A vehicle driving track planning method, system, device and storage medium

Abstract

The application discloses a vehicle driving track planning method, system, device and storage medium, and relates to the technical field of automatic driving. The vehicle driving track planning method comprises the following steps: establishing a cost function based on the track smoothness, the interval obstacle distance and the interval reference line distance of a candidate driving track of a host vehicle according to the predicted track of surrounding vehicles and the candidate driving track of the host vehicle; and evaluating each candidate driving track of the host vehicle based on the cost function according to the track smoothness, the interval obstacle distance and the interval reference line distance of each candidate driving track of the host vehicle, so as to determine a planned driving track of the host vehicle. The application solves the problem that the influence judgment accuracy for non-straight driving obstacles is not enough when the influence of the interval distance from obstacles is considered when the cost function of the candidate driving track of the vehicle is constructed.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, specifically to a vehicle trajectory planning method, system, device, and storage medium. Background Technology

[0002] Intelligent driving vehicle trajectory planning acquires surrounding driving environment information through sensors. Considering factors such as the vehicle's location, road information, traffic conditions, obstacles, and dynamic constraints, it plans a safe, reliable, and comfortable driving trajectory based on the vehicle's current position and state. As a key technology for intelligent driving, the result of trajectory planning directly affects the vehicle's path, playing a crucial role in driving safety and comfort. Currently, classic trajectory planning algorithms mainly include sampling-based and optimization-based algorithms. Sampling-based algorithms generate a cluster of candidate trajectories by sampling extensively in space, and then select the optimal candidate trajectory as the planned trajectory based on a preset cost function. Optimization-based trajectory algorithms generate a coarse path by randomly sampling on the road, and then select the optimal path that meets the road constraints as the planned trajectory through optimization.

[0003] In existing technologies, when constructing the cost function for candidate vehicle trajectories, the accuracy of judging the impact of non-straight-line obstacles is insufficient when considering the influence of distance from obstacles. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a vehicle trajectory planning method, system, device, and storage medium that can solve the problem that the accuracy of judging the impact of non-straight-line obstacles is insufficient when considering the influence of distance with obstacles in the cost function of constructing the candidate vehicle trajectory.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] Firstly, this solution provides a vehicle trajectory planning method, including:

[0007] Based on the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle, a cost function is established for the trajectory smoothness, distance from obstacles, and distance from reference lines based on the candidate driving trajectory of the main vehicle.

[0008] Based on the trajectory smoothness, distance to obstacles, and distance to reference lines of each candidate driving trajectory of the master vehicle, the candidate driving trajectory of each master vehicle is evaluated based on the cost function to determine the planned driving trajectory of the master vehicle.

[0009] In some alternative schemes, the cost function of the candidate driving trajectory of the main vehicle is:

[0010]

[0011] Among them, F cost (s) is the cost function for the candidate driving trajectory of the main vehicle, s is the candidate driving trajectory of the main vehicle, F smooth (s i F is the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle. obj (s i F is the distance cost function for the interval obstacles in the i-th segment of the candidate vehicle's driving trajectory. ref (s i ) is the distance cost function of the interval reference line of the i-th segment of the candidate driving trajectory of the main vehicle, s i Let N be the i-th segment of the candidate driving trajectory for the main vehicle, and N be the number of segments of the candidate driving trajectory for the main vehicle.

[0012] In some alternative schemes, the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle is:

[0013]

[0014] Among them, Fs mooth (s i ) is the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle, f′(s) i,j ) is the first derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, f″(s) i,j ) is the second derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, f″′(s i,j ) is the third derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, s i The i-th segment of the candidate driving trajectory of the main vehicle, s i,j M is the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle. i c1 is the number of sampling points in the i-th segment of the candidate driving trajectory of the main vehicle, c2 is the weight coefficient of the arithmetic sum of the absolute values ​​of the first derivative, c3 is the weight coefficient of the arithmetic sum of the absolute values ​​of the second derivative, and c3 is the weight coefficient of the arithmetic sum of the absolute values ​​of the third derivative.

[0015] In some alternative schemes, the obstacle distance cost function for the i-th segment of the candidate driving trajectory of the main vehicle is:

[0016] Among them, F obj (s i F is the distance cost function for the interval obstacles in the i-th segment of the candidate driving trajectory of the main vehicle. obj (s i,j) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the sampling points of the predicted trajectories of surrounding vehicles, s i The i-th segment of the candidate driving trajectory of the main vehicle, s i,j M is the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle. i The number of sampling points in the i-th segment of the candidate driving trajectory of the main vehicle;

[0017] The obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the sampling points of the predicted trajectories of surrounding vehicles is:

[0018] Where, f(s) i,j ,s′ k ) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate vehicle's trajectory and the k-th sampling point in the predicted trajectories of surrounding vehicles, s′ k The k-th sampling point is the predicted trajectory of surrounding vehicles, where K is the number of sampling points for the predicted vehicle trajectory.

[0019] In some alternative schemes, the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the k-th sampling point in the predicted trajectory of surrounding vehicles is:

[0020]

[0021] Where, f(s) i,j ,s′ k ) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate vehicle's trajectory and the k-th sampling point in the predicted trajectories of surrounding vehicles, s i,j The j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, s′ k d is the kth sampling point of the predicted trajectory of surrounding vehicles, d is the distance between the jth sampling point in the i-th segment of the candidate trajectory of the main vehicle and the kth sampling point of the predicted trajectory of surrounding vehicles, r0 is the minimum tolerance collision distance, r1 is the safe zone radius of the candidate trajectory of the main vehicle, r2 is the safe zone radius of the predicted trajectory of surrounding vehicles, and c4 is the collision coefficient.

[0022] In some alternative schemes, the distance cost function of the interval reference line for the i-th segment of the candidate driving trajectory of the main vehicle is:

[0023] Among them, F ref (s i ) is the distance cost function of the interval reference line of the i-th segment of the candidate driving trajectory of the main vehicle, s i The i-th segment of the candidate driving trajectory of the main vehicle, l i,j M is the distance M between the reference line intervals of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle.i The number of sampling points in the i-th segment of the candidate driving trajectory of the main vehicle.

[0024] In some alternative schemes, before establishing the cost function for each candidate driving trajectory of the master vehicle based on the influence of trajectory smoothness, obstacle distance, and reference line distance on the vehicle's driving trajectory, the following steps are also included:

[0025] The system acquires information about the main vehicle's status, the status of surrounding vehicles, and the road environment through the sensing unit.

[0026] Based on the status information of surrounding vehicles and road environment information, the behavioral intentions of surrounding vehicles are identified, and predicted trajectories of surrounding vehicles are generated.

[0027] Based on the vehicle status information and road environment information, candidate driving trajectories for the main vehicle are generated.

[0028] Secondly, this solution also provides a vehicle trajectory planning device, including:

[0029] The cost function establishment module is used to establish a cost function based on the trajectory smoothness, distance to obstacles, and distance to reference lines of the candidate driving trajectory of the main vehicle, according to the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle.

[0030] The driving trajectory determination module is used to evaluate each candidate driving trajectory of the master vehicle based on the trajectory smoothness, distance to obstacles, and distance to reference lines, and to determine the planned driving trajectory of the master vehicle.

[0031] Thirdly, this solution also provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the steps of any of the above-described vehicle trajectory planning methods.

[0032] Fourthly, this solution also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the vehicle trajectory planning method described above.

[0033] Compared with existing technologies, the advantages of this invention are as follows: This solution establishes a cost function based on the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle, considering the trajectory smoothness, distance from obstacles, and distance from reference lines of the candidate driving trajectory of the main vehicle; based on the trajectory smoothness, distance from obstacles, and distance from reference lines of each candidate driving trajectory of the main vehicle, the cost function is used to evaluate each candidate driving trajectory of the main vehicle and determine the planned driving trajectory of the main vehicle. This solves the problem in existing technologies where, when constructing the cost function for candidate driving trajectories, the accuracy of judging the impact of non-straight-line obstacles is insufficient when considering the influence of distance from obstacles. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the vehicle trajectory planning method in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram illustrating the composition of the candidate driving trajectory of the main vehicle in an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram illustrating the relative positional relationship between the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle in an embodiment of the present invention.

[0038] Figure 4 This is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] Firstly, such as Figure 1 As shown, the present invention provides a vehicle trajectory planning method, comprising the following steps:

[0042] S01: The main vehicle status information, surrounding vehicle status information and road environment information are obtained through the sensing unit.

[0043] S02: Based on the status information of surrounding vehicles and road environment information, identify the behavioral intentions of surrounding vehicles and generate predicted trajectories of surrounding vehicles.

[0044] S03: Generate candidate driving trajectories for the main vehicle based on the main vehicle status information and road environment information.

[0045] S1: Based on the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle, establish a cost function for trajectory smoothness, distance from obstacles, and distance from reference lines based on the candidate driving trajectory of the main vehicle.

[0046] S2: Based on the trajectory smoothness, distance to obstacles, and distance to reference lines of each candidate driving trajectory of the master vehicle, evaluate each candidate driving trajectory of the master vehicle based on the cost function, and determine the planned driving trajectory of the master vehicle.

[0047] In this embodiment, the sensing unit includes devices that acquire information using sensing technology, such as cameras, lidar, and millimeter-wave radar. The main vehicle status information includes the main vehicle's position, speed, heading angle, and yaw angle. The surrounding vehicle status information includes the positions, speeds, and heading angles of surrounding vehicles. The road environment information includes road lane markings and obstacle information.

[0048] In this embodiment, any candidate driving trajectory of the main vehicle consists of multiple segments, each segment including multiple sampling points. For example... Figure 2 The diagram shows the composition of a candidate driving trajectory for a primary vehicle. In the Frenet coordinate system, this candidate driving trajectory consists of three segments: the first segment has M1 sampling points, the second segment has M2 sampling points, and the third segment has M3 sampling points. For the j-th sampling point in the i-th segment of the candidate driving trajectory, its coordinates are represented as (s...). i,j ,l i,j ), where s i,j For the vertical axis, l i,j These are lateral coordinates. Figure 2 The coordinates of each sampling point are shown in the table. The obstacle distance is the distance between the main vehicle candidate trajectory sampling point and the surrounding vehicle predicted trajectory sampling points. The reference line in the reference line distance is the reference line of the Frenet coordinate system, i.e., the center line of the road. The reference line distance is the distance between the main vehicle candidate trajectory sampling point and the center line of the road.

[0049] Based on the trajectory smoothness, distance to obstacles, and distance to reference lines of each candidate driving trajectory of the master vehicle, the candidate driving trajectory of the master vehicle with the smallest cost function value is selected as the planned driving trajectory of the master vehicle.

[0050] In some optional embodiments, the cost function of the candidate driving trajectory of the main vehicle is:

[0051]

[0052] Among them, F cost (s) is the cost function for the candidate driving trajectory of the main vehicle, s is the candidate driving trajectory of the main vehicle, F smooth (s i F is the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle. obj (s i F is the distance cost function for the interval obstacles in the i-th segment of the candidate driving trajectory of the main vehicle. ref (s i ) is the distance cost function of the interval reference line of the i-th segment of the candidate driving trajectory of the main vehicle, s i Let N be the i-th segment of the candidate driving trajectory for the main vehicle, and N be the number of segments of the candidate driving trajectory for the main vehicle.

[0053] In this embodiment, the value of i ranges from 1 to N.

[0054] In some optional embodiments, the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle is:

[0055]

[0056] Among them, F smooth (s i ) is the trajectory smoothness cost function for the i-th segment of the candidate driving trajectory of the main vehicle, f′(s) i,j ) is the first derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, f″(s) i,j ) is the second derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, f″′(s i,j ) is the third derivative of the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, s i The i-th segment of the candidate driving trajectory of the main vehicle, s i,j M is the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle. i c1 is the number of sampling points in the i-th segment of the candidate driving trajectory of the main vehicle, c2 is the weight coefficient of the arithmetic sum of the absolute values ​​of the first derivative, c3 is the weight coefficient of the arithmetic sum of the absolute values ​​of the second derivative, and c3 is the weight coefficient of the arithmetic sum of the absolute values ​​of the third derivative.

[0057] In this embodiment, the value of j ranges from 1 to M. i The values ​​of c1, c2, and c3 were all obtained through actual vehicle testing and calibration.

[0058] In some optional embodiments, the obstacle distance cost function for the i-th segment of the candidate driving trajectory of the main vehicle is:

[0059] Among them, F obj (s i F is the distance cost function for the interval obstacles in the i-th segment of the candidate driving trajectory of the main vehicle. obj (s i,j ) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the sampling points of the predicted trajectories of surrounding vehicles, s i The i-th segment of the candidate driving trajectory of the main vehicle, s i,j M is the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle. i The number of sampling points in the i-th segment of the candidate driving trajectory of the main vehicle;

[0060] The obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the sampling points of the predicted trajectories of surrounding vehicles is:

[0061] Where, f(s) i,j ,s′ k ) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate vehicle's trajectory and the k-th sampling point in the predicted trajectories of surrounding vehicles, s′ k The k-th sampling point is the predicted trajectory of surrounding vehicles, where K is the number of sampling points for the predicted vehicle trajectory.

[0062] In this embodiment, the value of j ranges from 1 to M. i .

[0063] In some optional embodiments, the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle and the k-th sampling point in the predicted trajectory of surrounding vehicles is:

[0064]

[0065] Where, f(s) i,j ,s′ k ) is the obstacle distance cost function between the j-th sampling point in the i-th segment of the candidate vehicle's trajectory and the k-th sampling point in the predicted trajectories of surrounding vehicles, s i,j The j-th sampling point in the i-th segment of the candidate driving trajectory of the main vehicle, s′ kThe k-th sampling point of the predicted trajectory for the surrounding vehicle, d is the distance between the j-th sampling point in the i-th segment of the candidate driving trajectory of the host vehicle and the k-th sampling point of the predicted trajectory of the surrounding vehicle, r0 is the minimum tolerable collision distance, r1 is the safety area radius of the candidate driving trajectory of the host vehicle, r2 is the safety area radius of the predicted trajectory of the surrounding vehicle, and c4 is the collision coefficient.

[0066] In this embodiment, the value range of j is from 1 to M i . The numerical calculation of the cost function of the obstacle distance between the j-th sampling point in the i-th segment of the candidate driving trajectory of the host vehicle and the k-th sampling point of the predicted trajectory of the surrounding vehicle is related to the relative positions of the sampling points (s i,j , l i,j ) in the candidate driving trajectory of the host vehicle and the sampling points (s' k , l' k ) in the predicted trajectory of the surrounding vehicle.

[0067] As Figure 3 shown is a schematic diagram of the relative position relationship between the predicted trajectory of the surrounding vehicle and the candidate driving trajectory of the host vehicle. Figure 3 In it, the surrounding vehicle is performing an operation of changing lanes and cutting into the main lane, and its predicted trajectory is a smooth lane-changing curve. A certain candidate driving trajectory of the host vehicle is a straight line planned along the center line of the lane.

[0068] For any sampling point (s i,j , l i,j ) on the candidate trajectory of the host vehicle, draw a circle with a radius of r1 as the safety area of the candidate driving trajectory of the host vehicle. Similarly, for any sampling point (s' k , l' k ) on the predicted trajectory of the surrounding vehicle, draw a circle with a radius of r2 as the safety area of the predicted trajectory of the surrounding vehicle.

[0069] When the straight-line distance d between the sampling point (s i,j , l i,j ) of the candidate driving trajectory of the host vehicle and the sampling point (s' k , l' k ) of the predicted trajectory of the surrounding vehicle is greater than the sum of the radius values of these two points, it is determined that there is no collision risk between the two points, and f(s i,j , s' k ) = 0; when the straight-line distance d between the sampling point (s i,j , l i,j ) of the candidate driving trajectory of the host vehicle and the sampling point (s' k , l' k ) of the predicted trajectory of the surrounding vehicle is within the range of r0 < d < r1 + r2, it is determined that there is a certain collision risk between the two, and f(s i,j , s' k) = c4 * (d - r0) / (r1 + r2 - r0); When the sampling point (s i,j , l i,j ) of the candidate driving trajectory of the host vehicle and the sampling point (s' k , l' k ) of the predicted trajectory of the surrounding vehicles, when the straight-line distance d between the two points is in the range of 0 < d < r0, it is determined that the collision risk between the two is extremely high, and f(s i,j , s' k ) = +∞. Among them, the values of r0, r1, r2, and c4 are all calibrated through real vehicle test.

[0070] In some optional embodiments, the cost function of the interval reference line distance of the i-th segment of the candidate driving trajectory of the host vehicle is:

[0071] Among them, F ref (s i ) is the cost function of the interval reference line distance of the i-th segment of the candidate driving trajectory of the host vehicle, s i is the i-th segment of the candidate driving trajectory of the host vehicle, l i,j is the interval reference line distance of the j-th sampling point of the i-th segment of the candidate driving trajectory of the host vehicle, M i 为 is the number of sampling points in the i-th segment of the candidate driving trajectory of the host vehicle.

[0072] In this embodiment, the value range of j is from 1 to M i . The interval reference line distance of the j-th sampling point of the i-th segment of the candidate driving trajectory of the host vehicle is the lateral coordinate of the j-th sampling point (s i,j , l i,j ) of the i-th segment of the candidate driving trajectory of the host vehicle.

[0073] In summary, the present invention establishes a cost function based on the trajectory smoothness, the distance from the interval obstacle, and the distance from the interval reference line of the candidate driving trajectory of the host vehicle according to the predicted trajectory of the surrounding vehicles and the candidate driving trajectory of the host vehicle; based on the trajectory smoothness, the distance from the interval obstacle, and the distance from the interval reference line of each candidate driving trajectory of the host vehicle, evaluates each candidate driving trajectory of the host vehicle based on the cost function, and determines the planned driving trajectory of the host vehicle. It solves the problem that in the prior art, when constructing the cost function of the candidate driving trajectory of the vehicle and considering the influence of the distance from the obstacle, the judgment accuracy of the influence of the non-straight driving obstacle is insufficient.

[0074] The present invention plans the trajectory of the host vehicle by real-time obtaining the driving environment information and combining the behavior intention of the surrounding vehicles, improving the safety and comfort of the system.

[0075] In the second aspect, the present invention also provides a vehicle driving trajectory planning device, including:

[0076] The cost function establishment module is used to establish a cost function based on the trajectory smoothness, distance to obstacles, and distance to reference lines of the candidate driving trajectory of the main vehicle, according to the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle.

[0077] The driving trajectory determination module is used to evaluate each candidate driving trajectory of the master vehicle based on the trajectory smoothness, distance to obstacles, and distance to reference lines, and to determine the planned driving trajectory of the master vehicle.

[0078] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the above-described device and its modules and units can be referred to the corresponding processes in the foregoing embodiments, and will not be repeated here.

[0079] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 4 It runs on the computer device shown.

[0080] Please see Figure 4 , Figure 4 This is a schematic block diagram illustrating the structure of a computer device provided in an embodiment of this application. The computer device can be a terminal.

[0081] like Figure 4 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0082] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any vehicle trajectory planning method.

[0083] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0084] Internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When these computer programs are executed by the processor, the processor can execute any vehicle trajectory planning method.

[0085] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0086] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0087] In one embodiment, the processor is used to run a computer program stored in a memory to implement the steps of the vehicle trajectory planning method described above.

[0088] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to various embodiments of this application.

[0089] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0090] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0091] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for planning vehicle driving trajectory, characterized in that, include: Based on the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle, a cost function is established for the trajectory smoothness, distance from obstacles, and distance from reference lines based on the candidate driving trajectory of the main vehicle. Based on the trajectory smoothness, distance to obstacles, and distance to reference lines of each candidate driving trajectory of the master vehicle, the candidate driving trajectory of each master vehicle is evaluated based on the cost function to determine the planned driving trajectory of the master vehicle. The cost function for the candidate driving trajectory of the main vehicle is: ； in, Candidate driving trajectories for the main vehicle The candidate driving trajectory of the main vehicle The cost function for the trajectory smoothness of the segment. The candidate driving trajectory of the main vehicle The distance cost function of the segment's interval obstacle. The candidate driving trajectory of the main vehicle The segment interval reference line distance cost function, The first candidate driving trajectory of the main vehicle part, The number of segments of the candidate driving trajectory for the main vehicle; ； in, The candidate driving trajectory of the main vehicle Section 1 The obstacle distance cost function between each sampling point and the surrounding vehicle predicted trajectory sampling points. The candidate driving trajectory of the main vehicle Section 1 One sampling point, The candidate driving trajectory of the main vehicle Number of sampling points in the segment; ； in, The candidate driving trajectory of the main vehicle Section 1 The predicted trajectory of the sampling point and surrounding vehicles The obstacle distance cost function at intervals of each sampling point Predicting trajectories for surrounding vehicles One sampling point, The number of sampling points for the vehicle's predicted trajectory; ； in, The candidate driving trajectory of the main vehicle Section 1 The predicted trajectory of the sampling point and surrounding vehicles The distance between each sampling point Minimum tolerance collision distance, The radius of the safe zone for the candidate driving trajectory of the main vehicle. The radius of the safe zone for predicting the trajectories of surrounding vehicles. This represents the collision coefficient.

2. The vehicle trajectory planning method as described in claim 1, characterized in that, Main vehicle candidate driving trajectory The trajectory smoothness cost function of the segment is: ； in, The candidate driving trajectory of the main vehicle Section 1 The first derivative of each sampling point The candidate driving trajectory of the main vehicle Section 1 The second derivative of each sampling point The candidate driving trajectory of the main vehicle Section 1 The third derivative of each sampling point The weighting coefficients are the arithmetic sum of the absolute values ​​of the first derivatives. The weighting coefficients are the arithmetic sum of the absolute values ​​of the second derivatives. The weighting coefficients are the arithmetic sum of the absolute values ​​of the third derivatives.

3. The vehicle trajectory planning method as described in claim 1, characterized in that, Main vehicle candidate driving trajectory The distance cost function for the segment's interval reference line is: ; in, The candidate driving trajectory of the main vehicle Duan Di The distance between each sampling point is the reference line distance.

4. The vehicle trajectory planning method as described in claim 1, characterized in that, Before establishing the cost function for each candidate driving trajectory of the master vehicle based on the influence of trajectory smoothness, obstacle distance, and reference line distance on the vehicle's driving trajectory, the following is also included: The system acquires information about the main vehicle's status, the status of surrounding vehicles, and the road environment through the sensing unit. Based on the status information of surrounding vehicles and road environment information, the behavioral intentions of surrounding vehicles are identified, and predicted trajectories of surrounding vehicles are generated. Based on the vehicle status information and road environment information, candidate driving trajectories for the main vehicle are generated.

5. A vehicle trajectory planning device, employing the vehicle trajectory planning method as described in any one of claims 1-4, characterized in that, include: The cost function establishment module is used to establish a cost function based on the trajectory smoothness, distance to obstacles, and distance to reference lines of the candidate driving trajectory of the main vehicle, according to the predicted trajectories of surrounding vehicles and the candidate driving trajectory of the main vehicle. The driving trajectory determination module is used to evaluate each candidate driving trajectory of the master vehicle based on the trajectory smoothness, distance to obstacles and distance to reference lines, and determine the planned driving trajectory of the master vehicle based on the cost function.

6. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the steps of the vehicle trajectory planning method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the vehicle trajectory planning method as described in any one of claims 1 to 4.

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