Target screening method and system based on driving intention of other vehicle, vehicle and equipment

Abstract

The invention discloses a target screening method and system based on other vehicle driving intentions, a vehicle and equipment. The target screening method based on the driving intention of the other vehicle comprises the steps that obstacle driving information is obtained according to road perception data, and the driving intention state of the other vehicle is determined according to the obstacle driving information; evaluating the state of the own vehicle according to the driving information of the own vehicle; according to the self-vehicle state, self-vehicle driving track optimization is carried out; and according to the driving intention state of the other vehicle and the optimized driving track of the own vehicle, optimizing a driving track fence of the own vehicle, and according to the driving track fence of the own vehicle, obtaining a final screening target. With the adoption of the method and the device, the target obstacle can be screened more accurately and efficiently on the premise of ensuring safety, and the driving comfort is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, system, vehicle and equipment for target screening based on the driving intention of another vehicle. Background Art

[0002] With the vigorous development of the automobile industry, intelligent driving has gradually entered people's lives. As an important part of assisted intelligent driving, Adaptive Cruise Control (ACC) technology has received widespread attention. So far, the ACC system has been fully proven to have the function of reducing driver fatigue and improving vehicle driving safety, and has been widely used. However, due to the inaccuracy of the camera's visual perception results and the irrationality of target screening, ghost braking and brake failure may occur when the driver follows the vehicle in front. In some cases, the target vehicle is about to enter or exit the trajectory range of the vehicle. Since only the existing state is considered during screening, it is easy to cause braking or acceleration that contradicts the habits of human drivers. That is, ghost braking and brake failure may occur when the driver follows the vehicle in front. For example, when the vehicle in front of you on the left suddenly squeezes into the lane you are driving, the ACC system will change the target to follow from the vehicle in front to the vehicle in front of you on the left, causing your car to decelerate suddenly. Or when the vehicle in front of you on the left suddenly does not want to squeeze into the lane you are driving, the ACC system will change the target to follow from the vehicle in front of you on the left back to the vehicle in front of you on the left, causing your car to accelerate suddenly. Sudden acceleration or deceleration will make the driver feel uncomfortable or even highly nervous. For another example, when the vehicle in front of you that you are following suddenly turns, the ACC system will lose the target to follow, causing it to accelerate suddenly, which will also make the driver feel uncomfortable. Summary of the invention

[0003] Based on this, it is necessary to provide a target screening method, system, vehicle and equipment based on the driving intention of other vehicles to address the above technical problems, which can screen target obstacles more accurately and efficiently while ensuring safety, thereby effectively improving driving comfort.

[0004] In a first aspect, a target screening method based on the driving intention of another vehicle is provided, comprising:

[0005] Obtain obstacle driving information based on road perception data, and determine the driving intention state of other vehicles based on the obstacle driving information;

[0006] According to the driving information of the vehicle, the vehicle status is evaluated;

[0007] Optimizing the driving trajectory of the vehicle according to the vehicle state;

[0008] According to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and the final screening target is obtained according to the driving trajectory fence of the own vehicle.

[0009] In some examples, obtaining obstacle driving information based on the road perception data, and determining the driving intention state of the other vehicle based on the obstacle driving information, includes:

[0010] According to the obstacle driving information, predict the lane changing / keeping probability of the other vehicle;

[0011] Combined with the obstacle driving information, the lane change / keeping probability is arbitrated to obtain the driving intention of the other vehicle;

[0012] The driving intention of the other vehicle is packaged in a predetermined data structure to obtain the driving intention state of the other vehicle.

[0013] In some examples, the step of evaluating the vehicle state based on the vehicle driving information includes:

[0014] Evaluate the lane-changing behavior of the ego vehicle based on its turn signal, steering wheel angle, and yaw rate;

[0015] Evaluate the driving scenario of the vehicle based on the steering wheel angle and yaw rate of the vehicle;

[0016] The longitudinal speed of the vehicle is evaluated based on the accelerator pedal opening and brake pedal opening of the vehicle.

[0017] In some examples, optimizing the driving trajectory of the vehicle according to the vehicle state includes:

[0018] The original trajectory of the ego vehicle is optimized according to the state of the ego vehicle. When the lane line is credible, the original trajectory of the ego vehicle is the center line of the driving lane. When the lane line is unavailable, the original trajectory of the ego vehicle is the driving trajectory fitted according to the yaw angular velocity.

[0019] In some examples, the optimizing the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and obtaining the final screening target according to the driving trajectory fence of the own vehicle, includes:

[0020] According to the driving intention state of the other vehicle, the driving trajectory fence of the own vehicle is optimized, wherein obstacles located within the driving trajectory fence are marked as dangerous preparation targets, and objects marked as dangerous preparation targets are arbitrated to output a final screening target.

[0021] In some examples, the arbitrating the objects marked as dangerous preparation targets and outputting the final screening targets includes:

[0022] The obstacles whose effective distance is closest to the longitudinal position of the vehicle are selected as final selection targets, and the final selection targets are output.

[0023] In the second aspect, a target screening system based on the driving intention of another vehicle is provided, comprising:

[0024] The target intention analysis module is used to obtain obstacle driving information based on the road perception data, and determine the driving intention state of other vehicles based on the obstacle driving information;

[0025] The vehicle state evaluation module is used to evaluate the vehicle state according to the vehicle driving information;

[0026] The comprehensive screening module is used to optimize the driving trajectory of the own vehicle according to the state of the own vehicle, and to optimize the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and to obtain the final screening target according to the driving trajectory fence of the own vehicle.

[0027] In a third aspect, a vehicle is provided, comprising: a target screening system based on the driving intention of another vehicle according to the second aspect.

[0028] In a fourth aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the target screening method based on the driving intention of another vehicle of the above-mentioned first aspect and any possible implementation method of the first aspect are implemented.

[0029] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the target screening method based on the driving intention of another vehicle in the above-mentioned first aspect and any possible implementation method of the first aspect are implemented.

[0030] In a sixth aspect, a computer program product is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the target screening method based on the driving intention of another vehicle in the above-mentioned first aspect and any possible implementation method of the first aspect are implemented.

[0031] By adopting the embodiments of the present application, obstacle driving information is obtained based on road perception data, and the driving intention state of the other vehicle is determined based on the obstacle driving information, and the state of the own vehicle is evaluated based on the driving information of the own vehicle. According to the state of the own vehicle, the driving trajectory of the own vehicle is optimized. Finally, according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and according to the driving trajectory fence of the own vehicle, the final screening target is obtained, which can more accurately and efficiently screen the target obstacles while ensuring safety, and effectively improve the driving comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 A flow chart of a target screening method based on the driving intention of another vehicle provided in an embodiment of the present application;

[0034] Figure 2 A schematic diagram of an application of a target screening method based on the driving intention of another vehicle provided in an embodiment of the present application;

[0035] Figure 3 Schematic diagram of the vehicle's driving trajectory before and after optimization of the target screening method based on the driving intention of another vehicle provided in an embodiment of the present application;

[0036] Figure 4 Schematic diagram of the vehicle's driving fence before and after optimization of the target screening method based on the driving intention of another vehicle provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of fence expansion of a target screening method based on the driving intention of another vehicle provided in an embodiment of the present application;

[0038] Figure 6 A structural block diagram of a target screening system based on the driving intention of another vehicle provided in an embodiment of the present application;

[0039] Figure 7 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The present application is further described in detail below in conjunction with the embodiments and drawings. It is to be understood that the specific embodiments described herein are only used to explain the relevant application, rather than to limit the application. It is also necessary to explain that, for ease of description, only the parts related to the application are shown in the drawings.

[0041] It should be noted that, in the absence of conflict, the embodiments of the present application, that is, the features of the embodiments, can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] The following describes in detail the target screening method, system, device and medium based on the driving intention of other vehicles according to an embodiment of the present application in conjunction with the accompanying drawings.

[0043] The target screening method based on the driving intention of another vehicle in the embodiment of the present application can output the predicted status of the surrounding obstacle vehicles according to the intention model of the driver of the other vehicle, evaluate the status of the own vehicle, and output the lateral and longitudinal evaluation status of the own vehicle. Finally, based on the predicted status of the surrounding obstacle vehicles and the lateral and longitudinal evaluation status of the own vehicle, the target is comprehensively evaluated to screen out the final target.

[0044] Figure 1 FIG. 1 is a flow chart of a target screening method based on the driving intention of another vehicle according to an embodiment of the present application. Figure 1 As shown, according to the target screening method based on the driving intention of another vehicle according to the embodiment of the present application, the steps include:

[0045] S101: Obtain obstacle driving information based on road perception data, and determine the driving intention state of other vehicles based on the obstacle driving information.

[0046] In one embodiment of the present application, obstacle driving information is obtained based on road perception data, and the driving intention state of the other vehicle is determined based on the obstacle driving information, including: predicting the lane changing / keeping probability of the other vehicle based on the obstacle driving information; arbitrating the lane changing / keeping probability in combination with the obstacle driving information to obtain the driving intention of the other vehicle; and packaging the driving intention of the other vehicle in a predetermined data structure to obtain the driving intention state of the other vehicle.

[0047] Combination Figure 2 As shown, it can be realized by the target intention analysis module. Specifically, the target intention analysis module can include a driver intention prediction module, a driver intention evaluation module and a driver intention output module. The target intention analysis module builds a driver intention prediction model based on the lateral position coordinates, longitudinal position coordinates, lateral speed, longitudinal speed, lateral acceleration, longitudinal acceleration, turn signal information, brake light information, and double flash warning light information of the surrounding obstacles output by the perception fusion module. The driver intention prediction model of the other car outputs the lane change / maintenance probability of the obstacle car, and the result is output to the downstream driver intention evaluation module of the other car. The evaluation module performs semantic decomposition on the result and arbitrates the result, and the output result is cut-in, cut-out, acceleration, deceleration and other specific states. The driver intention output module of the other car is responsible for packaging the result into a specified data structure type and outputting it to the downstream module.

[0048] S102: Evaluate the vehicle status based on the vehicle's driving information.

[0049] In one embodiment of the present application, the state of the vehicle is evaluated based on the driving information of the vehicle, including: evaluating the lane changing behavior of the vehicle based on the turn signal, steering wheel angle and yaw angular velocity of the vehicle; evaluating the driving scene of the vehicle based on the steering wheel angle and yaw angular velocity of the vehicle; and evaluating the longitudinal speed of the vehicle based on the accelerator pedal opening and brake pedal opening of the vehicle.

[0050] Combination Figure 2 As shown, it can be realized by the self-vehicle state evaluation module, wherein the self-vehicle state evaluation module includes a self-vehicle lane change behavior estimation module, a self-vehicle driving scene evaluation module and a self-vehicle longitudinal speed estimation module. Specifically, the self-vehicle state evaluation module builds a self-vehicle state evaluation model based on the self-vehicle speed, steering wheel angle, yaw angular velocity, turn signal, accelerator pedal opening, brake pedal opening and other information output from the self-vehicle power CAN module. It is divided into: self-vehicle lane change behavior estimation, self-vehicle driving scene evaluation and self-vehicle longitudinal speed estimation. Self-vehicle lane change behavior estimation, according to the self-vehicle turn signal, steering wheel angle and yaw angular velocity information, the self-vehicle lane change behavior is judged. Self-vehicle driving scene evaluation, according to the self-vehicle steering wheel angle and yaw angular velocity information, the self-vehicle driving scene is evaluated. Self-vehicle longitudinal speed estimation, according to the self-vehicle accelerator pedal opening and brake pedal opening and other information, the self-vehicle longitudinal speed and other information are comprehensively judged.

[0051] S103: Optimizing the driving trajectory of the vehicle according to the vehicle state.

[0052] In one embodiment of the present application, the driving trajectory of the self-vehicle is optimized according to the state of the self-vehicle, including: optimizing the original trajectory of the self-vehicle according to the state of the self-vehicle, wherein, when the lane line is credible, the original trajectory of the self-vehicle is the center line of the driving lane, and when the lane line is unavailable, the original trajectory of the self-vehicle is the driving trajectory fitted according to the yaw angular velocity.

[0053] like Figure 2 As shown, it can be realized by a comprehensive screening output module, which includes a self-vehicle trajectory optimization module, wherein the self-vehicle trajectory optimization module optimizes the original trajectory of the self-vehicle according to the self-vehicle state information output by the self-vehicle state evaluation unit. When the lane line is credible, the original trajectory of the self-vehicle is the center line of the driving lane. When the lane line information is unavailable, the original trajectory of the self-vehicle is the driving trajectory fitted according to the yaw angular velocity.

[0054] S104: Optimizing the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and obtaining a final screening target according to the driving trajectory fence of the own vehicle.

[0055] In one embodiment of the present application, the driving trajectory fence of the own vehicle is optimized according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and the final screening target is obtained according to the driving trajectory fence of the own vehicle, including: optimizing the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle, wherein obstacles located in the driving trajectory fence are marked as dangerous preparatory targets, and objects marked as dangerous preparatory targets are arbitrated to output the final screening target.

[0056] In this example, objects marked as dangerous preparation targets are arbitrated and a final screening target is output, including: screening out obstacles with effective distances closest to the longitudinal position of the vehicle as the final screening target, and outputting the final screening target.

[0057] Combination Figure 2 As shown, the comprehensive screening output module driving fence optimization module optimizes the driving track fence of the vehicle according to the obstacle vehicle state output by the target intention analysis unit, marks the obstacle target located in the driving track fence as a dangerous preparatory target, and stores the target information in the dangerous target set. The objects stored in the dangerous target set are arbitrated by the screening result arbitration module, and finally, the final screening target is output.

[0058] The following is a detailed description of each sub-module in the target intention analysis module, the vehicle status evaluation module and the comprehensive screening output module.

[0059] Other car driver intention prediction module:

[0060] The prediction of the intention of the other vehicle driver mainly focuses on the probability that the adjacent lane obstacle invades the driving trajectory of the vehicle, that is, the possibility that the adjacent lane obstacle changes lanes into the vehicle's lane. Based on the lateral and longitudinal distances and lateral speeds of the obstacle in the SL coordinate system provided by the perceived obstacle, a target obstacle lane change probability judgment model is built, and the obstacle lane change probability is calculated based on the binary normal distribution probability density function.

[0061] The lane change prediction model considering the intention of the other driver is:

[0062]

[0063] Among them, ψ(t) and ψ(t-1) represent the predicted lane change probability at this moment and the predicted lane change probability at the previous moment, respectively; δ sRepresents the turning state of the obstacle vehicle, 0 means the turn signal is off, 1 means the turn signal is on; a e Represents the longitudinal acceleration value of the obstacle vehicle, v e Represents the longitudinal velocity value of the obstacle vehicle.

[0064] When the value of ψ(t) is greater than 0.618, the probability of lane change is 1; when the value of ψ(t) is less than 0.382, the probability of lane change is 0; when the value of ψ(t) is greater than 0.382 and less than 0.618, it is in the lane change preparation state;

[0065] Other vehicle driver intention assessment module:

[0066] Semantic analysis is performed by combining the lane change probability output by the other vehicle's driver intention prediction module with the turn signal information, brake light information, and double flash warning light information of the obstacle vehicle.

[0067] When the lane change probability is 1 and the left turn signal is on, the obstacle target changes lanes to the left; when the lane change probability is 1 and the right turn signal is on, the obstacle target changes lanes to the right; when the lane change probability is 0 and the brake light is on, the obstacle target is in the braking state; when the lane change probability is 0 and the double flash warning light is on, the obstacle target is in an emergency state.

[0068] Other car driver intention output module:

[0069] The results of the other vehicle driver intention assessment module are packaged into a specified data structure type and output to the downstream module.

[0070] The lane-changing behavior estimation module estimates the lane-changing state of the vehicle based on the vehicle's turn signal and steering wheel angle information. When the left turn signal is on and the steering wheel angle is greater than 50°, the vehicle is in the left lane-changing state; when the right turn signal is on and the steering wheel angle is less than -50°, the vehicle is in the right lane-changing state.

[0071] The ego vehicle scenario assessment is mainly based on the ego vehicle's ego vehicle speed, steering wheel angle, yaw rate output by the EBS and other information to evaluate the ego vehicle's driving scenario.

[0072] According to r = v r / Yaw_Rate gets the curvature radius of the current vehicle trajectory, where r represents the curvature radius; v r Represents the longitudinal velocity of the vehicle; Yaw_Rate represents the yaw rate of the vehicle;

[0073] When r>0, and the steering wheel angle is greater than -5 and less than 25, it is a left turn; when r<0, and the steering wheel angle is greater than -25 and less than 5, it is a right turn.

[0074] The longitudinal speed estimation of the vehicle is mainly based on the information of the accelerator pedal opening, brake pedal opening and so on, to compensate the longitudinal speed of the vehicle to output a more accurate speed value.

[0075] Ego vehicle trajectory optimization:

[0076] The vehicle is moving forward in the current lane and turns the steering lever to turn left. The vehicle trajectory before optimization is shown as the dashed line 3 in the figure, which is the center line of the lane. Figure 4 Shown as solid line.

[0077] The trajectory equation before optimization is:

[0078]

[0079] The trajectory equation after optimization is:

[0080]

[0081] In the formula, y represents the lateral position coordinate, x represents the longitudinal position coordinate, and the value of C0 is 0; C1 represents the angle between the vehicle head and the center line of the lane; C2 represents the curvature of the lane center line, and C3 represents the rate of change of the curvature of the lane center line.

[0082] In the formula steer represents the steering wheel angle, and v represents the vehicle speed.

[0083] Driving fence optimization:

[0084] The ego vehicle is driving forward in the current lane. A target vehicle on the left is turning right and cutting into the ego lane. The ego vehicle driving fence before optimization is shown as the 3 dotted line in the figure. It extends to both sides with the center line of the lane as the center. The length is 100m and the width is 1.875m.

[0085] After trajectory optimization, Figure 4 As shown by the solid line, the optimized fence includes the range before optimization and extends to the obstacle target.

[0086] like Figure 5 As shown, the extended triangle is an isosceles triangle with a waist length of 100m and an included angle of θ.

[0087] Among them, d lat Represents the lateral distance of the obstacle target; v lat Represents the lateral speed of the obstacle target;

[0088] Screening results arbitration:

[0089] The objects stored in the dangerous target set are arbitrated through the screening result arbitration module, and the effective ones closest to the longitudinal position of the vehicle are screened out as the final target output.

[0090] According to the target screening method based on the driving intention of the other vehicle in the embodiment of the present application, obstacle driving information is obtained according to the road perception data, and the driving intention state of the other vehicle is determined according to the obstacle driving information, and the state of the own vehicle is evaluated according to the driving information of the own vehicle, and the driving trajectory of the own vehicle is optimized according to the state of the own vehicle. Finally, according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and according to the driving trajectory fence of the own vehicle, the final screening target is obtained, which can more accurately and efficiently screen target obstacles while ensuring safety, and effectively improve driving comfort.

[0091] Figure 6 is a structural block diagram of a target screening system based on the driving intention of another vehicle according to an embodiment of the present application. Figure 6 As shown, according to an embodiment of the present application, a target screening system based on the driving intention of another vehicle includes: a target intention analysis module 610, a vehicle state evaluation module 620 and a comprehensive screening module 630, wherein:

[0092] The target intention analysis module 610 is used to obtain obstacle driving information based on the road perception data, and determine the driving intention state of the other vehicle based on the obstacle driving information;

[0093] The vehicle state evaluation module 620 is used to evaluate the vehicle state according to the driving information of the vehicle;

[0094] The comprehensive screening module 630 is used to optimize the driving trajectory of the own vehicle according to the state of the own vehicle, and optimize the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and obtain the final screening target according to the driving trajectory fence of the own vehicle.

[0095] According to the target screening method based on the driving intention of the other vehicle in the embodiment of the present application, obstacle driving information is obtained according to the road perception data, and the driving intention state of the other vehicle is determined according to the obstacle driving information, and the state of the own vehicle is evaluated according to the driving information of the own vehicle, and the driving trajectory of the own vehicle is optimized according to the state of the own vehicle. Finally, according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and according to the driving trajectory fence of the own vehicle, the final screening target is obtained, which can more accurately and efficiently screen target obstacles while ensuring safety, and effectively improve driving comfort.

[0096] For the specific limitations of the target screening system based on the driving intention of another vehicle, please refer to the limitations of the target screening method based on the driving intention of another vehicle mentioned above, which will not be repeated here. The various modules of the above-mentioned target screening system based on the driving intention of another vehicle can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0097] In one embodiment, a vehicle is provided, comprising: a target screening system based on the driving intention of another vehicle according to any of the above embodiments. The vehicle obtains obstacle driving information based on road perception data, determines the driving intention state of another vehicle based on the obstacle driving information, evaluates the state of the own vehicle based on the driving information of the own vehicle, optimizes the driving trajectory of the own vehicle based on the state of the own vehicle, and finally optimizes the driving trajectory fence of the own vehicle based on the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and obtains the final screening target based on the driving trajectory fence of the own vehicle, which can screen target obstacles more accurately and efficiently while ensuring safety, and effectively improve driving comfort.

[0098] In addition, other structures and functions of the vehicle according to the embodiment of the present application are known to ordinary technicians in the field and will not be elaborated here.

[0099] In one embodiment, a computer device is provided. Figure 7 This is a block diagram of the computer device provided in the embodiment of the present application, refer to Figure 7 The computer device includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the target screening method embodiment based on the driving intention of another vehicle is implemented. For example, the following is performed: Obtaining obstacle driving information based on road perception data, and determining the driving intention state of another vehicle based on the obstacle driving information;

[0100] According to the driving information of the vehicle, the vehicle status is evaluated;

[0101] Optimizing the driving trajectory of the vehicle according to the vehicle state;

[0102] According to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and the final screening target is obtained according to the driving trajectory fence of the own vehicle.

[0103] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the processor implements the aforementioned target screening method embodiment based on the driving intention of another vehicle when executing the computer program. For example, the following is performed: Obtain obstacle driving information based on road perception data, and determine the driving intention state of another vehicle based on the obstacle driving information;

[0104] According to the driving information of the vehicle, the vehicle status is evaluated;

[0105] Optimizing the driving trajectory of the vehicle according to the vehicle state;

[0106] According to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and the final screening target is obtained according to the driving trajectory fence of the own vehicle.

[0107] The present application embodiment provides a computer program product, which includes instructions. When the instructions are executed, the method described in the embodiment of the present application is executed. For example, it can be executed Figure 1 The various steps of the target screening method based on the driving intention of the other vehicle shown are, for example, performed: obtaining obstacle driving information according to the road perception data, and determining the driving intention state of the other vehicle according to the obstacle driving information;

[0108] According to the driving information of the vehicle, the vehicle status is evaluated;

[0109] Optimizing the driving trajectory of the vehicle according to the vehicle state;

[0110] According to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and the final screening target is obtained according to the driving trajectory fence of the own vehicle.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the methods for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0112] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A target screening method based on other vehicle driving intention, characterized in that: include: Obtain obstacle driving information based on road perception data, and determine the driving intention state of other vehicles based on the obstacle driving information; According to the driving information of the vehicle, the vehicle status is evaluated; Optimizing the driving trajectory of the vehicle according to the vehicle state; According to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, the driving trajectory fence of the own vehicle is optimized, and the final screening target is obtained according to the driving trajectory fence of the own vehicle.

2. The target screening method based on the driving intention of other vehicles according to claim 1 is characterized in that: Obtaining obstacle driving information based on the road perception data, and determining the driving intention state of the other vehicle based on the obstacle driving information, includes: According to the obstacle driving information, predict the lane changing / keeping probability of the other vehicle; Combined with the obstacle driving information, the lane change / keeping probability is arbitrated to obtain the driving intention of the other vehicle; The driving intention of the other vehicle is packaged in a predetermined data structure to obtain the driving intention state of the other vehicle.

3. The target screening method based on the driving intention of other vehicles according to claim 1 is characterized in that: The step of evaluating the vehicle state according to the vehicle driving information includes: Evaluate the lane-changing behavior of the ego vehicle based on its turn signal, steering wheel angle, and yaw rate; Evaluate the driving scenario of the vehicle based on the steering wheel angle and yaw rate of the vehicle; The longitudinal speed of the vehicle is evaluated based on the accelerator pedal opening and brake pedal opening of the vehicle.

4. The target screening method based on the driving intention of other vehicles according to claim 1 is characterized in that: According to the state of the vehicle, the driving trajectory of the vehicle is optimized, including: The original trajectory of the ego vehicle is optimized according to the state of the ego vehicle. When the lane line is credible, the original trajectory of the ego vehicle is the center line of the driving lane. When the lane line is unavailable, the original trajectory of the ego vehicle is the driving trajectory fitted according to the yaw angular velocity.

5. The target screening method based on the driving intention of another vehicle according to any one of claims 1 to 4, characterized in that: The method of optimizing the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and obtaining the final screening target according to the driving trajectory fence of the own vehicle, includes: According to the driving intention state of the other vehicle, the driving trajectory fence of the own vehicle is optimized, wherein obstacles located within the driving trajectory fence are marked as dangerous preparation targets, and objects marked as dangerous preparation targets are arbitrated to output a final screening target.

6. The target screening method based on the driving intention of other vehicles according to claim 5 is characterized in that: The objects marked as dangerous preparation targets are arbitrated and the final screening targets are output, including: The obstacles whose effective distance is closest to the longitudinal position of the vehicle are selected as final selection targets, and the final selection targets are output.

7. A target screening system based on other vehicle driving intention, characterized in that: include: The target intention analysis module is used to obtain obstacle driving information based on the road perception data, and determine the driving intention state of other vehicles based on the obstacle driving information; The vehicle state evaluation module is used to evaluate the vehicle state according to the vehicle driving information; The comprehensive screening module is used to optimize the driving trajectory of the own vehicle according to the state of the own vehicle, and to optimize the driving trajectory fence of the own vehicle according to the driving intention state of the other vehicle and the optimized driving trajectory of the own vehicle, and to obtain the final screening target according to the driving trajectory fence of the own vehicle.

8. A vehicle, characterized in that: include: The target screening system based on the driving intention of another vehicle according to claim 7.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the target screening method based on the driving intention of another vehicle according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the target screening method based on the driving intention of another vehicle according to any one of claims 1 to 6 is implemented.

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