Trajectory planning method, device and equipment for autonomous vehicle and storage medium

Abstract

The invention discloses a trajectory planning method, device and equipment for an autonomous vehicle, and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring driving state information and planning path information of an automatic driving vehicle, and driving state information of a dynamic obstacle; wherein the autonomous vehicle has a steering intention; determining a risk dynamic obstacle from the dynamic obstacles by using a risk dynamic obstacle identification strategy based on the planned path information of the autonomous vehicle and the driving state information of the dynamic obstacles; based on the planned path information of the autonomous vehicle and the driving state information of the risk dynamic obstacle, a steering collision boundary is obtained by using a steering collision boundary identification strategy; and performing trajectory planning processing on the autonomous vehicle based on the steering collision boundary, the driving state information of the autonomous vehicle, the planning path information and the driving state information of the risk dynamic obstacle to obtain a planned trajectory of the autonomous vehicle.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to technical fields such as intelligent transportation and autonomous driving, and in particular to a trajectory planning method, device, equipment and storage medium for an autonomous driving vehicle. Background Art

[0002] In the delivery scenario of autonomous vehicles, when turning at an intersection, the vehicle may have a collision with other turning vehicles due to the inner wheel difference phenomenon. Therefore, when planning the driving trajectory of the autonomous vehicle turning at the intersection, it is necessary to consider the impact of the inner wheel difference of other turning dynamic obstacles.

[0003] At present, most of the related technical methods analyze how to avoid scratches from the perspective of ordinary vehicles with drivers, but fail to provide effective solutions for autonomous driving vehicles encountering such risk scenarios. Summary of the invention

[0004] The present application provides a trajectory planning method, device, equipment and storage medium for an autonomous driving vehicle, which realizes risk assessment of steering of an autonomous driving vehicle and improves the safety and reliability of steering of an autonomous driving vehicle. The technical solution is as follows:

[0005] In a first aspect, a trajectory planning method for an autonomous driving vehicle is provided, the method comprising:

[0006] Acquiring driving state information, planned path information, and driving state information of dynamic obstacles of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention;

[0007] Based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacles, a risky dynamic obstacle is determined from the dynamic obstacles using a risky dynamic obstacle identification strategy;

[0008] Based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary is obtained by using a steering collision boundary identification strategy;

[0009] Based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle, the autonomous driving vehicle is subjected to trajectory planning processing to obtain the planned trajectory of the autonomous driving vehicle.

[0010] In a possible implementation, the risk dynamic obstacle identification strategy includes a steering risk area identification algorithm and a risk dynamic obstacle identification algorithm, and the risk dynamic obstacle identification strategy is used based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle to determine the risk dynamic obstacle from the dynamic obstacles, including:

[0011] Based on the planned path information of the autonomous driving vehicle, a turning risk area is obtained by using a turning risk area identification algorithm;

[0012] Based on the driving state information of the dynamic obstacle and the turning risk area, a risky dynamic obstacle is determined from the dynamic obstacles using a risky dynamic obstacle identification algorithm.

[0013] In a possible implementation, based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary recognition strategy is used to obtain a steering collision boundary, including:

[0014] In response to the driving state information of the risky dynamic obstacle including the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, a rear wheel driving trajectory of the risky dynamic obstacle is determined by using a preset Ackerman steering model;

[0015] The steering collision boundary is obtained based on the rear wheel driving trajectory of the risk dynamic obstacle.

[0016] In a possible implementation, based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary recognition strategy is used to obtain a steering collision boundary, including:

[0017] In response to the driving state information of the risky dynamic obstacle not including the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, using a preset decomposition algorithm, decomposing the risky dynamic obstacle to obtain a decomposition unit corresponding to the front of the risky dynamic obstacle;

[0018] Based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front portion, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle;

[0019] Based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risk dynamic obstacle, a lateral offset is calculated using a preset offset algorithm;

[0020] Based on the lateral offset and the front wheel driving trajectory, obtaining the rear wheel driving trajectory of the risk dynamic obstacle;

[0021] The steering collision boundary is obtained based on the rear wheel driving trajectory of the risk dynamic obstacle.

[0022] In a possible implementation, the driving state information of the risky dynamic obstacle includes shape information of the risky dynamic obstacle. Based on the driving state information of the risky dynamic obstacle, a preset decomposition algorithm is used to decompose the risky dynamic obstacle to obtain a decomposition unit corresponding to the front of the risky dynamic obstacle, including:

[0023] Based on the shape information of the risk dynamic obstacle, a preset decomposition algorithm is used to calculate the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first distance between two adjacent decomposition units;

[0024] Based on the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first distance between two adjacent decomposition units, the risk dynamic obstacle is decomposed to obtain a plurality of decomposition units corresponding to the risk dynamic obstacle;

[0025] The decomposition unit corresponding to the front of the risky dynamic obstacle is obtained from the multiple decomposition units corresponding to the risky dynamic obstacle.

[0026] In a possible implementation, the inferring the front wheel driving trajectory of the risk dynamic obstacle based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front portion to obtain the front wheel driving trajectory of the risk dynamic obstacle includes:

[0027] Obtain each position point and the orientation angle corresponding to each position point in the planned path information of the autonomous driving vehicle;

[0028] Based on each position point and the decomposition unit corresponding to the front portion, a matching position point matching the decomposition unit corresponding to the front portion is obtained by using a binary search algorithm;

[0029] Obtaining a second distance between the decomposition unit corresponding to the front portion and the matching position point;

[0030] Based on the orientation angle corresponding to each position point, a normal vector corresponding to each position point is determined using a preset vector algorithm;

[0031] Based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second distance, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle.

[0032] In a possible implementation, the lateral offset is calculated based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risk dynamic obstacle using a preset offset algorithm, including:

[0033] Obtaining a curvature value of each position point in the planned path information of the autonomous driving vehicle;

[0034] Determine the curvature value of each track point in the front wheel driving track of the risk dynamic obstacle based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing;

[0035] Get the first distance between two adjacent decomposition units;

[0036] The lateral offset is calculated based on the curvature value of each trajectory point and the first spacing.

[0037] In a possible implementation, the driving state information of the autonomous driving vehicle includes shape information, speed information, acceleration information, and jerk information of the autonomous driving vehicle, and based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risk dynamic obstacle, performing trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle includes:

[0038] Based on the shape information, the planned path information and the driving state information of the risky dynamic obstacle of the autonomous driving vehicle, obtaining the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle;

[0039] Based on the speed information, the planned path information, and the steering collision boundary of the autonomous driving vehicle, obtaining a first time when the autonomous driving vehicle reaches the steering collision boundary;

[0040] Based on the driving state information of the risky dynamic obstacle and the turning collision boundary, obtaining a second time when the risky dynamic obstacle reaches the turning collision boundary;

[0041] In response to the position relationship information satisfying a preset first constraint condition, or the speed information, the first time and the second time of the autonomous driving vehicle satisfying a preset second constraint condition, based on the speed information, acceleration information and jerk information of the autonomous driving vehicle, a preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain a planned trajectory of the autonomous driving vehicle.

[0042] In a second aspect, a trajectory planning device for an autonomous driving vehicle is provided, the device comprising:

[0043] An acquisition unit, used to acquire driving state information, planned path information, and driving state information of a dynamic obstacle of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention;

[0044] a determination unit, configured to determine a risky dynamic obstacle from the dynamic obstacles by using a risky dynamic obstacle identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle;

[0045] An obtaining unit, configured to obtain a steering collision boundary by using a steering collision boundary identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the risk dynamic obstacle;

[0046] A planning unit is used to perform trajectory planning processing on the autonomous driving vehicle based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle to obtain the planned trajectory of the autonomous driving vehicle.

[0047] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0048] In a fourth aspect, an electronic device is provided, including:

[0049] at least one processor; and

[0050] a memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0052] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0053] In a sixth aspect, an autonomous driving vehicle is provided, comprising the electronic device as described above.

[0054] The beneficial effects of the technical solution provided by this application include at least:

[0055] It can be seen from the above technical scheme that the embodiment of the present application can obtain the driving state information, planned path information, and driving state information of the dynamic obstacle of the autonomous driving vehicle, and the autonomous driving vehicle has a turning intention, and then based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle, a risky dynamic obstacle identification strategy can be used to determine the risky dynamic obstacle from the dynamic obstacles, and based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary identification strategy can be used to obtain the steering collision boundary, so that the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the dynamic obstacle can be used to identify the risky dynamic obstacle from the dynamic obstacles. The driving state information of the risk dynamic obstacle is used to perform trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle. Since the steering collision boundary between the autonomous driving vehicle and the dynamic obstacle can be effectively estimated based on the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the dynamic obstacle, the steering collision boundary is the boundary that poses a danger to the turning of the vehicle when the dynamic obstacle turns, thereby realizing risk estimation of the steering scenario of the autonomous driving vehicle, and based on the steering collision boundary, the driving trajectory of the autonomous driving vehicle during the turning process is planned, which can effectively reduce the risk of scratches, thereby improving the safety and reliability of the steering of the autonomous driving vehicle.

[0056] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 It is a flowchart of a trajectory planning method for an autonomous driving vehicle provided by an embodiment of the present application;

[0059] Figure 2 is a flow chart of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0060] Figure 3 is a schematic diagram of a turning risk area of ​​a trajectory planning method for an autonomous driving vehicle provided in another embodiment of the present application;

[0061] Figure 4is a schematic diagram of risk dynamic obstacle steering of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0062] Figure 5 is a schematic diagram of another risk dynamic obstacle steering of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0063] Figure 6 is a schematic diagram of multiple decomposition units of risk dynamic obstacles of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0064] Figure 7 is a schematic diagram of a front wheel driving trajectory of a risk dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0065] Figure 8 is a schematic diagram of a rear wheel driving trajectory of a risk dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0066] Figures 9a to 9c is a schematic diagram of the position relationship between an autonomous driving vehicle and a risky dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application;

[0067] Fig.10 is a structural block diagram of a trajectory planning device for an autonomous driving vehicle provided in yet another embodiment of the present application;

[0068] Fig.11 It is a block diagram of an electronic device used to implement the trajectory planning method of the autonomous driving vehicle in the embodiment of the present application. DETAILED DESCRIPTION

[0069] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0070] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0071] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0072] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0073] Inner wheel difference usually refers to the phenomenon that the rear wheels are closer to the center of rotation than the front wheels when the vehicle is turning. The greater the steering wheel turning angle and the longer the body of the vehicle, the more obvious the inner wheel difference. Specifically, the inner wheel difference is mainly due to the different driving paths of the front and rear wheels when the vehicle turns. Since the front wheels are usually responsible for steering, and the rear wheels follow the trajectory of the front wheels. The front wheels can change the driving direction according to the steering angle of the steering wheel, and the rear wheels cannot steer and can only follow the inclination of the body, which causes the rear wheels to have a wider driving path than the front wheels during the turn. The area formed by the inner wheel difference phenomenon is usually a blind spot in the driver's field of view, which is difficult to observe directly through the front windshield or side mirrors. This is also the reason why autonomous driving vehicles are often scratched in unmanned delivery scenarios (for example, park intersections, etc.).

[0074] Currently, most of the related technical methods analyze how to avoid scratches from the perspective of human truck drivers, but fail to provide effective solutions for autonomous vehicles encountering such risk scenarios.

[0075] Please refer to Figure 1 , which shows a flow chart of a trajectory planning method for an autonomous driving vehicle provided by an embodiment of the present application. The trajectory planning method for an autonomous driving vehicle may specifically include:

[0076] Step 101: Obtain driving status information, planned path information, and driving status information of dynamic obstacles of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention.

[0077] Step 102: Based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacles, a risky dynamic obstacle identification strategy is used to determine the risky dynamic obstacles from the dynamic obstacles.

[0078] Step 103: Based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary recognition strategy is used to obtain a steering collision boundary.

[0079] Step 104: Based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle, trajectory planning processing is performed on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle.

[0080] It should be noted that a dynamic obstacle may be a vehicle located in the driving environment of the autonomous driving vehicle. A dynamic obstacle may include a large vehicle such as a truck or trailer with a turning intention.

[0081] For example, the steering intention of the autonomous driving vehicle can be obtained based on the planned path information of the autonomous driving vehicle. If the steering intention of the autonomous driving vehicle is to turn left, the dynamic obstacle can be a vehicle traveling on the right side of the autonomous driving vehicle. If the steering intention of the autonomous driving vehicle is to turn right, the dynamic obstacle can be a vehicle traveling on the left side of the autonomous driving vehicle.

[0082] It should be noted that the steering collision boundary may be located between the autonomous driving vehicle and the dynamic obstacle, and the steering collision boundary may be used to characterize the boundary line where a collision occurs between the autonomous driving vehicle and the dynamic obstacle.

[0083] It should be noted that the driving state information of the autonomous driving vehicle may include but is not limited to the speed information, acceleration information, jerk information, and shape information of the autonomous driving vehicle. The planned path information of the autonomous driving vehicle may include but is not limited to multiple position points on the path, the forward distance corresponding to each position point, the orientation angle corresponding to each position point, the curvature value corresponding to each position point, etc. The shape information of the autonomous driving vehicle may include but is not limited to the length, width, and position of the vertex angle of the autonomous driving vehicle, etc. The driving state information of the dynamic obstacle may include but is not limited to posture information, speed information, shape information, front wheel speed, etc. The shape information of the dynamic obstacle may also include but is not limited to the length, width, wheelbase, rear wheel track, etc. of the dynamic obstacle. The front wheel speed of the dynamic obstacle may include the left front wheel speed and the right front wheel speed of the dynamic obstacle.

[0084] It should be noted that part or all of the execution subject of steps 101 to 104 may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in a data processing platform on the network side, etc. This embodiment does not specifically limit this.

[0085] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0086] In this way, the steering collision boundary between the autonomous driving vehicle and the dynamic obstacle can be effectively estimated based on the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the dynamic obstacle. The steering collision boundary is the boundary that poses a danger to the turning of the vehicle when the dynamic obstacle turns, thereby realizing the risk estimation of the steering scenario of the autonomous driving vehicle. Moreover, based on the steering collision boundary, the driving trajectory of the autonomous driving vehicle during the turning process is planned, which can effectively reduce the risk of scratches, thereby improving the safety and reliability of the steering of the autonomous driving vehicle.

[0087] Optionally, in a possible implementation of this embodiment, the risk dynamic obstacle identification strategy includes a turning risk area identification algorithm and a risk dynamic obstacle identification algorithm. In step 102, the turning risk area can be obtained based on the planned path information of the autonomous driving vehicle using the turning risk area identification algorithm, and then the risk dynamic obstacle can be determined from the dynamic obstacles based on the driving state information of the dynamic obstacle and the turning risk area using the risk dynamic obstacle identification algorithm.

[0088] In this implementation, the planned path information of the autonomous driving vehicle may include but is not limited to multiple position points on the path, the forward distance corresponding to each position point, the heading angle corresponding to each position point, the curvature value corresponding to each position point, etc.

[0089] In a specific implementation process of this implementation method, first, the turning risk side of the autonomous driving vehicle can be determined based on the planned path information of the autonomous driving vehicle. Secondly, the exploration distance of the turning risk side corresponding to each position point can be calculated based on the preset adjustment coefficient, the forward distance corresponding to each position point, and the curvature value corresponding to each position point. Thirdly, the turning risk area can be obtained based on the exploration distance of the turning risk side corresponding to each position point.

[0090] In this implementation, the driving state information of the dynamic obstacle may include the position information and shape information of the dynamic obstacle.

[0091] In another specific implementation process of the implementation method, first, based on the posture information and shape information of the turning risk area and the dynamic obstacle, it can be determined whether there is an overlapping relationship between the dynamic obstacle and the turning risk area. If there is an overlapping relationship, the dynamic obstacle can be determined to be a risky dynamic obstacle, and then based on the judgment result of whether there is an overlapping relationship, the risky dynamic obstacle can be determined from the dynamic obstacles.

[0092] In this way, risky dynamic obstacles with a risk of turning collision can be accurately and effectively selected from dynamic obstacles, so that the turning collision boundary can be calculated for the risky dynamic obstacles later, thereby improving the efficiency and reliability of trajectory planning processing.

[0093] Optionally, in a possible implementation of the present embodiment, in step 103, specifically in response to the driving state information of the risky dynamic obstacle including the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, a preset Ackerman steering model is used to determine the rear wheel driving trajectory of the risky dynamic obstacle, and then the steering collision boundary can be obtained based on the rear wheel driving trajectory of the risky dynamic obstacle.

[0094] In this implementation, the risky dynamic obstacle may be an ordinary vehicle or an ordinary large vehicle, or a trailer.

[0095] In a specific implementation process of this implementation method, if the risky dynamic obstacle is an ordinary vehicle or an ordinary large vehicle, etc., here, the driving status information of the risky dynamic obstacle may include the left front wheel speed, right front wheel speed, wheelbase, and rear wheel track of the vehicle. The preset Ackerman steering model can be used to deduce the turning radius of the rear wheels of the risky dynamic obstacle based on the left front wheel speed, right front wheel speed, wheelbase, and rear wheel track of the risky dynamic obstacle, and then the rear wheel driving trajectory of the risky dynamic obstacle can be determined based on the turning radius of the rear wheels.

[0096] In another specific implementation process of the implementation method, if the risk dynamic obstacle is a trailer, the trailer can be composed of a tractor and a trailer. Here, the driving state information of the risk dynamic obstacle can include the left front wheel speed of the tractor, the right front wheel speed of the tractor, the wheelbase of the tractor, the rear wheel track of the tractor, the wheelbase of the trailer, the rear wheel track of the trailer, and the distance from the connection between the trailer and the tractor to the rear axle of the tractor. The preset Ackerman steering model can be used to deduce the turning radius of the rear wheel based on the left front wheel speed of the tractor, the right front wheel speed of the tractor, the wheelbase of the tractor, the rear wheel track of the tractor, the wheelbase of the trailer, the rear wheel track of the trailer, and the distance from the connection between the trailer and the tractor to the rear axle of the tractor, and then the rear wheel driving trajectory of the risk dynamic obstacle can be determined based on the turning radius of the rear wheel.

[0097] It is understandable that the rear wheel driving trajectory in the turning direction can be calculated according to the turning direction of the risk dynamic obstacle. For example, if the risk dynamic obstacle turns left, the left rear wheel driving trajectory can be calculated, and if the risk dynamic obstacle turns right, the right rear wheel driving trajectory can be calculated. Here, whether the turning direction of the risk dynamic obstacle is left or right, it can be implemented using the specific implementation process in this implementation method, and the detailed process may not be repeated here.

[0098] In this way, when the front wheel speed of the risk dynamic obstacle can be obtained, the driving trajectory of the rear wheels of the risk dynamic obstacle serving as the steering collision boundary can be directly determined by utilizing the preset Ackerman steering model, thereby improving the accuracy and reliability of the steering collision boundary.

[0099] It should be noted that the various specific implementation processes provided in this implementation can be combined with each other to implement the steps of the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0100] Optionally, in a possible implementation of this embodiment, in step 103, first, in response to the driving state information of the risky dynamic obstacle not including the front wheel speed of the risky dynamic obstacle, the risky dynamic obstacle may be decomposed based on the driving state information of the risky dynamic obstacle using a preset decomposition algorithm to obtain a decomposition unit corresponding to the front of the risky dynamic obstacle. Secondly, based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front, the front wheel driving trajectory of the risky dynamic obstacle may be inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle. Thirdly, based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risky dynamic obstacle, a preset offset algorithm may be used to calculate a lateral offset. Thirdly, based on the lateral offset and the front wheel driving trajectory, the rear wheel driving trajectory of the risky dynamic obstacle is obtained, and thirdly, based on the rear wheel driving trajectory of the risky dynamic obstacle, the steering collision boundary is obtained.

[0101] In this implementation, the driving state information of the risky dynamic obstacle includes shape information of the risky dynamic obstacle.

[0102] In a specific implementation process of this implementation method, first, based on the shape information of the risk dynamic obstacle, a preset decomposition algorithm can be used to calculate the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first spacing between two adjacent decomposition units. Secondly, based on the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first spacing between two adjacent decomposition units, the risk dynamic obstacle can be decomposed to obtain multiple decomposition units corresponding to the risk dynamic obstacle. Thirdly, the decomposition unit corresponding to the front of the risk dynamic obstacle can be obtained from the multiple decomposition units corresponding to the risk dynamic obstacle.

[0103] In this specific implementation process, the shape information may include the length and width of the risk dynamic obstacle.

[0104] In one embodiment of the present specific implementation process, based on the preset number of decomposition units, the length and width of the risky dynamic obstacle, the radius of the decomposition unit corresponding to the risky dynamic obstacle and the first distance between two adjacent decomposition units are calculated.

[0105] In this implementation, the decomposition unit may be a circle that can cover a portion of the contour of the risk dynamic obstacle. The first spacing between two adjacent decomposition units may be the distance between the centers of the two adjacent circles.

[0106] In another specific implementation process of this implementation method, first, each position point in the planned path information of the autonomous driving vehicle and the orientation angle corresponding to each position point can be obtained. Secondly, based on each position point and the decomposition unit corresponding to the front, a binary search algorithm can be used to obtain a matching position point that matches the decomposition unit corresponding to the front. Thirdly, a second spacing between the decomposition unit corresponding to the front and the matching position point can be obtained. Thirdly, based on the orientation angle corresponding to each position point, a preset vector algorithm can be used to determine the normal vector corresponding to each position point. Thirdly, based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second spacing, the front wheel driving trajectory of the risky dynamic obstacle can be inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle.

[0107] In this implementation, the matching position point that matches the decomposition unit corresponding to the front can be a position point in the planned path information that is closest to the center of the decomposition unit corresponding to the front, that is, the matching position point is the position point among all the position points in the planned path information that has the smallest distance from the center of the decomposition unit corresponding to the front.

[0108] One situation of the specific implementation process is that, first, the curvature value of each position point in the planned path information of the autonomous driving vehicle can be obtained. Secondly, based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing, the curvature value of each track point in the front wheel driving track of the risk dynamic obstacle can be determined. Thirdly, the first spacing between two adjacent decomposition units is obtained. Thirdly, based on the curvature value of each track point and the first spacing, the lateral offset is calculated.

[0109] In this way, when the front wheel speed of the risky dynamic obstacle cannot be obtained, the risky dynamic obstacle can be decomposed into multiple decomposition units by utilizing a preset decomposition algorithm, and then the rear wheel driving trajectory of the risky dynamic obstacle can be derived based on the multiple decomposition units, so as to obtain a more accurate steering collision boundary in this case based on the rear wheel driving trajectory of the risky dynamic obstacle, which can improve the accuracy and reliability of the steering collision boundary.

[0110] It should be noted that the various specific implementation processes provided in this implementation can be combined with each other to implement the steps of the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0111] Optionally, in a possible implementation of this embodiment, the driving state information of the autonomous driving vehicle includes shape information, speed information, acceleration information, and jerk information of the autonomous driving vehicle. In step 104, first, the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle can be obtained based on the shape information, planned path information, and driving state information of the risky dynamic obstacle. Secondly, the first time when the autonomous driving vehicle reaches the steering collision boundary can be obtained based on the speed information, planned path information, and the steering collision boundary of the autonomous driving vehicle. Thirdly, the second time when the risky dynamic obstacle reaches the steering collision boundary can be obtained based on the driving state information and the steering collision boundary of the risky dynamic obstacle. Thirdly, in response to the positional relationship information satisfying the preset first constraint condition, or the speed information, the first time, and the second time of the autonomous driving vehicle satisfying the preset second constraint condition, based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle, a preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle.

[0112] In this implementation, the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle may include the autonomous driving vehicle being in front of the risky dynamic obstacle, the autonomous driving vehicle being on the side of the risky dynamic obstacle, the autonomous driving vehicle being behind the risky dynamic obstacle, a first vector between the top angle of the risky dynamic obstacle and the autonomous driving vehicle, and a second vector of the traveling direction of the risky dynamic obstacle.

[0113] Here, the preset first constraint condition may be that the autonomous driving vehicle is located on the side of the risk dynamic obstacle, and the product of the first vector and the second vector is greater than zero. The preset second constraint condition may be that the difference between the first time and the second time is greater than zero, and the speed information of the autonomous driving vehicle is greater than zero and less than a preset speed threshold.

[0114] In a specific implementation process of this implementation method, in response to the position relationship information satisfying a preset first constraint condition, trajectory planning processing can be performed on the autonomous driving vehicle based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle and using a preset objective function to obtain a planned trajectory of the autonomous driving vehicle.

[0115] It is understandable that this specific implementation process may be a process in which an autonomous driving vehicle performs an overtaking process.

[0116] In another specific implementation process of this implementation method, in response to the speed information of the autonomous driving vehicle, the first time and the second time satisfying a preset second constraint condition, based on the speed information, acceleration information and jerk information of the autonomous driving vehicle, a preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain a planned trajectory of the autonomous driving vehicle.

[0117] Here, the preset second constraint condition may be that the difference between the first time and the second time is greater than zero, and the speed information of the autonomous driving vehicle is greater than zero and less than a preset speed threshold.

[0118] It can be understood that this specific implementation process can be a process in which an autonomous driving vehicle gives way.

[0119] In this way, the steering trajectory of the autonomous driving vehicle can be planned according to the steering collision boundary and the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacles, which can further improve the reliability of the driving trajectory and thus further improve the safety of the steering of the autonomous driving vehicle.

[0120] It should be noted that the various specific implementation processes provided in this implementation can be combined with each other to implement the steps of the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0121] In order to better understand the method of the embodiment of the present application, the method of the embodiment of the present application is described below in conjunction with the accompanying drawings and specific application scenarios.

[0122] Figure 2 is a flow chart of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application, such as Figure 2 shown.

[0123] Step 201: Obtain driving status information, planned path information, and driving status information of dynamic obstacles of the autonomous driving vehicle.

[0124] In this embodiment, the driving state information of the autonomous driving vehicle may include but is not limited to the speed information, acceleration information, jerk information, and shape information of the autonomous driving vehicle. The planned path information of the autonomous driving vehicle may include but is not limited to multiple position points on the path, the forward distance corresponding to each position point, the heading angle corresponding to each position point, the curvature value corresponding to each position point, etc.

[0125] In this embodiment, the driving state information of the dynamic obstacle may include but is not limited to position information, speed information, shape information, front wheel speed, etc.

[0126] Here, the shape information of the autonomous driving vehicle may include but is not limited to the length, width, and position of the top angle of the autonomous driving vehicle. The shape information of the dynamic obstacle may also include but is not limited to the length, width, wheelbase, and rear wheel track of the dynamic obstacle. The front wheel speed of the dynamic obstacle may include the left front wheel speed and the right front wheel speed of the dynamic obstacle.

[0127] It is understandable that the front wheel speed of a dynamic obstacle can be obtained in some scenarios, but not in other scenarios. For example, an autonomous vehicle can interact with a dynamic obstacle in real time, and the dynamic obstacle can sense the accurate front wheel speed, while the accurate front wheel speed of a dynamic obstacle cannot be obtained under normal circumstances. After determining a risky dynamic obstacle, it can be determined whether the front wheel speed of the dynamic obstacle that is a risky dynamic obstacle can be obtained.

[0128] Step 202: Based on the planned path information of the autonomous driving vehicle, a turning risk area identification algorithm is used to obtain a turning risk area.

[0129] In this embodiment, the planned path information of the autonomous driving vehicle may include but is not limited to multiple position points on the path, the forward distance corresponding to each position point, the heading angle corresponding to each position point, the curvature value corresponding to each position point, etc.

[0130] Optionally, first, the turning risk side of the autonomous driving vehicle can be determined based on the planned path information of the autonomous driving vehicle. First, the exploration distance of the turning risk side corresponding to each position point can be calculated based on the preset adjustment coefficient, the forward distance corresponding to each position point, and the curvature value corresponding to each position point. Secondly, the turning risk area can be obtained based on the exploration distance of the turning risk side corresponding to each position point.

[0131] Specifically, based on the preset adjustment coefficient, the forward distance corresponding to each position point, and the curvature value corresponding to each position point, the exploration distance of the turning risk side corresponding to each position point is calculated, which can be shown as formula (1):

[0132]

[0133] in, is the exploration distance of the turning risk side corresponding to position point i, λ i It can be a preset adjustment coefficient for adjusting the relationship between the forward distance and the exploration distance, s i can be the forward distance corresponding to position point i, k i is the curvature value corresponding to the position point i.

[0134] Specifically, based on the exploration distance corresponding to each position point, the planned path of the autonomous driving vehicle is explored toward the risk side of the autonomous driving vehicle to obtain an exploration area, which can then be used as a steering risk area.

[0135] Here, the steering intention of the autonomous driving vehicle can be obtained based on the planned path information of the autonomous driving vehicle, and then the steering risk side of the autonomous driving vehicle can be determined based on the steering intention of the autonomous driving vehicle. If the steering intention of the autonomous driving vehicle is to turn left, the right side of the autonomous driving vehicle is the steering risk side. If the steering intention of the autonomous driving vehicle is to turn right, the left side of the autonomous driving vehicle is the steering risk side.

[0136] In addition, it is understandable that when determining the turning risk area, two aspects can be considered. For the risk side of the path of the autonomous vehicle (the right side is the risk side when turning left, and vice versa for turning right), on the one hand, the intention of the dynamic obstacles at the near end of the path is clear, and the randomness at the far end is greater; on the other hand, the curvature of the path of the autonomous vehicle can be used as a reference, and the dynamic obstacles at the place with larger curvature are more likely to turn. In this way, the delineation of the turning risk area can be achieved.

[0137] Step 203: Based on the driving state information and the turning risk area of ​​the dynamic obstacle, a risky dynamic obstacle is identified from the dynamic obstacles using a risky dynamic obstacle identification algorithm.

[0138] In this embodiment, the driving state information of the dynamic obstacle may include the position information and shape information of the dynamic obstacle.

[0139] Optionally, it is possible to determine whether there is an overlapping relationship between the dynamic obstacle and the turning risk area based on the posture information and shape information of the turning risk area and the dynamic obstacle. If there is an overlapping relationship, the dynamic obstacle can be determined to be a risky dynamic obstacle, and then based on the judgment result of whether there is an overlapping relationship, the risky dynamic obstacle can be determined from the dynamic obstacles.

[0140] Specifically, the following formula (2) can be used to determine the risky dynamic obstacles among the dynamic obstacles:

[0141]

[0142] in, can be a risk dynamic obstacle, i.e., a collision risk vehicle, V is a dynamic obstacle, i.e., a vehicle, is the shape of the dynamic obstacle, that is, the two-dimensional space occupied by the dynamic obstacle, To move to risk areas, Can represent the empty set.

[0143] In this implementation, Figure 3 FIG. 1 is a schematic diagram of a turning risk area of ​​a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 3 As shown, the autonomous driving vehicle (ego) turns left, and its right side is the turning risk side. Among the dynamic obstacles (agent) on the right side, there is a dynamic obstacle that overlaps with the turning risk area (M_risk), and this dynamic obstacle can be used as a risk dynamic obstacle.

[0144] In addition, it should be noted that the principles for judging risky dynamic obstacles include the following: dynamic obstacle vehicles turning in the same direction pose a risk of scratching the autonomous vehicle; dynamic obstacle vehicles located behind the autonomous vehicle's direction of travel do not pose a risk to the autonomous vehicle's turning direction; along the direction of travel of the autonomous vehicle, the consideration range of dynamic obstacle vehicles is proportional to the forward distance.

[0145] Step 204: If the driving state information of the risky dynamic obstacle includes the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, a rear wheel driving trajectory of the risky dynamic obstacle is determined using a preset Ackerman steering model, so as to use the rear wheel driving trajectory of the risky dynamic obstacle as a steering collision boundary.

[0146] In this embodiment, the driving state information of the risk dynamic obstacle may include front wheel speed and shape information. The front wheel speed may be the left front wheel speed and the right front wheel speed. The shape information may include wheelbase, rear wheel track, etc. The wheelbase may be the distance between the front and rear axles.

[0147] Alternatively, if the risk dynamic obstacle is an ordinary vehicle or an ordinary large vehicle, etc., the preset Ackerman steering model can be used to deduce the turning radius of the rear wheel based on the left front wheel speed, right front wheel speed, wheelbase, and rear wheel track of the risk dynamic obstacle, as shown in formulas (3) to (6):

[0148] First, the left front wheel steering angular velocity ω l It can be expressed as:

[0149]

[0150] Secondly, the right front wheel steering angular velocity ω r It can be expressed as:

[0151]

[0152] Again, by ω l =ω r , then:

[0153]

[0154] Again, the turning radius H of the rear wheel is derived as:

[0155]

[0156] Where W is the rear wheel track, v fl can be the left front wheel speed, v fr It can be the right front wheel speed, and L can be the wheelbase.

[0157] Furthermore, after the turning radius of the rear wheels is obtained, the rear wheel driving trajectory of the risk dynamic obstacle can be determined based on the turning radius of the rear wheels.

[0158] In addition, after obtaining the turning radius of the rear wheel, the formula (3) can be used to return and calculate the left front wheel steering angular velocity, and then the turning radius of the left front wheel can be calculated based on the left front wheel steering angular velocity and the left front wheel speed. Based on the turning radius of the left front wheel, the front wheel driving trajectory of the risk dynamic obstacle can be determined.

[0159] It is understandable that, here, the area enclosed by the front wheel driving track of the risky dynamic obstacle and the rear wheel driving track of the risky dynamic obstacle may be an area that may collide with the rear wheel of the risky dynamic obstacle, that is, a scratching risk area. The autonomous driving vehicle should avoid entering the scratching risk area when turning.

[0160] For example, Figure 4 FIG. 1 is a schematic diagram of a risk dynamic obstacle steering of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 4 As shown in the figure, when the risk dynamic obstacle turns, O is the turning center, R is the overall turning radius, W is the rear wheel track, and v fl can be the left front wheel speed, v fr It can be the right front wheel speed, L can be the wheelbase, v can be the vehicle speed, risk_line can represent the rear wheel driving trajectory, that is, the steering collision boundary, and H is the steering radius of the rear wheel, that is, the steering radius of the left rear wheel.

[0161] In this embodiment, if the risk dynamic obstacle is a trailer, the trailer may be composed of a tractor and a trailer. Here, the preset Ackerman steering model may be used to derive the steering radius of the rear wheels based on the left front wheel speed of the tractor, the right front wheel speed of the tractor, the wheelbase of the tractor, the rear wheel track of the tractor, the wheelbase of the trailer, the rear wheel track of the trailer, and the distance from the connection between the trailer and the tractor to the rear axle of the tractor, as shown in formulas (7) to (9):

[0162] First, the steering angular velocity of the left / right front wheels of the tractor is equal:

[0163]

[0164] Secondly, the turning radius R2 of the rear wheel of the tractor is derived as:

[0165]

[0166] Again, from the geometric relationship, the turning radius R1 of the rear wheel of the trailer can be obtained as:

[0167]

[0168] Among them, v fl can be the left front wheel speed of the tractor, v fr It can be the right front wheel speed of the tractor, W1 can be the rear wheel track of the trailer, W2 can be the rear wheel track of the tractor, L1 can be the wheelbase of the trailer, L2 can be the wheelbase of the tractor, and b can be the distance from the connection between the trailer and the tractor to the rear axle of the tractor.

[0169] Furthermore, after the turning radius of the rear wheels of the trailer is obtained, the rear wheel driving trajectory of the risk dynamic obstacle can be determined based on the turning radius of the rear wheels of the trailer.

[0170] For example, Figure 5 FIG. 1 is a schematic diagram of another risk dynamic obstacle steering of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 5 As shown in FIG. 1 , the risk dynamic obstacle may be a trailer. When the risk dynamic obstacle turns, O is the turning center, R is the turning radius of the connection, R1 is the turning radius of the rear wheels of the trailer, R2 is the turning radius of the rear wheels of the tractor, R3 is the turning radius of the left front wheel of the tractor, R4 is the turning radius of the right front wheel of the tractor, and v fl can be the left front wheel speed of the tractor, v fr It can be the right front wheel speed of the tractor, W1 can be the rear wheel track of the trailer, W2 can be the rear wheel track of the tractor, L1 can be the wheelbase of the trailer, L2 can be the wheelbase of the tractor, b can be the distance from the connection between the trailer and the tractor to the rear axle of the tractor, and Risk line can represent the driving trajectory of the rear wheels, that is, the steering collision boundary.

[0171] In addition, after obtaining the turning radius of the rear wheel, the left front wheel steering angular velocity can be returned to calculate, and then the turning radius of the left front wheel can be calculated based on the left front wheel steering angular velocity and the left front wheel speed. Based on the turning radius of the left front wheel, the front wheel driving trajectory of the risk dynamic obstacle can be determined.

[0172] Here, by using the Ackerman steering model, the rear wheel steering radius of an ordinary truck or trailer can be well inferred to derive an accurate steering collision boundary and obtain the scratch danger zone.

[0173] It should be noted that in actual application scenarios, when large trucks and trailers, as dynamic obstacles, turn at intersections or crossroads, the turning speed is slow due to the large inertia of the vehicle weight. Therefore, it can be assumed that the wheels of large trucks and trailers are fully rolled during the turning process. According to the preset Ackerman steering model, that is, the Ackerman steering geometry model, the normal direction of the wheel center intersects the instantaneous turning center O on the extension line of the rear axle, such as Figure 4 and 5 shown.

[0174] Step 205: If the driving state information of the risky dynamic obstacle does not include the front wheel speed of the risky dynamic obstacle, then based on the driving state information of the risky dynamic obstacle, a preset decomposition algorithm is used to decompose the risky dynamic obstacle, and based on the result of the decomposition processing and the planned path information of the autonomous driving vehicle, a preset trajectory inference algorithm is used to infer the rear wheel driving trajectory of the risky dynamic obstacle, and the rear wheel driving trajectory of the risky dynamic obstacle is used as the steering collision boundary.

[0175] In this embodiment, the driving state information of the risky dynamic obstacle may also include shape information of the risky dynamic obstacle. The shape information may include the length and width of the risky dynamic obstacle.

[0176] In this embodiment, the result of the decomposition process may include multiple decomposition units corresponding to the risky dynamic obstacle. From the multiple decomposition units corresponding to the risky dynamic obstacle, the decomposition unit corresponding to the front part of the risky dynamic obstacle, that is, the decomposition unit corresponding to the front wheel part of the risky dynamic obstacle can be obtained.

[0177] First, it may be determined whether the acquired driving state information of the risky dynamic obstacle includes the front wheel speed of the risky dynamic obstacle.

[0178] Secondly, when the driving status information of the risky dynamic obstacle does not include the front wheel speed of the risky dynamic obstacle, the radius of the decomposition unit corresponding to the risky dynamic obstacle and the first spacing between two adjacent decomposition units can be calculated based on the length and width of the risky dynamic obstacle using a preset decomposition algorithm. Then, based on the radius of the decomposition unit corresponding to the risky dynamic obstacle and the first spacing between two adjacent decomposition units, the risky dynamic obstacle can be decomposed to obtain multiple decomposition units corresponding to the risky dynamic obstacle, and the decomposition unit corresponding to the front of the risky dynamic obstacle can be obtained from the multiple decomposition units corresponding to the risky dynamic obstacle.

[0179] Here, based on the preset number of decomposition units, the length and width of the risk dynamic obstacle, the radius r of the decomposition unit corresponding to the risk dynamic obstacle and the first spacing between two adjacent decomposition units can be calculated. The preset decomposition algorithm can be shown as formulas (10) and (11):

[0180]

[0181]

[0182] Among them, r is the radius of the decomposition unit, n is the number of preset decomposition units, l is the length of the risk dynamic obstacle, w is the width of the risk dynamic obstacle, and d is the first distance between two adjacent decomposition units.

[0183] For example, Figure 6 FIG. 1 is a schematic diagram of multiple decomposition units of risk dynamic obstacles of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 6 As shown, after the risk dynamic obstacle is subjected to rotationally invariant primitive decomposition processing, the risk dynamic obstacle can be decomposed into multiple decomposition units, each of which can be a circle covering the risk dynamic obstacle, r is the radius of the decomposition unit, l is the length of the risk dynamic obstacle, w is the width of the risk dynamic obstacle, and d is the first spacing between two adjacent decomposition units, that is, the distance between the centers of two adjacent circles.

[0184] Thirdly, each position point in the planned path information of the autonomous driving vehicle and the orientation angle corresponding to each position point can be obtained, and based on each position point and the decomposition unit corresponding to the front, a binary search algorithm is used to obtain a matching position point that matches the decomposition unit corresponding to the front, and a second distance between the decomposition unit corresponding to the front and the matching position point is obtained, and based on the orientation angle corresponding to each position point, a preset vector algorithm is used to determine the normal vector corresponding to each position point, and based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second distance, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle.

[0185] Optionally, based on each position point and the decomposition unit corresponding to the front, a binary search algorithm is used to find a position point whose spacing with the decomposition unit corresponding to the front meets a preset matching condition, and the position point is used as a matching position point that matches the decomposition unit corresponding to the front, so that a second spacing between the decomposition unit corresponding to the front and the matching position point can be obtained.

[0186] Here, the preset matching condition may be that the distance between the position point and the decomposition unit corresponding to the front portion is the minimum distance. The decomposition unit corresponding to the front portion may represent the front wheel portion of the risk dynamic obstacle.

[0187] In this way, the optimal matching point of the risk dynamic obstacle on the planned path of the autonomous driving vehicle can be found through binary search, and then the minimum distance r between the risk dynamic obstacle and the autonomous driving vehicle can be obtained.

[0188] It is understandable that here, it is assumed that the risk dynamic obstacle on the risk side of the autonomous driving vehicle is rational in the steering process and will not intentionally deviate from the driving path of the autonomous driving vehicle. Based on the above assumptions, it can be inferred that the decomposition unit running trajectory corresponding to the front of the risk dynamic obstacle, that is, the front wheel driving trajectory can be expressed as

[0189] Optionally, the steering information of the autonomous driving vehicle can be determined based on the orientation angle corresponding to each position point of the autonomous driving vehicle, and then the normal vector corresponding to each position point can be calculated based on the orientation angle corresponding to each position point using a preset vector algorithm corresponding to the steering information of the autonomous driving vehicle.

[0190] For example, when the autonomous driving vehicle is turning left, based on the orientation angle corresponding to each position point, the normal vector corresponding to each position point is calculated using the preset vector algorithm corresponding to the left turn of the autonomous driving vehicle, which can be shown as formula (12):

[0191]

[0192] in, It can be the normal vector corresponding to the position point i, that is, the normal vector to the right of the position point i, θ can be the orientation angle corresponding to the position point i, and T can be the path planning information for the autonomous driving vehicle.

[0193] When the autonomous driving vehicle is turning right, based on the orientation angle corresponding to each position point, the normal vector corresponding to each position point is calculated using the preset vector algorithm corresponding to the right turn of the autonomous driving vehicle, which can be shown as formula (13):

[0194]

[0195] in, It can be the normal vector corresponding to the position point i, that is, the normal vector to the left of the position point i, θ can be the orientation angle corresponding to the position point i, and T can be the path planning information for the autonomous driving vehicle.

[0196] Optionally, after determining the normal vector corresponding to each position point, the front wheel driving trajectory of the risky dynamic obstacle can be inferred based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second distance to obtain the front wheel driving trajectory of the risky dynamic obstacle.

[0197] Specifically, based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second spacing, the front wheel driving trajectory of the risk dynamic obstacle is estimated, as shown in formula (14):

[0198]

[0199] in, is the location point i in the planned path information of the autonomous driving vehicle, Nv i is the normal vector corresponding to the position point i, q is the second spacing, that is, the minimum distance between the risk dynamic obstacle and the autonomous driving vehicle, is the trajectory point i in the inferred front wheel driving trajectory of the risky dynamic obstacle. Based on each inferred trajectory point, the front wheel driving trajectory of the risky dynamic obstacle can be obtained.

[0200] For example, Figure 7 FIG. 1 is a schematic diagram of the front wheel driving trajectory of a risk dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 7 As shown in FIG. 1 , the front wheel driving trajectory of the risk dynamic obstacle is inferred by the planned path information of the autonomous driving vehicle and the second distance q. The circle may be a decomposition unit corresponding to the front of the risk dynamic obstacle, the trajectory in front of the front decomposition unit of the risk dynamic obstacle may be an inferred trajectory (black dotted line), the trajectory behind the front decomposition unit of the risk dynamic obstacle may be a spliced ​​trajectory (red dotted line), and the inferred trajectory and the spliced ​​trajectory may constitute the inferred front wheel driving trajectory of the risk dynamic obstacle.

[0201] Thirdly, the lateral offset can be calculated based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risky dynamic obstacle using a preset offset algorithm. Specifically, the curvature value of each position point in the planned path information of the autonomous driving vehicle can be obtained first, and the curvature value of each track point in the front wheel driving trajectory of the risky dynamic obstacle can be determined based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing, and the first spacing of two adjacent decomposition units can be obtained. Based on the curvature value and the first spacing of each track point, the lateral offset is calculated, and based on the lateral offset and the front wheel driving trajectory, the rear wheel driving trajectory of the risky dynamic obstacle is obtained, so that the rear wheel driving trajectory of the risky dynamic obstacle is used as the steering collision boundary.

[0202] Optionally, here, based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing, the curvature value of each track point in the front wheel driving track of the risk dynamic obstacle is determined. It can be shown as formula (15):

[0203]

[0204] in, is the curvature value of the corresponding position point i of the autonomous driving vehicle, and q is the second spacing.

[0205] Using the preset offset algorithm, the lateral offset δ between the subsequent circle j motion trajectory point of the circle (i=0) of the decomposition unit corresponding to the front and the circular motion trajectory point of the decomposition unit corresponding to the front can be expressed as formula (16):

[0206]

[0207] in, It can be the curvature value of each track point in the front wheel driving track, that is, the estimated motion track of the circle of the decomposition unit corresponding to the front, d i,j It may be a first spacing, that is, the distance between the center of the i decomposition unit and the center of the j decomposition unit adjacent thereto.

[0208] Here, the rear wheel trajectory of the risky dynamic obstacle can be calculated based on the lateral offset and the front wheel trajectory.

[0209] For example, Figure 8 FIG. 1 is a schematic diagram of a rear wheel driving trajectory of a risk dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 8 As shown in the figure, the red track point (1) on the left may represent the rear wheel driving track of the risky dynamic obstacle, and the red track point (2) on the right may represent the front wheel driving track of the risky dynamic obstacle. The area formed between the two tracks may be the area where the rear wheel of the risky dynamic obstacle may collide, i.e., the scratching risk area. The autonomous driving vehicle should avoid entering the scratching risk area when turning.

[0210] In addition, it can be understood that the method of step 205 can be applicable to different types of risk dynamic obstacles. For example, risk dynamic obstacles can include but are not limited to ordinary vehicles, large trucks, trailers, etc.

[0211] Step 206: Based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle, trajectory planning processing is performed on the autonomous driving vehicle to obtain a planned trajectory of the autonomous driving vehicle.

[0212] In this embodiment, the driving status information of the autonomous driving vehicle includes shape information, speed information, acceleration information, and jerk information of the autonomous driving vehicle.

[0213] Optionally, first, the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle can be obtained based on the shape information, planned path information and driving state information of the risky dynamic obstacle of the autonomous driving vehicle. Secondly, the first time when the autonomous driving vehicle reaches the steering collision boundary can be obtained based on the speed information, planned path information and steering collision boundary of the autonomous driving vehicle. Thirdly, the second time when the risky dynamic obstacle reaches the steering collision boundary can be obtained based on the driving state information and steering collision boundary of the risky dynamic obstacle. Thirdly, in response to the positional relationship information satisfying the preset first constraint condition, or the speed information, the first time and the second time of the autonomous driving vehicle satisfying the preset second constraint condition, based on the speed information, acceleration information and jerk information of the autonomous driving vehicle, the preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle.

[0214] Here, the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle can be obtained based on the shape information of the autonomous driving vehicle, the position points in the planned path information of the autonomous driving vehicle, and the posture information in the driving state information of the risky dynamic obstacle. The positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle may include the autonomous driving vehicle being in front of the risky dynamic obstacle, the autonomous driving vehicle being to the side of the risky dynamic obstacle, the autonomous driving vehicle being behind the risky dynamic obstacle, a first vector between the top angle of the risky dynamic obstacle and the autonomous driving vehicle, and a second vector in the driving direction of the risky dynamic obstacle.

[0215] Optionally, the preset first constraint condition may be that the autonomous driving vehicle is located on the side of the risk dynamic obstacle, and the product of the first vector and the second vector is greater than zero. The preset second constraint condition may be that the difference between the first time and the second time is greater than zero, and the speed information of the autonomous driving vehicle is greater than zero and less than a preset speed threshold. The preset speed threshold may be pre-set according to the design of the autonomous driving vehicle or the road environment.

[0216] In this embodiment, one situation may be that when the autonomous driving vehicle is located on the side of a risky dynamic obstacle and the product of the first vector and the second vector is greater than zero, trajectory planning processing can be performed on the autonomous driving vehicle based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle using a preset objective function to obtain a planned trajectory of the autonomous driving vehicle.

[0217] It can be understood that here, in this case, the autonomous driving vehicle can perform the rush-to-drive processing.

[0218] Exemplarily, when the autonomous driving vehicle is located on the side of the risk dynamic obstacle and the product of the first vector and the second vector is greater than zero, the preset objective function y is as shown in formula (17):

[0219]

[0220] in, It can provide speed information for autonomous vehicles. It can provide acceleration information for autonomous vehicles. It can be the acceleration information of the autonomous driving vehicle, λ1, λ2, and λ3 are all preset adjustment coefficients. can be the first vector, It can be the second vector, and i can be the trajectory point of the autonomous driving vehicle.

[0221] Another situation may be that when the difference between the first time and the second time is greater than zero, and the speed information of the autonomous driving vehicle is greater than zero and less than a preset speed threshold, the autonomous driving vehicle can be subjected to trajectory planning processing based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle using a preset objective function to obtain a planned trajectory of the autonomous driving vehicle.

[0222] It can be understood that here, in this case, the autonomous driving vehicle can perform the yield process.

[0223] Exemplarily, when the difference between the first time and the second time is greater than zero, and the speed information of the autonomous driving vehicle is greater than zero and less than a preset speed threshold, the preset objective function y can be shown as formula (18):

[0224]

[0225]

[0226] 0 <v i <v max

[0227] i=0,1,....,T

[0228] It can provide speed information for autonomous vehicles. It can provide acceleration information for autonomous vehicles. It can be the acceleration information of the autonomous driving vehicle, λ1, λ2, and λ3 are all preset adjustment coefficients. Can be the first time, Can be the second time, v i can be the speed information of the autonomous driving vehicle at trajectory point i, v max It can be a preset speed threshold, and i can be a trajectory point of the autonomous driving vehicle.

[0229] Another situation may be that when the autonomous driving vehicle is located as a whole in front of a risky dynamic obstacle, the product of the first vector and the second vector is greater than zero, and the autonomous driving vehicle can plan the steering trajectory without considering the risky dynamic obstacle.

[0230] Another situation may be that the autonomous driving vehicle is located behind the risk dynamic obstacle, and when the product of the first vector and the second vector is less than zero, it can be determined whether the speed information, the first time, and the second time of the autonomous driving vehicle meet the preset second constraint condition. If the speed information, the first time, and the second time of the autonomous driving vehicle meet the preset second constraint condition, then based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle, the preset objective function can be used to perform trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle. Alternatively, the speed of the autonomous driving vehicle can be directly reduced based on the steering collision boundary to obtain the corresponding planned trajectory of the autonomous driving vehicle, so as to control the autonomous driving vehicle to follow the outside of the steering collision boundary based on the planned trajectory of the autonomous driving vehicle. In this way, the autonomous driving vehicle can be prevented from rushing.

[0231] In addition, another situation may be that when the autonomous driving vehicle is already inside the steering collision boundary risk line of the risk dynamic obstacle and the above-mentioned preset objective function has no solution, an alarm message may be reported to request takeover.

[0232] For example, Figures 9a to 9c FIG. 1 is a schematic diagram of the position relationship between an autonomous driving vehicle and a risk dynamic obstacle in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 9a As shown, the autonomous driving vehicle is located in front of the risky dynamic obstacle. Here, the front may mean that the autonomous driving vehicle as a whole is located in front of the risky dynamic obstacle, and it may not be directly in front. can be the first vector, It can be a second vector. The product of the first vector and the second vector is greater than zero. The process of the autonomous driving vehicle planning the steering trajectory does not need to consider the risk dynamic obstacles; Figure 9b As shown, the autonomous vehicle is located on the side of the risky dynamic obstacle. It can be the vector from the upper left corner of the risk dynamic obstacle to the upper right corner of the autonomous driving vehicle, that is, the first vector, It can be a vector of the direction of travel of the risk dynamic obstacle, that is, the second vector. The product of the first vector and the second vector is greater than zero. The steering trajectory can be planned using the method based on the preset first constraint condition or the method based on the preset second constraint condition; Fig.9cAs shown, the autonomous driving vehicle is located behind the risky dynamic obstacle. Here, the rear may mean that the autonomous driving vehicle is located behind the risky dynamic obstacle as a whole, and it may not be directly behind. can be the first vector, It can be a second vector, and the product of the first vector and the second vector is less than zero, and the steering trajectory can be planned using the method based on the preset second constraint condition. Alternatively, the speed of the autonomous driving vehicle can be directly reduced based on the steering collision boundary to obtain a planned trajectory for controlling the autonomous driving vehicle to follow the autonomous driving vehicle traveling outside the steering collision boundary.

[0233] It is understandable that the first vector can be determined by selecting other positions of the autonomous driving vehicle and the risk dynamic obstacle. For example, the lower vertex angle of the side of the autonomous driving vehicle close to the risk dynamic obstacle and the lower vertex angle of the side of the risk dynamic obstacle close to the autonomous driving vehicle can be selected to determine the first vector, or the lower vertex angle of the side of the autonomous driving vehicle close to the risk dynamic obstacle and the upper vertex angle of the side of the risk dynamic obstacle close to the autonomous driving vehicle can be selected to determine the first vector. The selection can be made according to actual business needs and is not specifically limited here.

[0234] In this way, by adopting the technical solution in this embodiment, the steering danger zone caused by the inner wheel difference phenomenon can be effectively estimated based on limited observation data such as the driving status information of the autonomous driving vehicle, the planned path information, and the driving status information of dynamic obstacles, thereby realizing risk estimation for this type of risk scenario.

[0235] Moreover, the driving trajectory of the autonomous driving vehicle during the turning process can be planned according to the turning danger zone, which can effectively reduce the risk of scratches, thereby improving the safety and reliability of the autonomous driving vehicle's steering.

[0236] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0237] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0238] Fig.10 A structural block diagram of a trajectory planning device for an autonomous driving vehicle provided by an embodiment of the present application is shown. Fig.10As shown. The trajectory planning device 800 of the autonomous driving vehicle of this embodiment may include an acquisition unit 1001, a determination unit 1002, an acquisition unit 1003, and a planning unit 1004. The acquisition unit 1001 is used to acquire the driving state information, the planned path information, and the driving state information of the dynamic obstacle of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention; the determination unit 1002 is used to determine the risk dynamic obstacle from the dynamic obstacles based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle, using the risk dynamic obstacle identification strategy; the acquisition unit 1003 is used to obtain the steering collision boundary based on the planned path information of the autonomous driving vehicle and the driving state information of the risk dynamic obstacle, using the steering collision boundary identification strategy; the planning unit 1004 is used to perform trajectory planning processing on the autonomous driving vehicle based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risk dynamic obstacle, so as to obtain the planned trajectory of the autonomous driving vehicle.

[0239] It should be noted that part or all of the trajectory planning device of the autonomous driving vehicle of this embodiment may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in a data processing platform on the network side, etc. This embodiment does not specifically limit this.

[0240] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0241] Optionally, in a possible implementation of this embodiment, the risk dynamic obstacle identification strategy includes a turning risk area identification algorithm and a risk dynamic obstacle identification algorithm, and the determination unit 1002 can be used to obtain a turning risk area based on the planned path information of the autonomous driving vehicle using the turning risk area identification algorithm; and determine the risk dynamic obstacle from the dynamic obstacles based on the driving state information of the dynamic obstacle and the turning risk area using the risk dynamic obstacle identification algorithm.

[0242] Optionally, in a possible implementation of the present embodiment, the obtaining unit 1003 may be used to respond to the driving state information of the risk dynamic obstacle including the front wheel speed of the risk dynamic obstacle, determine the rear wheel driving trajectory of the risk dynamic obstacle based on the driving state information of the risk dynamic obstacle and utilize a preset Ackerman steering model; and obtain the steering collision boundary based on the rear wheel driving trajectory of the risk dynamic obstacle.

[0243] Optionally, in a possible implementation of the present embodiment, the obtaining unit 1003 may be used to, in response to the driving state information of the risk dynamic obstacle not including the front wheel speed of the risk dynamic obstacle, decompose the risk dynamic obstacle based on the driving state information of the risk dynamic obstacle using a preset decomposition algorithm to obtain a decomposition unit corresponding to the front of the risk dynamic obstacle; infer the front wheel driving trajectory of the risk dynamic obstacle based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front to obtain the front wheel driving trajectory of the risk dynamic obstacle; calculate a lateral offset based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risk dynamic obstacle using a preset offset algorithm; obtain a rear wheel driving trajectory of the risk dynamic obstacle based on the lateral offset and the front wheel driving trajectory; and obtain the steering collision boundary based on the rear wheel driving trajectory of the risk dynamic obstacle.

[0244] Optionally, in a possible implementation of the present embodiment, the driving state information of the risk dynamic obstacle includes shape information of the risk dynamic obstacle, and the obtaining unit 1003 can be used to calculate, based on the shape information of the risk dynamic obstacle and using a preset decomposition algorithm, the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first spacing between two adjacent decomposition units; based on the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first spacing between two adjacent decomposition units, decompose the risk dynamic obstacle to obtain multiple decomposition units corresponding to the risk dynamic obstacle; and obtain the decomposition unit corresponding to the front of the risk dynamic obstacle from the multiple decomposition units corresponding to the risk dynamic obstacle.

[0245] Optionally, in a possible implementation of this embodiment, the acquisition unit 1003 can be used to obtain each position point in the planned path information of the autonomous driving vehicle and the orientation angle corresponding to each position point; based on each position point and the decomposition unit corresponding to the front, a binary search algorithm is used to obtain a matching position point that matches the decomposition unit corresponding to the front; a second distance between the decomposition unit corresponding to the front and the matching position point is obtained; based on the orientation angle corresponding to each position point, a preset vector algorithm is used to determine the normal vector corresponding to each position point; based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second distance, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle.

[0246] Optionally, in a possible implementation of this embodiment, the acquisition unit 1003 can be used to obtain the curvature value of each position point in the planned path information of the autonomous driving vehicle; determine the curvature value of each trajectory point in the front wheel driving trajectory of the risk dynamic obstacle based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing; obtain the first spacing between two adjacent decomposition units; and calculate the lateral offset based on the curvature value of each trajectory point and the first spacing.

[0247] Optionally, in a possible implementation of this embodiment, the driving state information of the autonomous driving vehicle includes shape information, speed information, acceleration information, and jerk information of the autonomous driving vehicle. The planning unit 1004 can be used to obtain positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle based on the shape information, planned path information, and driving state information of the risky dynamic obstacle; obtain a first time when the autonomous driving vehicle reaches the steering collision boundary based on the speed information, planned path information, and the steering collision boundary of the autonomous driving vehicle; obtain a second time when the risky dynamic obstacle reaches the steering collision boundary based on the driving state information and the steering collision boundary of the risky dynamic obstacle; in response to the positional relationship information satisfying a preset first constraint condition, or the speed information, the first time, and the second time of the autonomous driving vehicle satisfying a preset second constraint condition, based on the speed information, acceleration information, and jerk information of the autonomous driving vehicle, a preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle.

[0248] In this embodiment, the driving state information, planned path information, and driving state information of the dynamic obstacle of the autonomous driving vehicle can be obtained by the acquisition unit; wherein the autonomous driving vehicle has a turning intention, and then the determination unit can determine the risky dynamic obstacle from the dynamic obstacles based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle by using the risky dynamic obstacle identification strategy, and the acquisition unit can obtain the steering collision boundary based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle by using the steering collision boundary identification strategy, so that the planning unit can determine the risky dynamic obstacle from the dynamic obstacles based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle. The autonomous driving vehicle is subjected to trajectory planning processing based on the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle, so as to obtain the planned trajectory of the autonomous driving vehicle. Since the steering collision boundary between the autonomous driving vehicle and the dynamic obstacle can be effectively estimated based on the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the dynamic obstacle, the steering collision boundary is the boundary that poses a danger to the turning of the autonomous vehicle when the dynamic obstacle turns, thereby realizing the risk estimation of the steering scenario of the autonomous driving vehicle, and based on the steering collision boundary, the driving trajectory of the autonomous driving vehicle during the turning process is planned, which can effectively reduce the risk of scratching, thereby improving the safety and reliability of the steering of the autonomous driving vehicle.

[0249] In the technical solution of this application, the user personal information involved, such as the collection, storage, use, processing, transmission, provision and disclosure of user images and attribute data, etc., complies with the provisions of relevant laws and regulations and does not violate public order and good morals.

[0250] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0251] According to an embodiment of the present application, further, an autonomous driving vehicle including the provided electronic device is provided, and the autonomous driving vehicle may include an unmanned vehicle of level L2 or above. For example, the unmanned vehicle may include an unmanned logistics vehicle, an unmanned patrol vehicle, an unmanned delivery vehicle, etc.

[0252] Fig.11A schematic block diagram of an example electronic device 1100 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0253] like Fig.11 As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1106 is also connected to the bus 1104.

[0254] Multiple components in the electronic device 1100 are connected to the I / O interface 1105, including: an input unit 1106, such as a keyboard, a mouse, etc.; an output unit 1107, such as various types of displays, speakers, etc.; a storage unit 1108, such as a disk, an optical disk, etc.; and a communication unit 1109, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1109 allows the electronic device 1100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0255] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above, such as a trajectory planning method for an autonomous driving vehicle. For example, in some embodiments, the trajectory planning method for an autonomous driving vehicle may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1100 via the ROM 1102 and / or the communication unit 1109. When the computer program is loaded into the RAM 1103 and executed by the computing unit 1101, one or more steps of the trajectory planning method for the autonomous driving vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to execute a trajectory planning method for an autonomous driving vehicle in any other suitable manner (e.g., by means of firmware).

[0256] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0257] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0258] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0259] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0260] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0261] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0262] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0263] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A trajectory planning method for an autonomous driving vehicle, characterized in that: The method comprises: Acquiring driving state information, planned path information, and driving state information of dynamic obstacles of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention; Based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacles, a risky dynamic obstacle is determined from the dynamic obstacles using a risky dynamic obstacle identification strategy; Based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle, a steering collision boundary is obtained by using a steering collision boundary identification strategy; Based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle, the autonomous driving vehicle is subjected to trajectory planning processing to obtain the planned trajectory of the autonomous driving vehicle.

2. The method according to claim 1, characterized in that The risk dynamic obstacle identification strategy includes a steering risk area identification algorithm and a risk dynamic obstacle identification algorithm. The risk dynamic obstacle identification strategy is used based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle to determine the risk dynamic obstacle from the dynamic obstacles, including: Based on the planned path information of the autonomous driving vehicle, using the turning risk area identification algorithm, obtaining a turning risk area; Based on the driving state information of the dynamic obstacle and the turning risk area, the risky dynamic obstacle is determined from the dynamic obstacles using the risky dynamic obstacle identification algorithm.

3. The method according to claim 1, characterized in that The method of obtaining a steering collision boundary by using a steering collision boundary identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle includes: In response to the driving state information of the risky dynamic obstacle including the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, a rear wheel driving trajectory of the risky dynamic obstacle is determined by using a preset Ackerman steering model; The steering collision boundary is obtained based on the rear wheel driving trajectory of the risk dynamic obstacle.

4. The method according to claim 1, characterized in that: The method of obtaining a steering collision boundary by using a steering collision boundary identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the risky dynamic obstacle includes: In response to the driving state information of the risky dynamic obstacle not including the front wheel speed of the risky dynamic obstacle, based on the driving state information of the risky dynamic obstacle, using a preset decomposition algorithm, decomposing the risky dynamic obstacle to obtain a decomposition unit corresponding to the front of the risky dynamic obstacle; Based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front part, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle; Based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risk dynamic obstacle, a lateral offset is calculated using a preset offset algorithm; Based on the lateral offset and the front wheel driving trajectory, obtaining the rear wheel driving trajectory of the risk dynamic obstacle; The steering collision boundary is obtained based on the rear wheel driving trajectory of the risk dynamic obstacle.

5. The method according to claim 4, characterized in that The driving state information of the risky dynamic obstacle includes shape information of the risky dynamic obstacle. Based on the driving state information of the risky dynamic obstacle, the risky dynamic obstacle is decomposed by using a preset decomposition algorithm to obtain a decomposition unit corresponding to the front of the risky dynamic obstacle, including: Based on the shape information of the risk dynamic obstacle, a preset decomposition algorithm is used to calculate the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first distance between two adjacent decomposition units; Based on the radius of the decomposition unit corresponding to the risk dynamic obstacle and the first distance between two adjacent decomposition units, the risk dynamic obstacle is decomposed to obtain a plurality of decomposition units corresponding to the risk dynamic obstacle; The decomposition unit corresponding to the front of the risky dynamic obstacle is obtained from the multiple decomposition units corresponding to the risky dynamic obstacle.

6. The method according to claim 4, characterized in that The method of performing inference processing on the front wheel driving trajectory of the risk dynamic obstacle based on the planned path information of the autonomous driving vehicle and the decomposition unit corresponding to the front portion to obtain the front wheel driving trajectory of the risk dynamic obstacle includes: Obtain each position point and the orientation angle corresponding to each position point in the planned path information of the autonomous driving vehicle; Based on each position point and the decomposition unit corresponding to the front portion, a matching position point matching the decomposition unit corresponding to the front portion is obtained by using a binary search algorithm; Obtaining a second distance between the decomposition unit corresponding to the front portion and the matching position point; Based on the orientation angle corresponding to each position point, a normal vector corresponding to each position point is determined using a preset vector algorithm; Based on each position point in the planned path information of the autonomous driving vehicle, the normal vector corresponding to each position point, and the second distance, the front wheel driving trajectory of the risky dynamic obstacle is inferred to obtain the front wheel driving trajectory of the risky dynamic obstacle.

7. The method according to claim 6, characterized in that The lateral offset is calculated based on the planned path information of the autonomous driving vehicle and the front wheel driving trajectory of the risk dynamic obstacle using a preset offset algorithm, including: Obtaining a curvature value of each position point in the planned path information of the autonomous driving vehicle; Determine the curvature value of each track point in the front wheel driving track of the risk dynamic obstacle based on the curvature value of each position point in the planned path information of the autonomous driving vehicle and the second spacing; Get the first distance between two adjacent decomposition units; The lateral offset is calculated based on the curvature value of each trajectory point and the first spacing.

8. The method according to claim 1, characterized in that The driving state information of the autonomous driving vehicle includes shape information, speed information, acceleration information, and jerk information of the autonomous driving vehicle. Based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risk dynamic obstacle, trajectory planning processing is performed on the autonomous driving vehicle to obtain the planned trajectory of the autonomous driving vehicle, including: Based on the shape information, the planned path information and the driving state information of the risky dynamic obstacle of the autonomous driving vehicle, obtaining the positional relationship information between the autonomous driving vehicle and the risky dynamic obstacle; Based on the speed information, the planned path information, and the steering collision boundary of the autonomous driving vehicle, obtaining a first time when the autonomous driving vehicle reaches the steering collision boundary; Based on the driving state information of the risky dynamic obstacle and the turning collision boundary, obtaining a second time when the risky dynamic obstacle reaches the turning collision boundary; In response to the position relationship information satisfying a preset first constraint condition, or the speed information, the first time and the second time of the autonomous driving vehicle satisfying a preset second constraint condition, based on the speed information, acceleration information and jerk information of the autonomous driving vehicle, a preset objective function is used to perform trajectory planning processing on the autonomous driving vehicle to obtain a planned trajectory of the autonomous driving vehicle.

9. A trajectory planning device for an autonomous driving vehicle, characterized in that: The device comprises: An acquisition unit, used to acquire driving state information, planned path information, and driving state information of a dynamic obstacle of the autonomous driving vehicle; wherein the autonomous driving vehicle has a turning intention; a determination unit, configured to determine a risky dynamic obstacle from the dynamic obstacles by using a risky dynamic obstacle identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the dynamic obstacle; An obtaining unit, configured to obtain a steering collision boundary by using a steering collision boundary identification strategy based on the planned path information of the autonomous driving vehicle and the driving state information of the risk dynamic obstacle; A planning unit is used to perform trajectory planning processing on the autonomous driving vehicle based on the steering collision boundary, the driving state information of the autonomous driving vehicle, the planned path information, and the driving state information of the risky dynamic obstacle to obtain the planned trajectory of the autonomous driving vehicle.

10. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

12. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

13. An autonomous driving vehicle comprising the electronic device as claimed in claim 10.

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