 4, wherein in the step S4, the multi-intelligent body strengthening learning algorithm MAPAC uses the actor commentator structure in strengthening learning, wherein the actor network is used for calculating the action, the commentator network is used for evaluating the action through the estimation value function; the random Gaussian network replaces the original depth Q network, the random Gaussian network outputs a Gaussian distribution, the intelligent agent samples from the distribution to form parameterized continuous action; Wherein, in the model training process using the multi-agent enhanced learning algorithm MAPAC, the actor network pi i of the agent i updates the network by minimizing the following objects: wherein the experience buffer D is used for storing the state and action of all interconnected intelligent bodies, and Q (beta) represents the network parameter of the commentator; lambda is the regularization coefficient of the search performance of the control algorithm, and respectively representing the state information and action information of the intelligent agent i in the time step t, an actor network representing the parameters of the agent i; the combined set of the state and action of the interconnected intelligent body is used as the input of the commentator network, the network then outputs the Q value of the action taken by the intelligent body i in the time step t; The critic network is updated by minimizing the following Berman error JQ: wherein gamma is rewarding discount factor, ri is instant rewarding of t time step; Two target commentator networks for stable training process, when executing, each intelligent body operates the copy of the respective actor network and commentator network, namely distributed execution; the intelligent agent i only needs to obtain the observed traffic environment information and performs information enhancement through the shared information from the interconnected intelligent agent, and then calculates the final parameterized action based on the fused information; finally, selecting the action with the maximum Q value as the actually executed action; All CAVs in the Internet follow the process described above to generate their respective action decisions.
| 6. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 5, wherein in the step S5, the self-central reward function and the social influence reward function form a mixed reward function. The expression is as follows: In the formula, rego represents a self-centered reward, and rsoc represents a social impact reward; it is used for quantifying the cooperation degree between the automatic driving vehicle union and the human driving vehicle.
| 7. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 6, wherein the expression of the social influence reward function is as follows: FORMULA. In the formula, rsoc, 1 is used to quantify the incidence of sudden parking of CAV or sudden cut-in of corresponding lane, and the expression is as follows: rsoc = rsoc, 1 + rsoc, 2, wherein rsoc, 1 is used to quantify the incidence of sudden parking of CAV or sudden cut-in of corresponding lane, and the expression is as follows: In the formula, represents the speed of the HDV in the time step t, thrvel is a threshold value of the speed change, for determining whether the CAV causes the quick brake action of the HDV, thracc* delta t is the speed change threshold value between two continuous time steps; The reward is only effective when the HDV deceleration is greater than thrvel; rsoc, 2 is used for quantizing CAV to adjust its speed or position so as to reserve incidence rate of variable track space behaviour for HDV, the expression is as follows: wherein is the adjacent HDV of the agent i in the time step t; when the vehicle of the adjacent lane is in front of the CAV or behind the CAV safe lane change,/> set as 1; In other cases, is set as 0.
| 8. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 7, wherein the expression of the self-central reward function is as follows: FORMULA. rego=rsaf + reff + rcom, in the formula, rsaf is security reward, reff is efficiency reward, rcom is passenger comfort reward; wherein the security reward is the reward sum of CAV unsafe behaviour and traffic rule compliance rate; the vehicle-following safety uses the predicted collision time TTC to ensure the safe vehicle-following distance of the CAV; The TTC calculation formula is as follows: In the formula, fA. level and fH-level represent speeds of CAV and HDV, respectively, dis (A, H) represents Euclidean distance between A and H; On-vehicle security reward rsaf, 1 is calculated as follows: Wherein, is a threshold value of TTC; Secondly, the lane-keeping security reward keeping CAV travels in the center of the lane, and the calculation method is as follows: wherein dis (wp, A) measures the distance between CAV and the central point of the lane, d is half of the width of the lane; the emergency safety is CAV collision, deviation road action or violation traffic signal lamp of penalty, other condition is 0; efficiency reward: the efficiency of the multi-lane task is the sum of the speed control efficiency and the lane changing efficiency; The speed control efficiency reff, 1 promotes the automatic driving vehicle to keep a safe driving speed, calculating as follows: wherein fA. level represents the speed of the automatic driving vehicle, velmax is the maximum driving speed set by the vehicle; lane change reward reff, 2 encourage vehicle overtaking and avoid obstacle; It is calculated as follows after the lane change is completed wherein dis (Htar, A) and dis (Hprev, A) represent the distance of the vehicle from an obstacle or a forward vehicle on the target lane and the previous lane, respectively; Comfortable rewards for passengers: using the vehicle acceleration change rate Jerk to measure; The Jerk calculation method is as follows: wherein acct is the acceleration of the vehicle under the time step t, delta t is each time step length; rcom is calculated by Jerk: wherein thracc is the allowed maximum Jerk value. | The method involves collecting vehicle dynamics information of a vehicle through a vehicle global navigation satellite system (GNSS) and an internet of things (IMU) sensor. A self-central reward function is set for improving safety, efficiency and comfort of a CAV and a social influence reward function for reducing negative influence to a surrounding HDV to optimize an action decision of the CAV. Cooperative sensing and self-adaptive information fusion based interconnected automatic driving decision method for automatic driving vehicles e.g. human driving vehicle (HDV) and inter-connected auto-driving vehicle (CAV). The automatic driving vehicle can safely and effectively drive according to the special route, at the same time, the comfort level of the passenger is greatly improved and the influence to the surrounding HDV is reduced. The drawing shows an overall frame diagram of a cooperative sensing and self-adaptive information fusion based interconnected automatic driving decision method. (Drawing includes non-English language text). |
Please summarize the input | Intelligent cruise robot reverse searching system and method based on cloud wireless transmissionThe invention relates to the technology field of reverse searching in parking lot, specifically to an intelligent cruise robot reverse searching system and method based on cloud wireless transmission. The system comprises four parts of an intelligent cruise robot terminal, a parking space identification terminal, a cloud terminal and a reverse searching inquiry terminal. The system is a full-automatic intelligent robot device with vehicle position identification, license plate automatic identification and mobile walking functions. The searching robot device integrates automatic control, video recognition analysis, autonomous driving, wireless communication and algorithm based on artificial intelligence, realizing fast and accurate recognition of license plate and parking space and issuing the information for searching vehicle position.|1. An intelligent cruise robot reverse searching system based on cloud wireless transmission, wherein the system comprises the following four parts: intelligent cruise robot end of the intelligent cruise robot end comprises one or more intelligent cruise robot; each intelligent cruise robot comprises a high-definition camera and a license plate number recognition software, automatically control the cruise module, universal high-performance wireless communication transmission module, server module and a battery drive module, the high definition camera and license plate number recognition software uses video analysis algorithm for license plate number and the carport associated identification of the vehicle, and the computer visual detection; the automatic control cruise module for controlling the intelligent cruise robot walking and obstacle recognition avoidance function, the module through ultrasonic wave radar plus video visual detection of two kinds of technology, it can detect the size and distance of the obstacle object of all ultrasonic radar in the walking. after calculating controlling the robot to perform the corresponding avoid and special marks (such as white lines) video visual detection may be based on the path of the robot intelligent cruise control according to the route automatic walking; the wireless communication module adopts 4 G internet of things technology responsible for identifying the licence plate number and the associated parking space information to the cloud; the generic high performance server module is used for license plate number recognition software, automatically control the cruise module, wireless communication transmission module provides hardware computing and memory function; the battery drive module for providing intelligent cruise robot driving walking power and to charge the battery under the condition of low battery, parking mark end must be a parking mark end formed by the position identification device for associating the license plate of parking and stopping vehicle, wherein identification device adopts two technical methods for parking space recognition, and the two technical means also determines the working mode and working state of the intelligent cruise robot: A smart sign technology using the intelligent vehicle is mounted below the stall, integrated with an automatic induction device, wireless communication transmission device and the parking state display device; automatic sensing device adopts low power consumption communication technology, when the intelligent car label after sensing the vehicle parking the parking state display device from a vehicle green light into red light of a vehicle and the wireless communication transmission device to send a wireless signal to the intelligent cruise robot; the intelligent cruise robot working mode is driven, robot parked at the appointed place, after the wireless signal or more intelligent receiving the intelligent car label sent by the car label of white lines, road is fast by no change of vehicle area to stall before collecting the vehicle license plate number, B adopts traditional ground printed number technology using traditional printing mode, printing numbers on each parking space, the intelligent cruise robot working mode is active the cruise. Under this working mode, intelligent cruise robot according to the set time interval, driving the all vehicle in the area which is responsible for identifying one identifying vehicle license plate at the same time, the identification number of the ground printed. the two kinds of number associated to the cloud for issuance, cloud the cloud comprising one or more management servers, one or more indoor LED guide screen and one or more outdoor LED guide screen, the server cluster working mode, can support large data storage operation, providing the original learning data for future data mining and artificial intelligence algorithms, the server providing a plurality of external interfaces, which can remotely control one or more indoor LED guide screen, one or more outdoor LED guide screen. communicated through the local area network or the Internet server and the guide screen for multiple parking the parking information, receiving parking and vehicle information transmitted by multiple intelligent cruise robot, providing for indoor and outdoor LED guide screen to issue to realize centralized management and resource sharing of the vehicle information; reverse the searching inquiry user through reverse searching inquiry APP software or Minicell public number and cloud communication end, can query the vehicle position after inputting the inquiry condition.
| 2. A searching method based on the reverse searching system according to claim 1, wherein, when the vehicle identification end is a traditional ground printing vehicle number, the method adopts the intelligent cruise robot via the driving mode to the parking space recognition, specifically comprising the following steps: S1. the parking space of the parking lot A and lot B printed with the traditional ground parking space number associated set walking route on the lane marked white cruise robot, S2. Intelligent cruise robot via preset time (such as every 5 minutes or 10 minutes), driving walking cruise on a predetermined area and route, simultaneous analysis of license plate and parking space number through video analysis and recognition in the walking process, S3. the analysis back to the license and parking space number, the license plate and parking space number information uploaded to the cloud through the wireless communication module of the robot, S4. cloud end after statistic analysis, the transmission guide screen, the vehicle is guided into position to guide indoor LED screen and outdoor LED parking information and vehicle location through the network, S5. APP or Minicell public number owner inquiry end through backward searching when the need to find vehicle to vehicle position inquiry so as to fast and conveniently to the navigation guidance.
| 3. The searching method based on the reverse searching system according to claim 1, wherein when the vehicle identification terminal is intelligent sign, the method uses intelligent cruise robot parking space recognition by passive cruise mode, specifically comprising the following steps: S1. the parking space of the parking lot A and lot B is equipped with an intelligent vehicle, the intelligent vehicle is integrated with automatic induction device, the wireless communication transmission device and the parking state display device, then information associated with the parking space S2. when there is the vehicle parking position, in the intelligent vehicle automatic induction device utilizing photoelectric or electromagnetic induction technology, after sensing the vehicle parks, the intelligent vehicle position state display device of label index light from the non-green light into red light of a vehicle, the intelligent vehicle one or more label by the wireless communication transmission device and respective intelligent cruise robot communication, intelligent cruise robot receives the information, it will rapidly reach with change of the parking area, S3. cloud end after statistic analysis, the transmission guide screen, the vehicle is guided into position to guide indoor LED screen and outdoor LED parking information and vehicle location through the network, S4. APP or Minicell public number owner inquiry end through backward searching when the need to find vehicle to vehicle position inquiry so as to fast and conveniently to the navigation guidance. | The system has an intelligent cruise robot for comprising a high-definition camera, a number plate, an universal high-performance wireless communication transmission module, a server module and a battery drive module. An automatic control cruise module controls intelligent cruise robot walking and obstacle recognition avoidance function. A server is connected with external interfaces to control an indoor LED guide screen. A cloud communication end determines a vehicle position by using reverse searching inquiry application (APP) software or public number after inputting inquiry condition. An INDEPENDENT CLAIM is also included for a cloud wireless transmission based intelligent cruise robot reverse searching method. Cloud wireless transmission based intelligent cruise robot reverse searching system. The system can automatically control video recognition analysis, autonomous driving, wireless communication and algorithm based on artificial intelligence and realize information in the number plate and a parking space after searching the vehicle position. The drawing shows a block diagram of a cloud wireless transmission based intelligent cruise robot reverse searching system. '(Drawing includes non-English language text)' |
Please summarize the input | Method for determining reliability of received dataThe invention relates to a computer-implemented method for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the target vehicle is different from the autonomous vehicle. The invention also relates to a corresponding control system and a computer program product.|1. A computer implemented method for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, wherein the target vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, the autonomous vehicle comprises a control unit, wherein the method comprises the following steps: and using wireless communication at the control unit, receiving a first set of operation data associated with the target vehicle, and determining the reliability level by the control unit based on the first set of operation data and a predetermined model of the expected behaviour of the first set of indicating operation data.
| 2. The method according to claim 1, wherein the predetermined model is further dependent on the target vehicle.
| 3. The method according to any one of claim 1 and 2, wherein the predetermined model further depends on the expected change of the first set of operation data over time.
| 4. The method according to claim 1, further comprising the following steps: using a first sensor included in the autonomous vehicle to determine a second set of operational data associated with the target vehicle by the control unit, and determining, by the control unit, a difference between the first set of operational data and the second set, wherein the control unit determines the second set of operational data associated with the target vehicle, The determination of the level of reliability is also based on the determined difference.
| 5. The method according to claim 4, wherein the predetermined model further indicates an expected behaviour of the second set of operational data.
| 6. The method according to claim 4, wherein the predetermined model further indicates an expected behaviour of the difference between the first set of operational data and the second set.
| 7. The method according to any one of the preceding claim, further comprising the following steps: -only when the reliability level is higher than the first predetermined threshold value, the operation data from the target vehicle is defined as reliable.
| 8. The method according to any one of the preceding claim, further comprising the following steps: -if the reliability level is below a second predetermined threshold value, defining the operation data from the target vehicle as unreliable.
| 9. The method according to claim 1, wherein the first set of operating data relates to at least one of a speed of the target vehicle, an acceleration, a deceleration, and the like.
| 10. The method according to claim 2, further comprising the following steps: -determining an identifier of the target vehicle using a second sensor included in the autonomous vehicle.
| 11. The method according to claim 4, wherein the predetermined model represents a statistical behaviour of the set of operational data.
| 12. The method according to any one of the preceding claim, further comprising the following steps: -establishing a network connection with a server disposed outside the autonomous vehicle, and requesting the predetermined model from a remote server.
| 13. The method according to claim 12, further comprising the following steps: if the reliability level is higher than the third predetermined threshold value, providing the updated model.
| 14. The method according to claim 4, wherein the first sensor is at least one of a radar, a laser radar sensor, or a camera.
| 15. The method according to any one of the preceding claim, wherein the operation data is a vehicle-to-vehicle (V2V) data.
| 16. The invention claims an control system vehicle, comprising a control unit, which is suitable for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the control system control system vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, wherein the control unit is adapted to: receiving a first set of operational data associated with the target vehicle using wireless communication, and determining the reliability level based on the first set of operational data and a predetermined model indicative of an expected behaviour of the first set of operational data.
| 17. The system according to claim 16, wherein the predetermined model is further dependent on the target vehicle.
| 18. The system according to any one of claim 16 and 17, wherein the predetermined model further depends on the expected change of time of the first set of operation data.
| 19. The system according to claim 16, wherein the control unit is further adapted to: -determining a second set of operational data associated with the target vehicle using a first sensor included in the autonomous vehicle, and determining a difference between the first set of operational data and the second set, wherein the determination of the reliability level is further based on the determined difference.
| 20. The system according to claim 19, wherein the predetermined model further indicates an expected behaviour of the second set of operational data.
| 21. The system according to claim 19, wherein the predetermined model further indicates an expected behaviour of the difference between the first set of operational data and the second set.
| 22. The system according to any one of 16 to 22 claim to 22, wherein the control unit is further adapted to: -only when the reliability level is higher than the first predetermined threshold value, the operation data from the target vehicle is defined as reliable.
| 23. The system according to any one of claim 16 to 23, wherein the control unit is further adapted to: -if the reliability level is below a second predetermined threshold value, defining the operation data from the target vehicle as unreliable.
| 24. A vehicle comprising the control system according to any one of claim 16 to 23.
| 25. The vehicle according to claim 24, wherein the vehicle is a truck, a vehicle, a bus or a working machine.
| 26. A computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium is stored with a computer program component for operating the control system included in the autonomous vehicle; said control system is suitable for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the target vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, the control system comprises a control unit, wherein the computer program product comprises: a code for receiving, at the control unit, a first set of operational data associated with the target vehicle using wireless communication, and a code for determining the reliability level by the control unit based on the first set of operating data and a predetermined model of expected behaviour of the first set of operating data. | The method involves receiving a first set of operational data relating to the target vehicle, at the control unit and using wireless communication. The reliability level based on the first set of operational data and a predetermined model indicative of an expected behavior of the first set of operational data is determined by the control unit. The predetermined model is further dependent on the target vehicle. The predetermined model is dependent on an expected variation over time of the first set of operational data. The method comprises determining, by the control unit, a second set of operational data related to the target vehicle using a first sensor comprised with the ego vehicle. INDEPENDENT CLAIMS are included for the following:a control system comprised with an ego vehicle;a computer program product; anda vehicle comprising a control system. Method for use in determining a reliability level of data received by an ego vehicle from a target vehicle being different from the ego vehicle. Greatly improves the determination of the reliability level for the received data. The drawing shows a schematic view of a conceptual control system. 200Control system202Control unit204Radar206Lidar sensor arrangement208Camera |
Please summarize the input | Method and system of determining trajectory for an autonomous vehicleA method of determining a navigation trajectory for an autonomous ground vehicle (AGV) is disclosed. The method may include receiving first Region of Interest (ROI) data associated with an upcoming trajectory path, and receiving predicted attributes associated with a future navigation trajectory for the upcoming trajectory path. The predicted attributes are derived based on map for the upcoming trajectory path. The method may further include modifying the predicted attributes based on environmental attributes extracted from first ROI data to generate modified attributes, and dynamically receiving a second ROI data associated with the upcoming trajectory path upon reaching the upcoming trajectory path. The method may further include predicting dynamic attributes associated with an imminent navigation trajectory for the upcoming trajectory path based on the second ROI data, and refining the modified attributes based on the one or more dynamic attributes to generate a final navigation trajectory.What is claimed is:
| 1. A method of determining a navigation trajectory for an autonomous ground vehicle (AGV), the method comprising:
receiving, by a navigation device, first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures over vehicle-to-infrastructure (V2I) communication network, and
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of the AGV;
deriving, by the navigation device, one or more environmental attributes associated with the predetermined ROI based on the first ROI data;
receiving, by the navigation device, one or more predicted attributes associated with a static map of the predetermined ROI, from a cloud,
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection on map data associated with the predetermined ROI, using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modifying, by the navigation device, the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI, to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI;
receiving predictions from a cloud server;
merging the environmental information and the predictions received from the cloud server; and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server;
receiving, by the navigation device, a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predicting, by the navigation device, one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determining, by the navigation device, an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generating, by the navigating device, one or more refined attributes by correcting the one or more modified attributes based on the error;
updating, by the navigation device, the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
controlling, by the navigation device, the AGV to follow the final navigation trajectory.
| 2. The method of claim 1, wherein, the one or more dynamic attributes associated with the predetermined ROI are predicted, by the second AI prediction model by performing at least one of: the semantic segmentation, the object detection, and the lane detection.
| 3. The method of claim 1, wherein the first ROI data is further captured using a set of vision sensors installed on or other AGVs, and wherein the first ROI data is provided to the navigation device or vehicle-to-vehicle (V2V) communication network.
| 4. The method of claim 1, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle.
| 5. A navigation device for determining a navigation trajectory for an autonomous ground vehicle (AGV), the navigation device comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
receive first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures over vehicle-to-infrastructure (V2I) communication network,
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of the AGV;
derive one or more environmental attributes associated with the predetermined ROI based on the first ROI date;
receive one or more predicted attributes associated with a static map Of the predetermined ROI, from a cloud;
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection based—on map data associated with the predetermined ROI using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modify the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI, to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI,
receiving predictions from a cloud server,
merging the environmental information and the predictions received from the cloud server, and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server,
receive a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predict one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determine an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
determine an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generate one or more refined attributes by correcting the one or more modified attributes based on the error;
update the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
control the AGV to follow the final navigation trajectory.
| 6. The navigation device of claim 5, wherein the one or more dynamic attributes associated with the predetermined ROI are predicted, based-on by the second AI prediction model by performing at least one of: the semantic segmentation, the object detection, and the lane detection.
| 7. The navigation device of claim 5, wherein the first ROI data is captured using a set of vision sensors installed on or other AGVs, and wherein the first ROI data is provided to the navigation device over vehicle-to-vehicle (V2V) communication network.
| 8. The navigation device of claim 5, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle.
| 9. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
receiving first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures,
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of an autonomous ground vehicle (AGV);
deriving one or more environmental attributes associated with the predetermined ROI based on the first ROI data;
receiving one or more predicted attributes associated with a static map of the predetermined ROI, from a cloud,
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection on map data associated with the predetermined ROI using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modifying the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI,
receiving predictions from a cloud server,
merging the environmental information and the predictions received from the cloud server, and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server,
receiving a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predicting one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determining an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generating one or more refined attributes by correcting the one or more modified attributes based on the error;
updating the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
controlling the AGV to follow the final navigation trajectory.
| 10. The non-transitory computer-readable storage medium of 9, wherein one or more dynamic attributes associated with the ROI are predicted by the second AI prediction model by performing based-on at least one of a semantic segmentation, an object detection, and a lane detection.
| 11. The non-transitory computer-readable storage medium of claim 9, wherein the first ROI data is captured using a set of vision sensors installed on other AGVs, and wherein the first ROI data is provided to the navigation device over vehicle-to-vehicle (V2V) communication network.
| 12. The non-transitory computer-readable storage medium of claim 9, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle. | The method involves receiving region of interest (ROI) data associated with an upcoming trajectory path by a navigation device, where the ROI data is captured while approaching the trajectory path from an anticipated future location of an autonomous ground vehicle (AGV). A set of predicted attributes associated with a future navigation trajectory for the trajectory is received by the navigation device. The attributes are modified based on a set of environmental attributes to generate modified attributes. The modified attributes are refined based on the dynamic attributes by the device to generate a final navigation trajectory by using an artificial intelligence (AI) prediction model. INDEPEDENT CLAIMS are included for the followinga navigation device for determining navigation trajectory for AGV; anda non-transitory computer-readable storage medium storing program for determining navigation trajectory for AGV. Method for determining navigation trajectory for AGV i.e. autonomous ground vehicle (AGV), in indoor and outdoor settings to facilitate efficient transportation. The AGV is capable of sensing the dynamic changing environment, and of accurately navigating without any human intervention. The method provides for automatic classification for the objects detected in an environment and for enhancing mapping for the AGVs. The drawing shows a schematic view of the system for determining a navigation trajectory.602Computing system 604Processor 608Input device 610Output device 626RAM |
Please summarize the input | MODIFYING BEHAVIOR OF AUTONOMOUS VEHICLE BASED ON PREDICTED BEHAVIOR OF OTHER VEHICLESA vehicle configured to operate in an autonomous mode could determine a current state of the vehicle and the current state of the environment of the vehicle. The environment of the vehicle includes at least one other vehicle. A predicted behavior of the at least one other vehicle could be determined based on the current state of the vehicle and the current state of the environment of the vehicle. A confidence level could also be determined based on the predicted behavior, the current state of the vehicle, and the current state of the environment of the vehicle. In some embodiments, the confidence level may be related to the likelihood of the at least one other vehicle to perform the predicted behavior. The vehicle in the autonomous mode could be controlled based on the predicted behavior, the confidence level, and the current state of the vehicle and its environment.|1. A method, comprising:
* determining, using a computer system (112), a current state of a vehicle (308), wherein the vehicle is configured to operate in an autonomous mode;
* determining, using the computer system, a current state of an environment of the vehicle (308), wherein the environment of the vehicle (308) comprises at least one other vehicle (312, 314);
* determining, using the computer system, a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* determining, using the computer system, a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308), and the current state of the environment of the vehicle (308); and
* controlling, using the computer system, the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 2. The method of claim 1, wherein determining the current state of the vehicle comprises determining at least one of a current speed of the vehicle, a current heading of the vehicle, a current position of the vehicle, and a current lane of the vehicle.
| 3. The method of claim 1, wherein determining the current state of the environment of the vehicle comprises determining at least one of a respective position of the at least one other vehicle, a respective speed of the at least one other vehicle, and a position of an obstacle.
| 4. The method of claim 1, wherein controlling the vehicle comprises at least one of controlling the vehicle to accelerate, controlling the vehicle to decelerate, controlling the vehicle to change heading, controlling the vehicle to change lanes, controlling the vehicle to shift within the current lane and controlling the vehicle to provide a warning notification.
| 5. The method of claim 1, wherein the predicted behavior is determined by obtaining a match or near match between the current states of the vehicle and the environment of the vehicle and predetermined scenarios.
| 6. The method of claim 4, wherein the warning notification comprises at least one of a horn signal, a light signal, and a vehicle-to-vehicle communication message transmission and optionally wherein the vehicle-to-vehicle communication message transmission is transmitted using a dedicated short range communications (DSRC) device.
| 7. A vehicle (308), comprising:
* at least one sensor (310), wherein the at least one sensor is configured to acquire:
* i) vehicle state information; and
* ii) environment state information;
* wherein the vehicle state information comprises information about a current state of the vehicle (308), wherein the environment state information comprises information about a current state of an environment of the vehicle (308), wherein the environment of the vehicle comprises at least one other vehicle (312, 314); and
* a computer system configured to:
* i) determine the current state of the vehicle (308) based on the vehicle state information;
* ii) determine the current state of the environment of the vehicle (308) based on the environment state information;
* iii) determine a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* iv) determine a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308), and the current state of the environment of the vehicle (308); and
* v) control the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 8. The vehicle of claim 7, wherein the at least one sensor comprises at least one of a camera, a radar system, a lidar system, a global positioning system, and an inertial measurement unit.
| 9. The vehicle of claim 7, wherein the computer system is further configured to determine the current state of the vehicle based on at least one of a current speed of the vehicle, a current heading of the vehicle, a current position of the vehicle, and a current lane of the vehicle.
| 10. The vehicle of claim 7, wherein the computer system is further configured to determine the current state of the environment of the vehicle based on at least one of a respective position of the at least one other vehicle, a respective speed of the at least one other vehicle, a position of an obstacle, and a map of the roadway.
| 11. The vehicle of claim 7, wherein the computer system is further configured to cause at least one of accelerating the vehicle, decelerating the vehicle, changing a heading of the vehicle, changing a lane of the vehicle, shifting a position of the vehicle within a current lane, and providing a warning notification.
| 12. The vehicle of claim 7, wherein the warning notification comprises at least one of a horn signal, a light signal, and a vehicle-to-vehicle communication message transmission.
| 13. The vehicle of claim 7, wherein the vehicle-to-vehicle communication message transmission is transmitted using a dedicated short range communications (DSRC) device.
| 14. A non-transitory computer readable medium having stored therein instructions executable by a computer system to cause the computer system to perform functions comprising:
* determining a current state of a vehicle (308), wherein the vehicle is configured to operate in an autonomous mode;
* determining a current state of an environment of the vehicle (308), wherein the environment of the vehicle comprises at least one other vehicle (312, 314);
* determining a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* determining a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308) and the current state of the environment of the vehicle (308); and
* controlling the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 15. A computer program to be executed by the computer system of the vehicle claimed in any one of claims 7 to 13 to perform a method as claimed in any one of claims 1 to 6. | The behavior modification method involves determining confidence level which comprises the likelihood of at least one other vehicle (314,316) to perform a predicted behavior including acceleration, deceleration, change heading, change lanes, and leaving roadway. The confidence level is determined is based on predicted behavior of other vehicle, current vehicle state, and current vehicle environment state. Own vehicle (308) is controlled in autonomous mode based on the predicted behavior, confidence level, current vehicle state, and current vehicle environment state. INDEPENDENT CLAIMS are included for the following:a vehicle; anda non-transitory computer readable medium for storing instructions executable by computer system. Behavior modification method for vehicle (claimed) e.g. truck based on predicted behavior of other vehicle. Interaction between vehicles is allowed through the peripherals. Safety is improved through the computer system that causes the vehicle to slow down slightly by reducing the throttle. The drawing shows the top view of the autonomous vehicle operating scenario.302Left-most lane304Center lane306Right-most lane308Own vehicle314,316Other vehicle320Scenario |
Please summarize the input | Portable universal autonomous driving systemThis invention includes an autonomous driving system for automobiles, comprising: one or more common electronic communication ports of autonomous driving (communication ports) that are built-in on the automobiles; and one or more universal autonomous driving portable controllers (portable controllers) that are to be plugged-in to the said communication ports that are built-in on the automobiles. The interfaces of the communication ports and the portable controllers are both standardized such that the portable controllers can be plugged-in universally to all of the automobiles that are equipped with the built-in communication ports. The communication ports comprise electronic communication of all relevant electronic control units (ECUs) and feedback information of the automobiles, dedicated for the said portable controllers to communicate with and to control the automobiles. In addition to the portable controllers, the communication ports comprise a buffer that is designed to execute a short duration of controls to make emergency stops, in case of loss of connection with the portable controllers due to accidents or other failure conditions. The portable controllers comprise a central control unit (CCU), and a plurality of sensors and processors, and a plurality of data storages, and a plurality of data links, and a Global Positioning System (GPS). The portable controllers have standardized interfaces that match with that of the communication ports. The invention disclosed herein enables all automobiles to be ready for autonomous driving with minimal cost, provided that the said communication ports are adapted to the automobiles. The said portable controllers integrate all the hardware and software relevant to autonomous driving as standalone devices which can share the components, simplify the systems, reduce parasitic material and components, and most importantly, will be safer when multiple sensors and processors that are based on different physics are grouped together to detect objects and environment conditions. A method of compound sensor clustering (CSC) is introduced herein. The CSC method makes the sensors and processors to self-organize and to address real-world driving conditions. The portable controllers can be mass-produced as standard consumer electronics at lower cost. The portable controllers can also be more easily updated with the latest technologies since that they are standalone devices, which would be otherwise hard to achieve when the hardware and software are built-in permanently as part of the automobiles. The invention disclosed herein is more efficient, since that the portable controllers can be plugged-in to the automobiles when there are needs for autonomous driving, comparing with current methods of integrating autonomous driving control hardware and software that are built-in to automobiles permanently, which may not be used for autonomous driving frequently. The system also decouples the liability from automotive manufactures in case of accidents. The portable controllers can be insured by insurance companies independently, much like insuring human drivers.I claim:
| 1. An autonomous driving system for an automobile, comprising:
a) one or more common electronic communication ports ( 100) for autonomous driving, wherein the communication ports are built-in on the automobile;
b) one or more universal autonomous driving portable controllers, wherein said portable controllers are plugged in to the exterior of the automobile via the communication ports to detect a driving environment and to control the automobile for autonomous driving; wherein the communication ports and portable controllers share common interfaces;
c) said one or more communication ports having a primary high speed control area network wherein said primary high speed control area network providing communication between said one or more portable controllers via said one or more communication ports, and at least one electronic control unit, further wherein said at least one electronic control unit configured to control at least one of steering, braking, and acceleration;
d) said one or more communication ports having a secondary control area network, said secondary control area network configured to provide electronic communication, via said one or more communication ports, between said one or more portable controllers and secondary electronic control units, said secondary electronic control units configured to control at least one of turn signals, brake lights, emergency lights, head lamps and tail lamps, fog lamps, windshield wipers, defrosters, defogs, window regulators, and door locks;
e) said one or more communication ports having a tertiary control area network configured to electronically communicate at least one feedback parameter to said one or more portable controllers, via said one or more communication ports, said at least one feedback parameter comprised one or more of velocity, acceleration, ABS activation, airbag deployment, and traction control activation;
f) said one or more communication ports having a quaternary control area network configured to electronically communicate at least one status parameter to said one or more portable controllers via said one or more communication ports, said at least one status parameter comprised of one or more of fuel level, battery charge, tire pressure, engine oil level, coolant temperature, and windshield washer level;
g) said one or more communication ports having a buffer memory controller that provides emergency control instruction for emergency stops of the automobiles in the event of loss of electronic connection with the portable controller due to accidents or other failure conditions;
h) said one or more communication ports having electronic connections to the portable controllers and adapted to take at least one of the methods of: wired pin connections, wireless connections, or combinations of wired pin and wireless connections;
i) said one or more portable controllers adapted for mounting locations and anchorages for the portable controllers, which match with the configurations of the portable controllers;
j) a driver interface, said driver interface positioned to enable the driver to provide driving instructions to said one or more portable controllers;
k) said one or more portable controllers having a plurality of sensors, said plurality of sensors comprising:
i. one or more digital color cameras that detect optical information;
ii. one or more LIDARs that detect geometrical information;
iii. task specific sensors, including one or more ultrasonic sensors to detect near distance objects;
iv. one or more RADARs to detect median and far distance objects;
v. one or more thermal imaging cameras or passive infrared sensors to detect objects that have heat emissions;
vi. one or more three dimensional accelerometers to detect acceleration and vibration in vertical, lateral, and fore/aft directions;
vii. one or more gyroscopes to detect inclination angles;
viii. one or more physical-chemical sensors which adapted to detect specific air contents;
ix. one or more sound sensors to detect human languages or warning sirens;
x. one or more water sensors for detecting rain and rain intensity;
xi. one or more temperature sensors adapted for detecting temperature at the vicinity of the automobiles;
l) said one or more portable controllers having a plurality of processors comprising:
i. one or more processors for the digital color cameras;
ii. one or more processors for the LIDARs;
iii. one or more processors for the ultrasonic sensors;
iv. one or more processors for the RADARs;
v. one or more processors for the thermal imaging cameras or passive infrared sensors;
vi. one or more processors for the one or more three dimensional accelerometers;
vii. and one or more processors for the gyroscopes;
viii. one or more processors for the physical-chemical sensors;
ix. one or more processors for the sound sensors;
x. one or more processors for the water sensors;
xi. one or more processors for the temperature sensors;
m) said one or more portable controllers programmed to generate driving instructions based on information from said plurality of processors; said processors of the plurality of processors programmed to generate queries addressing specific driving conditions, said specific driving conditions being determined by pre-defined criteria, wherein said queries include queries between the processors of said plurality of processors, said queries programmed in the processors;
n) said one or more portable controllers having a Central Control Unit to direct the operations of the processors;
o) said one or more portable controllers having a plurality of communication links to send and/or receive data, said communication links including vehicle-to-vehicle and vehicle-to-infrastructure links;
p) said one or more portable controllers having a global positioning system to identify the locations of the automobiles to which the portable controllers are plugged-in;
q) said one or more universal autonomous driving portable controllers are compatible with said communication ports.
| 2. The autonomous driving system of claim 1 wherein,
a. the processors of said plurality of processors are integrated into said one or more portable controllers,
b. the sensors of said plurality of sensors are each integrated with at least one of the processors.
| 3. The autonomous driving system of claim 2 wherein the sensors of said plurality of sensors are built on one or more common substrates and/or integrated circuit boards.
| 4. The autonomous driving system of claim 2 further comprising wherein querying sensors are dynamically organized as clusters to function as groups such that sensors and processors communicate with each other to validate sensed information pertaining to specific driving conditions.
| 5. The autonomous driving system of claim 2 further comprising wherein queries function to detect mismatches between information between sensors and alert the Central Control Unit when mismatches are found.
| 6. The autonomous driving system of claim 5 wherein a mismatch between LIDARs and RADARs generates an alert to the central Control Unit, thereby enabling the Central Control Unit to respond to potential hazards.
| 7. The autonomous driving system of claim 5 wherein information derived from queries from the temperature sensors and water sensors is used to jointly determine a potential freezing rain condition.
| 8. The autonomous driving system of claim 5 wherein the queries for detection of said potential freezing rain condition include detection of rain, and/or ice, and/or snow using captured images and pattern recognition.
| 9. The autonomous driving system of claim 5 wherein detection of smoke by said physical chemical is used to query the thermal imaging cameras of passive infrared sensors to determine if there is a hazardous fire condition.
| 10. The autonomous driving system of claim 5 wherein road curvatures are detected by the cameras and/or LIDARs when lateral acceleration is detected by combined information from said gyroscopes and accelerometers to inform the central control unit of lateral stability status.
| 11. The autonomous driving system of claim 8 wherein ABS activation feedback triggers querying the water and temperature sensors.
| 12. The autonomous driving system of claim 2 wherein the cameras are queried to identify icy road surfaces, thereby generating a categorized information of low coefficient of friction road surface to the Central Control Unit.
| 13. The autonomous driving system of claim 5 wherein information derived from queries from the cameras and thermal sensors is used to jointly verify an existence of pedestrians.
| 14. The autonomous driving system of claim 5 wherein,
a. said thermal sensors are queried to detect a human heat signature, and if the human heat signature is detected, then,
b. the thermal sensor's processor queries object detection sensors for the presence of a human, said object sensors comprising the cameras, LIDARs, RADAR and/or the ultrasonic sensors.
| 15. The autonomous driving system of claim 5 wherein information derived from the RADARs and/or the ultrasonic sensors detection of a potential road sign generates a query to the cameras, thereby reducing likelihood of missing or misidentifying road signs.
| 16. The autonomous driving system of claim 15 wherein the queries from a RADAR are generated for detection of a road sign not identified or misidentified by camera captured images and pattern recognition.
| 17. The autonomous driving system of claim 3 further comprising wherein querying sensors are dynamically organized as clusters to function as groups such that sensors and processors communicate with each other to validate sensed information pertaining to specific driving conditions.
| 18. The autonomous driving system of claim 17, further comprising wherein queries function to detect mismatches between information between sensors and alert the Central Control Unit when mismatches are found.
| 19. The autonomous driving system of claim 14, wherein the sensors of said plurality of sensors are built on one or more common substrates and/or integrated circuit boards.
| 20. The autonomous driving system of claim 3, wherein one or more of the queries from at least one of said one or more RADARs are generated for detection of road signs not identified or misidentified by camera captured images and pattern recognition. | The computerized control system has common electronic communication ports (100) that are built-in on each of automobiles, and one or more universal autonomous driving portable controllers (200) that can be attached to the automobiles via the communication ports to accomplish the computerized control or autonomous driving. INDEPENDENT CLAIMS are included for the following:a design for buffer memory controller (BMC);a design of location of interface of communication ports;a design of the communication ports;a design of universal autonomous driving portable controllers;a sensor;a compound sensor clustering method;a back-up safety mechanism of interacting with the buffer memory controller; anda manufacturing rights of the electronic communication port of autonomous driving. Computerized control system or autonomous driving for automobiles. Ensures that computerized control or autonomous driving much more efficient, since that the portable controllers can be plugged-in to any of the automobiles that are equipped with the communication ports when there are needs for autonomous driving. The drawing shows the design of universal autonomous driving portable controller and its relation to common electronic communication port of autonomous driving. 100Common electronic communication ports160Mounting fixtures on automobiles200Autonomous driving portable controllers221Data storages230Central control unit241Data links260Wired or wireless user interface |
Please summarize the input | PREDICTING REALISTIC TIME OF ARRIVAL FOR QUEUE PRIORITY ADJUSTMENTA queue prioritization system and method for predicting a realistic time of arrival for performing a queue priority adjustment is provided. The method includes the steps of determining an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority, tracking a current location and predicting a route to be taken to arrive at the destination from the current location, detecting a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle, and reprioritizing the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.CLAIMS
| 1. A method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising:
determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively; detecting, by the processor, a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.
| 2. The method of claim 1, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 3. The method of claim 2, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 4. The method of claim 1, wherein determining the estimated initial arrival time includes: receiving, by the processor, scheduled delivery information and current GPS location information of the first user and the second user;
reviewing, by the processor, historical delivery pattern data, based on previous
deliveries to the destination;
evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 5. The method of claim 1, wherein the detecting of the schedule -altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to- vehicle communication network, and a combination thereof.
| 6. The method of claim 1, wherein the schedule-altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 7. The method of claim 1, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 8. The method of claim 1, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 9. The method of claim 1, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone.
| 10. A computer system, comprising:
a processor;
a memory device coupled to the processor; and
a computer-readable storage device coupled to the processor, wherein the storage device contains program code executable by the processor via the memory device to implement a method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising: determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively;
detecting, by the processor, a schedule-altering event of the first user by
analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to
calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule- altering event of the first user.
| 11. The computer system of claim 10, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 12. The computer system of claim 1 1, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 13. The computer system of claim 10, wherein determining the estimated initial arrival time includes: receiving, by the processor, a scheduled delivery information and a current GPS location information of the first user and the second user;
reviewing, by the processor, a historical delivery pattern data, based on previous deliveries to the destination;
evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 14. The computer system of claim 10, wherein the detecting of the schedule-altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to- vehicle communication network, and a combination thereof.
| 15. The computer system of claim 10, wherein the schedule -altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 16. The computer system of claim 10, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 17. The computer system of claim 10, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 18. The computer system of claim 10, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone.
| 19. A computer program product, comprising a computer-readable hardware storage device storing a computer-readable program code, the computer-readable program code comprising an algorithm that when executed by a computer processor of a computing system implements a method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising:
determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively; detecting, by the processor, a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.
| 20. The computer program product of claim 19, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and a current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 21. The computer program product of claim 20, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 22. The computer program product of claim 19, wherein determining the estimated initial arrival time includes:
receiving, by the processor, a scheduled delivery information and current GPS
location information of the first user and the second user;
reviewing, by the processor, a historical delivery pattern data, based on previous deliveries to the destination; and evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 23. The computer program product of claim 19, wherein the detecting of the schedule -altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to-vehicle communication network, and a combination thereof.
| 24. The computer program product of claim 19, wherein the schedule -altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 25. The computer program product of claim 19, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 26. The computer program product of claim 19, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 27. The computer program product of claim 19, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone . | The method involves tracking a current location of a first user and a current location of a second user during transit to a destination by a processor (141). A route to be taken to arrive at the destination from the current location of the first user and the second user is predicted by the processor. A schedule-altering event of the first user is detected by the processor by analyzing the predicted route of the first user, or a current state of a vehicle. A queue priority database (114) is reprioritized by the processor in response to calculating an updated queue priority of the first user that is lower than queue priority of the second user based on the detection of the schedule-altering event of the first user. INDEPENDENT CLAIMS are also included for the following:a computer systema computer program product comprising a set of instructions for predicting a realistic time of arrival for performing a queue priority adjustment for a user at a retail location by a computer system. Method for predicting a realistic time of arrival for performing a queue priority adjustment for a user e.g. customer, delivery lorry driver, autonomous vehicle or unmanned drone, at a retail location by a computer system (all claimed). The method enables allowing goods to be retrieved in response to the predicted time of arrival based on user input information on a purchase order or to be automatically generated based on complexity of the purchase order. The method enables allowing the customers to pick up purchased items at the retail location selected by the customer by maintaining the queue priority such that the purchased items are ready for the customer when the customer arrives at the retail location. The drawing shows a schematic block diagram of a queue prioritization system. 113Customer database114Queue priority database120Computer system141Processor142Memory |
Please summarize the input | Method for controlling an autonomously operated vehicle and error control moduleThe invention relates to a method for controlling an autonomously operated vehicle (1) in the event of an error (Fm; m = 1,2, ... N0), with at least the following steps:
- Detection of a fault (Fm) in the vehicle (1);
- Evaluating the detected error (Fm) and, as a function thereof, outputting an error assessment result (EF), the error assessment result (EF) indicating a significance of the detected error (Fm);
- Determination of a local environment (Uk; k = 1,2, ..., N3) in which the vehicle (1) is located;
- Selecting and activating an emergency operating mode as a function of the determined local environment (Uk) as well as the output evaluation result (EF) and / or the significance of the detected error (Fm); and
- Autonomous control of the vehicle (1) as a function of the selected and activated emergency operating mode using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1).|1. Method for controlling an autonomously operated vehicle (1) in the event of an error (Fm; m = 1,2, ... N0), with at least the following steps:
- Detection of a fault (Fm) in the vehicle (1) (ST1);
- Assessment of the detected error (Fm) (ST2) and, as a function thereof, output of an error evaluation result (EF) (ST3), the error evaluation result (EF) having a significance (Bi; i = 1,2 ... N1) of the detected fault (Fm);
- Determination of a local environment (Uk; k = 1,2, ..., N3) in which the vehicle (1) is located (ST4);
- Selecting and activating an emergency operating mode (NBnk; n = 1,2, ..., N2) depending on the determined local environment (Uk) and the output evaluation result (EF) and / or the significance (Bi) of the detected error (Fm) (ST5); and
- Autonomous control of the vehicle (1) as a function of the selected and activated emergency operating mode (NBnk) using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305 ) in the vehicle (1) (ST6).
| 2. Procedure according to Claim 1, characterizedthat the error (Fm) from in the vehicle (1) via a vehicle-internal data transmission system (30), for example a CAN bus (31), or from directly transmitted status signals (So, o = 1, 2, ..., N4) is derived.
| 3. Procedure according to Claim 2, characterizedthat the status signals (So) from the at least one movement system (100; 101, 102, 103) of the vehicle (1) and / or environment detection system (300; 301, 302, 303, 304, 305) of the vehicle (1) and / or from further sources (3) of the vehicle (1), for example a V2X module (3), the status signals (So) indicating whether the respective movement system (100; 101, 102, 103) and / or the environment detection system (300; 301, 302, 303, 304, 305) has an error (Fm).
| 4. Method according to one of the preceding claims, characterizedthat as an error (Fm) at least
- an elementary, fatal error (F1) or
- a moderate error (F2) or
- a non-safety-critical error (F3) can be detected.
| 5. Method according to one of the preceding claims, characterizedthat the error evaluation result (EF) output is at least that there is an error (Fm) with high importance (B1) or medium importance (B2) or low importance (B3).
| 6. Procedure according to Claim 4 or 5, characterizedthat if present
- A fault (Fm) with high significance (B1) and / or an elementary, serious fault (F1) depending on the local environment (Uk) a first emergency operating mode (NB1k) is activated to use the vehicle (1) of the at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1) to be brought to a standstill (H) autonomously, or
- an error (Fm) with medium significance (B2) and / or a moderately serious error (F2) depending on the local environment, a second emergency operating mode (NB2k) is activated in order to move the vehicle (1) using the at least one movement system ( 100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1) to move autonomously to a stopping area (HB), or
- an error (Fm) of little importance (B3) and / or a non-safety-critical error (F3) depending on the local environment (Uk) a third emergency operating mode (NB3k) is activated in order to stop the autonomous driving of the vehicle (1) To continue using the at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1).
| 7. Procedure according to Claim 6, characterizedthat in the first emergency operating mode (NB1k) at least one brake system (101) and / or a drive system (103) for reducing the engine power (ML) is controlled autonomously in order to bring the vehicle (1) to a standstill (H), and as a function of the local environment (Uk), a steering system (103) is still controlled autonomously in order to enable an avoidance to a secured area (BS), for example an emergency lane (BS1), before the standstill (H) is reached.
| 8. Procedure according to Claim 6 or 7, characterizedthat in the second emergency operating mode (NB2k) a drive system (102) and / or a braking system (101) and / or a steering system (103) of the vehicle (1) are controlled autonomously in such a way that the vehicle (1) moves with reduced Speed ??(vred) and / or with reduced engine power (ML) moves autonomously along a defined driving trajectory (T) to the stopping area (HB).
| 9. Procedure according to Claim 8, characterizedthat the neck area (HB) and / or the reduced speed (vred) and / or the motor power (ML) is selected as a function of the local environment (Uk).
| 10. Method according to one of the Claims 6 to 9, characterizedthat the first emergency operating mode (NB1k) is fixed and unchangeable and / or the second emergency operating mode (NB2k) can be expanded and / or the third and / or further emergency operating modes (NBnk, for n> = 3) changed and / or can be expanded.
| 11. Method according to one of the preceding claims, characterizedthat a motorway (U1), a country road (U2), an urban environment (U3), a depot (U4), a construction site (U5) or a port area (U6) are determined as the local environment (Uk).
| 12. Method according to one of the preceding claims, characterizedthat the local environment (Uk) is an environment with a public traffic area (?) or an environment with an enclosed area (G).
| 13. Method according to one of the preceding claims, characterizedthat the local environment (Uk) is determined as a function of position information (PI) and / or environment information (UI), the position information (PI) being based on automatically provided position data (DPa) and / or manually entered position data (DPm) and the environment information (UI) is based on provided environment data (DU) that are output, for example, by environment detection systems (300; 301, 302, 303, 304, 305) in the vehicle (1) become.
| 14. Procedure according to Claim 13, characterizedthat the automatically provided position data (DPa) are output by a position detection device (70) and contain a global position (Pg) of the vehicle (1) and / or are output by a telematics system (400), the telematics system ( 400) accesses external information (IX) which is transmitted via a local data interface (8).
| 15. Procedure according to Claim 13 or 14, characterizedthat the local environment (Uk) is extracted from the automatically provided position data (DPa) via map data (KD), in particular a navigation system (7).
| 16. Method according to one of the preceding claims, characterizedthat the autonomously and / or driverlessly controlled vehicle (1) is controlled according to an autonomy level (AS) equal to three or higher.
| 17. Method according to one of the preceding claims, characterizedthat the error (Fm) and / or the error evaluation result (EF) after the detection (ST1) and the evaluation (ST2, ST3) of the error (Fm) via a communication module (50) and / or a V2X module ( 3) is issued, for example to a vehicle operator (K1), a dispatcher (K2), to yard staff (K3) of a depot (U4) and / or to other people (K4) and / or to another vehicle (2) and / or to infrastructure facilities (200).
| 18. Error control module (60) for autonomous control of a vehicle (1) in the event of an error (Fm), in particular according to a method according to one of the preceding claims, the error control module (60) being designed
- An emergency operating mode (NBnk; n = 1,2 .. N2) as a function of a local environment (Uk) determined from position information (PI) and / or environment information (UI), in which the vehicle (1 ) and to select and activate an evaluation result (EF) output by an error evaluation module (40) and / or a significance (Bi) of an error (Fm) detected by an error detection module (20), and
- The vehicle (1) autonomously depending on the selected and activated emergency operating mode (NBnk) using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) to control in the vehicle (1).
| 19. Vehicle (1) with a movement coordination module (10) for coordinating and controlling movement systems (100; 101, 102, 103) and / or surrounding systems (300; 301, 302, 303, 304, 305) in the vehicle (1) for autonomous control of the vehicle (1), an error detection module (20) for detecting an error (Fm) in the vehicle (1), an error evaluation module (40) for evaluating the detected error (Fm) and to output an evaluation result (EF) and with an error control module (60) Claim 18 for autonomous control of the vehicle (1), in particular via the movement coordination module (10), in the event of an error (Fm). | The method involves detecting a fault (Fm) in vehicle (1). The assessment of the detected fault is performed. An error evaluation result (EF) is outputted, where the error evaluation result has a significance of detected fault. A local environment in which the vehicle is located is determined. An emergency operating mode is selected and activated as a function of a determined local environment and the evaluation result and/or the significance of the detected fault. The autonomous control of the vehicle is performed as a function of the selected and activated emergency operating mode using one movement system (100-103) and/or environment detection system (300-304) in the vehicle. INDEPENDENT CLAIMS are included for the following:an error control module for autonomous control of a vehicle in the presence of an error; anda vehicle with a movement coordination module for coordinating and controlling movement systems and/or surrounding systems in the vehicle. Method for controlling autonomously operated vehicle e.g. truck in event of fault, using error control module (claimed). The autonomous driving operation is ensured safely and efficiently in the event of a fault. The vehicle is autonomously controlled as a function of the local environment when the error occurs, where the error that can have an effect on the own vehicle and/or on an environment around the own vehicle taxes to a certain extent. The autonomous vehicles are operated in a single local environment, and in different local environments. The efficiency and the possibility of reacting to error can turn out to be different depending on the local environment, so that the different emergency operating modes are selected or activated depending on the environment. The warning is effectively issued to external persons or vehicles in addition to the autonomous control, when the vehicle is driving without any occupants. The drawing shows a schematic view of the autonomously operated vehicle in local environment with public traffic area. 1Vehicle100-103Movement system300-304Environment detection systemEFError evaluation resultFmFault |
Please summarize the input | METHOD FOR TRANSFORMING BETWEEN A LONG VEHICLE COMBINATION AND A PLATOON ON THE MOVEThe invention relates to a method for transforming between a long vehicle combination (10) and a platoon (12) on the move. The present invention also relates to vehicles (14a-b; 14b-c) for such a method.|1. A method for transforming between a long vehicle combination (10) and a platoon (12) on the move, wherein the long vehicle combination (10) comprises a plurality of vehicles (14a-c) mechanically coupled together one after the other, which method comprises the steps of:
* detecting (S2) that the long vehicle combination (10) is approaching a first road section (58) ahead, by means of a navigation system (22) or by means of wireless vehicle-to-infrastructure (V2I) communication, which first road section (58) stipulates decoupling the vehicles (14a-c) of the long vehicle combination (10) to form the platoon (12);
* automatically decoupling (S4) the vehicles (14a-c) from each other while the vehicles (14a-c) are in motion to form the platoon (12) before reaching the first road section (58);
* the platoon (12) driving (S5) through the first road section (58);
* detecting (S6) a second road section (62), by means of a navigation system (22) or by means of wireless vehicle-to-infrastructure (V2I) communication, which stipulates coupling together the vehicles (14a-c) of the platoon (12) to form the long vehicle combination (10);
* a vehicle (14a-b) in the platoon (12) immediately ahead of a following vehicle (14b-c) of said the platoon (12) sending (S7) information (64) to the following vehicle (14b-c) via wireless vehicle-to-vehicle communication, which information (64) indicates the position and speed of a rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead;
* based at least on the position and speed indicated in the sent information (64), autonomously driving (S8) the following vehicle (14b-c) so that the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead of the following vehicle (14b-c) gets within an operational range (66) of a front coupling element (32) of the following vehicle (14b-c);
* while in motion and when the rear automatic coupling device (18) is within the operational range (66), the following vehicle (14b-c) automatically adjusting (S9) a front coupling device (30) including said front coupling element (32) so that the position of the front coupling element (32) matches the position of the rear automatic coupling device (18) as indicated in the sent information (64); and
* automatically coupling (S10) together the following vehicle (14b-c) and the vehicle (14a-b) immediately ahead while the vehicles (14a-c) are in motion to form at least a part of the long vehicle combination (10),
wherein each vehicle (14a-b) immediately ahead is adapted to estimate the position of its rear automatic coupling device (18) based on
* the heading of the vehicle (14a-b) immediately ahead,
* the position of a part of the vehicle (14a-b) immediately ahead as determined by a navigation system (22) of the vehicle immediately ahead,
* a vehicle model representing the vehicle (14a-b) immediately ahead,
* the height of the rear automatic coupling device (18), and
* in case the vehicle (14a-b) immediately ahead is an articulated vehicle, at least one articulation angle of the vehicle immediately ahead as detected by at least one articulation angle detection means (28) on the vehicle immediately ahead.
| 2. A method according to claim 1, wherein each following vehicle (14b-c) comprises actuator means (48a-b) adapted to adjust the front coupling device (30).
| 3. A method according to claim 2, wherein the actuator means (48a-b) is adapted to laterally adjust the front coupling device (30).
| 4. A method according to claim 2 or 3, wherein the actuator means (48a-b) is adapted to vertically adjust the front coupling device (30).
| 5. A method according to any preceding claim, wherein each following vehicle (14b-c) comprises means (54) adapted to adjust the length of the front coupling device (30).
| 6. A method according to claim 5, further comprising the step of: shortening (S1) the length of the front coupling device (30) while driving as the long vehicle combination.
| 7. A method according to any preceding claim, wherein each following vehicle (14b-c) is adapted to estimate the position of its front coupling element (32) based on
* the heading of the following vehicle (14b-c),
* the position of a part of the following vehicle (14b-c) as determined by a navigation system (36) of the following vehicle (14b-c),
* a vehicle model representing the following vehicle (14b-c),
* a first angle representing a lateral adjustment of the front coupling device (30),
* a second angle representing any vertical adjustment of the front coupling device (30),
* the length of the front coupling device (30), and
* a height related to the front coupling device (30).
| 8. A method according to any preceding claim, wherein each vehicle immediately ahead (14a-b) comprises at least two independent means (21, 22) for determining its speed.
| 9. A method according to any preceding claim, further comprising the step of: a leading vehicle of the platoon sending an acceleration or deceleration request (63) to the following vehicles (14b-c) of the platoon (12) via wireless vehicle-to-vehicle communication.
| 10. A method according to any preceding claim, wherein the information (64) sent from the vehicle (14a-b) immediately ahead to the following vehicle (14b-c) includes the heading of the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead.
| 11. A method according to any preceding claim, wherein the first road section (58) is at least one of a bridge, a roundabout, and a turn.
| 12. A method according to any preceding claim, further comprising the step of planning (S3) an inter-vehicle distance (60) between subsequent vehicles based on the first road section (58) ahead, wherein the platoon (12) is driven through the first road section (58) with the planned inter-vehicle distance(s) (60).
| 13. A method according to any preceding claim, wherein at least one of the automatic decoupling and the automatic coupling is performed while driving at a safety speed.
| 14. A method according to any preceding claim, wherein the automatic coupling is performed while driving on a straight road.
| 15. A method according to any preceding claim, wherein the automatic coupling starts with the vehicle (14b) immediately behind the leading vehicle (14a) of the platoon (12) coupling to the leading vehicle (14a) of the platoon (12).
| 16. A method according to any preceding claim, wherein the automatic decoupling starts with the last vehicle (14c) of the long vehicle combination (10) decoupling from the vehicle immediately ahead (14b).
| 17. A method according to any preceding claim, wherein each vehicle (14b-c) after the leading vehicle (14a) of the long vehicle combination (10) or platoon (12) is an autonomous vehicle.
| 18. A method according to any preceding claim, wherein at least one vehicle (14b-c) after the leading vehicle (14a) of the long vehicle combination (10) or platoon (12) is an autonomous dolly (16) and semi-trailer combination.
| 19. A vehicle (14a-b) comprising:
* a rear automatic coupling device (18); means (21) for speed determination;
* a control unit (20) adapted to estimate the position of the rear automatic coupling device (18) while the vehicle (14a-b) is in motion based on the heading of the vehicle (14a-b), the position of a part of the vehicle (14a-b) as determined by a navigation system (22) of the vehicle, a vehicle model representing the vehicle (14a-b), the height of the rear automatic coupling device (18) as determined by a height level sensor (24), and in case the vehicle (14a-b) is an articulated vehicle, at least one articulation angle of the vehicle as detected by at least one articulation angle detection means (28) on the vehicle; and
* communication means (26) adapted to wirelessly send information (64) indicating the estimated position and the speed of the rear automatic coupling device (18) to a following vehicle (14b-c).
| 20. A vehicle (14b-c) comprising:
* a front coupling device (30) including a front coupling element (32);
* a control unit (34) adapted to estimate the position of the front coupling element (32) while the vehicle (14b-c) is in motion;
* a navigation system (36) and a height level sensor (38);
* communication means (40) adapted to wirelessly receive information (64) from a vehicle (14a-b) immediately ahead, which information (64) indicates the position and speed of a rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead;
* autonomous driving means (42) adapted to drive the vehicle (14b-c) based at least on the position and speed in the received information (64) so that the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead gets within an operational range (66) of the front coupling element (32); and
* means (48a-b, 54) adapted to automatically adjust the front coupling device (30), while in motion and when the rear automatic coupling device (18) is within the operational range (66), so that the position of the front coupling element (32) matches the position of the rear automatic coupling device (18) as indicated in the received information (64). | The method involves detecting (S6) second road section which stipulates coupling together the vehicles of the platoon to form the long vehicle combination. The information is sent (S7) to the following vehicle through wireless vehicle-to-vehicle communication. The information indicates the position and speed of rear automatic coupling device of the vehicle immediately ahead. The following vehicle is automatically driven (S8) so that the rear automatic coupling device of the vehicle immediately ahead of the following vehicle gets within an operational range of front coupling element of the following vehicle. The front coupling device is automatically adjusted (S9) so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent information. The following vehicle and the vehicle immediately ahead are automatically coupled (S10) together while the vehicles are in motion to form portion of the long vehicle combination. An INDEPENDENT CLAIM is included for a vehicle. Method for transforming between long vehicle combination and platoon on move, for heavy duty vehicle e.g. truck. The following vehicle can receive the correct speed allowing to safely drive so that the rear automatic coupling device of the vehicle immediately ahead gets within the operational range, even if one of the systems fails. The acceleration or deceleration request sent through wireless vehicle-to-vehicle communication can allow following vehicle to safely drive within the operational range, even if the operational range results in relatively short headway between the following vehicle and the vehicle immediately ahead and even if the speed is relatively high. The long vehicle combination can be automatically re-formed in motion after the roundabout or turn, to improve fuel efficiency. The front coupling device is automatically adjusted so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent information. The drawing shows a flowchart illustrating the method for transforming between long vehicle combination and platoon on move. S6Step for detecting second road section which stipulates coupling together the vehicles of the platoon to form the long vehicle combinationS7Step for sending information to the following vehicle through wireless vehicle-to-vehicle communicationS8Step for automatically driving following vehicle so that the rear automatic coupling device of the vehicle immediately ahead of the following vehicle gets within an operational range of front coupling element of the following vehicleS9Step for automatically adjusting front coupling device so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent informationS10Step for automatically coupling together the following vehicle and the vehicle immediately ahead while the vehicles are in motion to form portion of the long vehicle combination |
Please summarize the input | A METHOD FOR PROVIDING A POSITIVE DECISION SIGNAL FOR A VEHICLEA method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.|1. A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action, such as entering a crossing, entering a highway and/or changing lanes, the method comprising:
receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user;
calculating a value based on the received information;
providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition, wherein the value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration, characterized in that, the surrounding road user is a predefined virtual surrounding road user and the predetermined condition is defined by a threshold value which is indicative of an acceleration limit for the surrounding road user.
| 2. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration after a reaction time.
| 3. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration to an acceleration profile having a constant acceleration.
| 4. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration to an acceleration profile having a variable acceleration.
| 5. The method according to claim 1, further comprising providing a negative decision signal not to perform the traffic scenario action when the calculated value is not fulfilling the predetermined condition.
| 6. The method according to claim 1, wherein the threshold value is variable depending on at least one factor, such as any one of speed of the surrounding road user, type of surrounding road user, ambient weather conditions with respect to the vehicle and a state of the surrounding road user, such as a state where a turning indicator is active.
| 7. The method according to claim 1, wherein the information about the at least one surrounding road user is received by any one of a perception sensor of the vehicle, a V2X communication interface and a remote perception sensor which is in communicative contact with the vehicle.
| 8. The method according to claim 1, wherein the method is used as a safety control method for an autonomous vehicle, wherein the autonomous vehicle is primarily performing traffic scenario actions by use of a primary autonomous vehicle control method, and wherein a traffic scenario action permitted to be performed by the primary autonomous vehicle control method is allowed to be performed if also the positive decision signal is provided.
| 9. The method according to claim 1, wherein the calculated value is further based on auxiliary information relating to the traffic scenario action, such as any one of shape and/or dimension(s) of a crossing, a road lane and a neighboring road lane.
| 10. A method for automatically performing a traffic scenario action of a vehicle, comprising:
providing a positive decision signal to perform the traffic scenario action, which positive decision signal has been provided according to the method of claim 1; and
automatically performing the traffic scenario action.
| 11. A method for automatically avoiding performing a traffic scenario action of a vehicle, comprising:
providing a negative decision signal not to perform the traffic scenario action, which negative decision signal has been provided according to the method of claim 5; and
automatically avoiding performing the traffic scenario action.
| 12. A control unit for a vehicle which is configured to perform the steps of claim 1.
| 13. A vehicle comprising the control unit according to claim 12.
| 14. The vehicle according to claim 13, wherein the vehicle is a fully autonomous or semiautonomous vehicle.
| 15. The vehicle according to claim 13, wherein the vehicle is a road vehicle, such as a public road vehicle, for example a truck, a bus and a construction equipment vehicle adapted to be driven on a road.
| 16. The vehicle according to claim 13, wherein the vehicle is a heavy-duty vehicle which has a minimum weight of at least 5000 kg, such as 30.000 kg.
| 17. A computer program comprising program code means for performing the steps of claim 1, when said program is run on a computer.
| 18. A computer readable medium carrying a computer program comprising program code means for performing the steps of claim 1, when said program product is run on a computer. | The method involves receiving information about the surrounding road user (2), which information is indicative of distance to the surrounding road user with respect to the vehicle and the speed and acceleration of the surrounding road user. A value is calculated based on the received information. The positive decision signal is provided to perform the traffic scenario action, when the calculated value is fulfilled a predetermined condition. The value is calculated based on an assumption that the surrounding road user is reacted on the traffic scenario action by changing the acceleration of the vehicle. INDEPENDENT CLAIMS are included for the following:a vehicle;a computer program for providing positive decision signal for vehicle; anda computer readable medium carrying computer program for providing positive decision signal for vehicle. Method for providing positive decision signal for vehicle (claimed). The surrounding road user increases and reduces the speed when the vehicle initiates lane change to the nearby lane to avoid the risk of collision. The improved and cost-efficient redundancy for the autonomous vehicle is implied. The drawing shows a schematic view of the traffic scenario. 1Heavy-duty truck2Surrounding road user |
Please summarize the input | method for the gap between vehicle control teamThe invention claims a vehicle gap (24a-24c) between control vehicle in method (10), the train (10) comprises a preceding vehicle (12) and one or a plurality of following vehicle (14a-14c). the method comprises the following steps: indicating parameter (27) acquires the potential collision threat (26) recognized by leader vehicle autonomous emergency braking system (16), wherein leader of the autonomous vehicle emergency braking system comprises a plurality of predefined control period (28a-28c), and wherein the parameter for indicating the current control stage at least partially determining the autonomous emergency braking system, and obtained the indication parameter is transmitted to the one or more of the following vehicle.|1. A vehicle gap (24a-24c) between a control vehicle in method (10), the vehicle (10) comprises a preceding vehicle (12) and one or a plurality of following vehicle (14a-14c), wherein, the feature of the method lies in the following steps: obtaining by the leader vehicle autonomous emergency braking system (16) identifies the potential collision threat indication parameter (27) (26), wherein the leading vehicle of the autonomous emergency braking system comprises a plurality of predefined control stage (28a-28c). and wherein the current control stage, the indication parameter at least partially determining the autonomous emergency braking system, and the obtained by the indication parameter is transmitted to the one or more of the following vehicle.
| 2. The method according to claim 1, further comprising the following step: in the one or more of the following vehicle receiving the indication parameter, and automatically adjusting the clearance between vehicle parameters based on the indication received.
| 3. The method according to claim 1 or 2, wherein the indicating parameter is collision time.
| 4. The method according to claim 2 or 3, wherein the step of adjusting the gap between the vehicle comprises automatically based on the indication parameter of the received: the one or more of the following vehicle following vehicle (14c) according to the position of the following vehicle in the motorcade from the avoidance time minus a predetermined time so as to generate the collision time (TTC14C), and the following vehicle based on the collision time is reduced to adjust the following vehicle and the preceding vehicle (14b) and gap (24c).
| 5. The method according to claim 2, wherein the step of automatically adjusting the clearance between the vehicle starts at the fleet of the last vehicle (14c) based on the received indicating parameter so as to increase the last vehicle (14c) and the gap between the preceding vehicle (14b) (24c).
| 6. The method according to claim 2, wherein the step of automatically adjusting the clearance between said vehicle is started before the complete braking phase of the leading vehicle in the autonomous emergency braking system (28c) based on the received indicating parameter.
| 7. The method according to claim 2, further comprising: the driver of the leading vehicle with respect to the vehicle of the last vehicle (14c) how to adjust the last vehicle (14c) and the preceding vehicle (14b) between the gap (24c).
| 8. The method according to claim 1, wherein, using a vehicle-to-vehicle communication device (18) to perform the indication parameter.
| 9. The method according to claim 2, wherein, using a vehicle-to-vehicle communication device (32a-32c) to perform reception of the indication parameter.
| 10. The method according to any one of the preceding claims, further comprising: a friction-based estimation value for determining the retarding capacity of the leader vehicle.
| 11. The method according to claim 2 and 10, wherein the step of automatically adjusting the clearance between the vehicle based on the received indication parameter comprises: also considers the reduction ability.
| 12. A vehicle gap (24a-24c) between a control vehicle in method (10), the vehicle (10) comprises a preceding vehicle (12) and one or more following vehicle (14a-14c), wherein the feature of the method lies in the following steps: indicating parameter (27) in the one or more of the following vehicle receiving the potential collision threat (26) identified by autonomous of the leader vehicle emergency brake system (16), wherein the leader vehicle of the autonomous emergency braking system comprises a plurality of predefined control period (28a-28c), and wherein the indication parameter the current control stage at least partially determining the autonomous emergency braking system, and automatically adjusting the clearance between vehicle parameters based on the indication received.
| 13. A computer program comprising program code, when said program is run on a computer, said program code to perform the method according to any one of claims 1-12 the step.
| 14. A computer readable medium, the computer readable medium carrying a computer program comprising program code, when said program product is run on a computer, said program code to perform the method according to any one of claims 1-12 the step.
| 15. A control unit (22, 34a-34c), said control unit is used for controlling the clearance between the vehicle in the fleet, the control unit is configured to perform the method according to any one of claims 1-12 the steps of the method.
| 16. A vehicle (12; 14a-14c), the vehicle (12; 14a-14c) is configured to perform the method according to any one of claim 1-12 the steps of the method. | The method involves obtaining an indicator (27) of potential collision threat (26) identified by an autonomous emergency braking system (16) of a lead vehicle, where the braking system of the lead vehicle comprises pre-defined control phases, and the indicator determines current control phase of the braking system. The obtained indicator is sent to following vehicles. The indicator is received in the following vehicles. Inter-vehicle gaps (24a-24c) are automatically adjusted based on the received indicator. INDEPENDENT CLAIMS are also included for the following:a computer program comprising a set of instructions for controlling inter-vehicle gaps in a platoona computer readable medium comprising a set of instructions for controlling inter-vehicle gaps in a platoona control unit for controlling inter-vehicle gaps in a platoona vehicle. Method for controlling inter-vehicle gaps between vehicles (claimed), e.g. lorries, buses and passenger cars, in a platoon. The method allows the lead vehicle to remain predictable for the following vehicles even if a slippery or low friction road reduces deceleration capacity and calls for earlier braking, and building buffer distance to mitigate effects of different braking capacity of the vehicles in the platoon. The method enables exploring possibilities to drive road vehicles in the platoons or road trains with small time gaps so as to save fuel and decrease driver workload and road footprint in an effective manner. The drawing shows a schematic view of a platoon. 10Platoon16Autonomous emergency braking system24a-24cInter-vehicle gaps26Potential collision threat27Indicator |
Please summarize the input | A METHOD FOR FORMING A VEHICLE COMBINATIONThe present disclosure relates to a method for selecting and identifying a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, the method being implemented by one or more processors of a wireless control system, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the method comprising: receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating (S20) the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting (S30) a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating (S40) the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating (S50) the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.|1. A method for selecting and identifying a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, the method being implemented by one or more processors of a wireless control system, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the method comprising:
* - receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination;
* - evaluating (S20) the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - selecting (S30) a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locating (S40) the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicating (S50) the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to the primary vehicle.
| 2. Method according to claim 1, further comprising selecting one or more trailers among a group of trailers based at least in part on the mission-characteristic for the vehicle combination and the selected powered dolly vehicle; communicating the location of the selected one or more trailers to the powered dolly vehicle, or to the primary vehicle; and operating the powered dolly vehicle to couple with the one or more trailers.
| 3. Method according to any one of the preceding claims, wherein the mission-characteristic of the vehicle combination comprises any one of an assignment instruction for the vehicle combination, a cargo space-requirement for the vehicle combination, a pick-up location of the cargo, a pick-up time for the cargo, a delivery time for the cargo, a delivery location of the cargo, and data indicating type of cargo.
| 4. Method according to any one of the preceding claims, wherein the mission-characteristic of the vehicle combination comprises data indicating type of primary vehicle.
| 5. Method according to any one of the preceding claims, further comprising receiving data relating to environmental conditions.
| 6. Method according to any one of the preceding claims, wherein the associated operational characteristic comprises data indicating any one of a brake capacity of the powered dolly vehicle, energy storage system capacity of the powered dolly vehicle, and state of charge of the energy storage system of the powered dolly vehicle.
| 7. Method according to any one of the preceding claims, wherein the associated operational characteristic comprises data indicating type of powered dolly vehicle.
| 8. Method according any one of the preceding claims, wherein the associated distinguishable identification information comprises an identification component configurable to be updated by the one or more processors of a wireless control system.
| 9. Method according to any one of the preceding claims, wherein the evaluating comprises determining if at least one operational characteristic of at least one of the powered dolly vehicles fulfils the mission-characteristic, or is at least sufficient for fulfilling the mission-characteristic.
| 10. Method according any one of the preceding claims, wherein the request is received at a remote-control source from the primary vehicle, the remote-control source comprising a transceiver for receiving the request from the autonomous vehicle.
| 11. Method according to claim 10, wherein the remote-control source comprises a memory configurable to contain and store the associated distinguishable identification information and operational characteristic of each one of the powered dolly vehicles.
| 12. Method according any one of the claims 10 to 11, comprising receiving the request from the primary vehicle at the remote-control source when the primary vehicle arrives at the geographical area.
| 13. Method according any one of the preceding claims, wherein the primary vehicle comprises a memory configurable to contain any one of the associated distinguishable identification information and operational characteristic of each one of the powered dolly vehicles.
| 14. Method according to any one of the preceding claims, comprising obtaining the operational characteristic directly from powered dolly vehicles.
| 15. Method according to any one of the preceding claims, comprising controlling any one of the primary vehicle and the selected powered dolly vehicle to couple to each other so as to form the vehicle combination.
| 16. A computer program comprising instructions, which when executed by one or more processors of a wireless control system, cause the one or more processors to perform operations comprising: receiving a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 17. A non-transitory computer-readable medium comprising instructions, which when executed by one or more processors of a control system, cause the one or more processors to perform operations comprising: receiving a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 18. A wireless control system for identifying and selecting a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the system comprising a memory that stores a set of instructions and one or more processors which use the instructions from the set of instructions to:
* - receive a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination;
* - evaluate the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locate the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicate the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 19. Wireless control system according to claim 19, further comprising a communication interface operably coupled to the one or more processors for receiving instructions and for transmitting the location of the selected powered dolly vehicle to the primary vehicle.
| 20. A vehicle for forming a vehicle combination with a powered dolly vehicle and one or more trailers, comprising a memory that stores a mission-characteristic for the vehicle combination and one or more processors which use the mission-characteristic to:
* - select a powered dolly vehicle among a group of powered dolly vehicles based at least in part on the mission-characteristic for the vehicle combination, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic;
* - evaluate the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locate the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicate the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to vehicle. | The method involves receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based on a mission-characteristic for the vehicle combination. The operational characteristic of each one of the powered dolly vehicles evaluated (S20) based on mission-characteristic. A powered dolly vehicle selected (S30) among the group of powered dolly vehicles based on evaluation. The selected powered dolly vehicle located (S40) in the geographical area based on the identification information. The location of the selected powered dolly vehicle is communicated (S50) to the primary vehicle or operating the powered dolly vehicle to the primary vehicle. INDEPENDENT CLAIMS are included for the following:a computer program for selecting and identifying powered dolly vehicle;a non-transitory computer-readable medium storing program for selecting and identifying powered dolly vehicle;a wireless control system for identifying and selecting a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers; anda vehicle for forming a vehicle combination with a powered dolly vehicle and one or more trailers. Method for selecting and identifying powered dolly vehicle such as electric-powered dolly, steerable dolly vehicles. The method enables providing more efficient transportation vehicle systems that are fully, or partially, autonomous, thus increasing operational capacity of heavy-duty vehicles by vehicle combinations with multiple vehicle units in form of trailer units. The drawing shows a flowchart illustrating method for selecting and identifying powered dolly vehicle. S10Step for receiving a request from the primary vehicle to select a powered dolly vehicleS20Step for evaluating the operational characteristic of each one of the powered dolly vehiclesS30Step for selecting a powered dolly vehicleS40Step for locating the selected powered dolly vehicle in the geographical areaS50Step for communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to the primary vehicle |
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