Path planning method, vehicle and storage medium

Abstract

The present application provides a path planning method, a vehicle and a storage medium. The path planning method includes: obtaining the current position and target position of the current vehicle; obtaining the identified obstacles; identifying a number of vehicles traveling in the opposite direction from the obstacles according to preset rules, and constructing a virtual static obstacle using the identified vehicles traveling in the opposite direction; referring to the current position and target position of the current vehicle, and using the preset algorithm to generate a left turn planning path for the current vehicle. The solution provided by the present application can plan and generate a safer left turn path in the left turn intersection scene, thereby improving driving safety.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a path planning method, a vehicle, and a storage medium. Background Art

[0002] With the continuous development of smart car technology, smart cars generally have automatic driving functions.

[0003] In the application scenario of autonomous driving on urban roads, passing through an unprotected left turn intersection is a very common scenario. In this process, there is often dynamic traffic in the opposite direction. If the path planning of the vehicle's autonomous driving is not appropriate, the vehicle may enter the opposite traffic flow, which may cause danger. Figure 1 As shown, when the current vehicle 10 turns left at the left-turn intersection, the planned path conflicts with the path of the reverse traffic flow 20, which will cause risks. In the related art, free space graph search algorithms such as A* algorithm (also known as A star algorithm) and hybrid A* algorithm (Hybrid A* algorithm, also known as hybrid A star algorithm) are generally used to generate paths to avoid obstacles. However, these methods can only handle static obstacles, and cannot handle dynamic obstacles such as reverse traffic.

[0004] Therefore, the path planning method of the related art has shortcomings when planning the path of the left turn intersection. Summary of the invention

[0005] In order to solve or partially solve the problems existing in the related art, the present application provides a path planning method, a vehicle and a storage medium, which can plan and generate a safer left-turn path in a left-turn intersection scenario, thereby improving driving safety.

[0006] The first aspect of the present application provides a path planning method, comprising:

[0007] Get the current vehicle's current position and the current vehicle's target position;

[0008] Obtain identified obstacles;

[0009] Identifying a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and constructing a virtual static obstacle using the identified number of vehicles traveling in the opposite direction;

[0010] With reference to the current posture of the current vehicle, the current target posture of the current vehicle and the virtual static obstacle, a preset algorithm is used to generate a planned left turn path for the current vehicle.

[0011] In one embodiment, the identifying a plurality of vehicles traveling in the opposite direction from the obstacles according to a preset rule comprises:

[0012] An obstructing vehicle is identified from the obstacle, and according to the fact that the traveling direction of the obstructing vehicle is opposite to the traveling path direction of the current vehicle, the obstructing vehicle is identified as a vehicle traveling in the opposite direction.

[0013] In one embodiment, the obtaining of the current vehicle's current posture and the current vehicle's target posture includes:

[0014] The target position of the current vehicle is obtained based on the high-precision map information and navigation information of the current vehicle, and the current position of the current vehicle is obtained based on the positioning information of the current vehicle.

[0015] In one embodiment, the step of constructing a virtual static obstacle using the identified plurality of vehicles traveling in the opposite direction includes:

[0016] All identified vehicles traveling in the opposite direction are traversed, and based on the geometric boundaries of the vehicles traveling in the opposite direction, virtual static obstacles are extended to a set distance on the left side of the driving direction of the vehicles traveling in the opposite direction to construct a static obstacle map according to the virtual static obstacles.

[0017] In one embodiment, referring to the current posture of the current vehicle, the current target posture of the vehicle and the virtual static obstacle, and using a preset algorithm to generate a left turn planning path for the current vehicle to travel, includes:

[0018] Taking the current posture of the current vehicle as the starting point of the planned path, the current target posture of the current vehicle as the end point of the planned path, and the virtual static obstacles in the static obstacle map as the obstacles in the planned path, a hybrid A* algorithm is used to search for the planned path to obtain a left turn planned path for the current vehicle that meets the kinematic constraints.

[0019] A second aspect of the present application provides a vehicle, comprising:

[0020] A posture acquisition module is used to obtain the current posture of the vehicle and the target posture of the vehicle;

[0021] A perception module, used to obtain and identify obstacles;

[0022] A virtual static obstacle construction module, used to identify a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and construct a virtual static obstacle using the identified number of vehicles traveling in the opposite direction;

[0023] The path planning module is used to refer to the current position of the current vehicle, the current target position of the current vehicle and the virtual static obstacle, and use a preset algorithm to generate a left turn planning path for the current vehicle.

[0024] In one embodiment, the virtual static obstacle construction module includes:

[0025] The reverse vehicle identification module is used to identify an obstructing vehicle from the obstacle, and identify the obstructing vehicle as a reverse-traveling vehicle based on the fact that the traveling direction of the obstructing vehicle is opposite to the traveling path direction of the current vehicle.

[0026] In one embodiment, the virtual static obstacle construction module includes:

[0027] The static map construction module is used to traverse all identified vehicles traveling in the opposite direction, and based on the geometric boundaries of the vehicles traveling in the opposite direction, to construct virtual static obstacles extending a set distance to the left of the driving direction of the vehicles traveling in the opposite direction, and to construct a static obstacle map based on the virtual static obstacles.

[0028] In one embodiment, the path planning module uses the current position of the current vehicle as the starting point of the planned path, the current target position of the vehicle as the end point of the planned path, and the virtual static obstacles in the static obstacle map as obstacles in the planned path, and uses a hybrid A* algorithm to search for the planned path to obtain a left turn planned path for the current vehicle that meets kinematic constraints.

[0029] A third aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0030] The technical solution provided by this application has the following beneficial effects:

[0031] This application identifies several vehicles traveling in the opposite direction from obstacles according to preset rules, and uses the identified several vehicles traveling in the opposite direction to construct a virtual static obstacle; then, the current position of the current vehicle, the target position of the current vehicle, and the virtual static obstacle can be referred to, and a preset algorithm can be used to generate a planned left turn path for the current vehicle. Since the solution of this application is to staticize the dynamic problem and construct the dynamic reverse traffic flow as a static obstacle, a preset algorithm that can handle static obstacles can be used, such as a hybrid A* algorithm, to generate a collision-free driving path that meets kinematic constraints, and the driving path can be used as the planned left turn path for the current vehicle, so that given the starting position and target position of the current vehicle, that is, the self-vehicle, the reverse traffic can be avoided and the unprotected left turn intersection can be safely passed.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0034] Figure 1 It is a schematic diagram showing a conflict between a left-turn path of a vehicle and a path of a reverse traffic flow in the related art;

[0035] Figure 2 is a first flow chart of the path planning method shown in the present application;

[0036] Figure 3 is a second flow chart of the path planning method shown in the present application;

[0037] Figure 4 It is a schematic diagram of the application framework of the path planning method shown in this application;

[0038] Figure 5 is a schematic diagram of identifying a vehicle traveling in the opposite direction in the path planning method shown in the present application;

[0039] Figure 6 It is a schematic diagram comparing the current vehicle left turn path generated by the present application with the current vehicle left turn path generated by the related art;

[0040] Figure 7 is a structural schematic diagram of a vehicle shown in the present application;

[0041] Figure 8 It is another structural schematic diagram of the vehicle shown in the present application. DETAILED DESCRIPTION

[0042] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0043] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0044] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0045] The path planning method of the related art has shortcomings when planning the path of the left turn intersection. Figure 1 As shown, in the scenario of passing through an unprotected left turn intersection, when the current vehicle 10 turns left at the left turn intersection, the planned path conflicts with the path of the reverse traffic flow 20, which will cause risks. The present application provides a path planning method that can plan and generate a safer left turn path in the left turn intersection scenario to improve driving safety.

[0046] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0047] Figure 2 1 is a first flow chart of a path planning method shown in the present application. The method can be applied to vehicles.

[0048] See also Figure 2 , the method comprising:

[0049] S201. Obtain the current position and target position of the current vehicle.

[0050] Among them, the target position of the current vehicle can be obtained according to the high-precision map information and navigation information of the current vehicle, and the current position of the current vehicle can be obtained according to the positioning information of the current vehicle.

[0051] S202: Obtain identified obstacles.

[0052] The present application can utilize the recognition technology in the related art to identify obstacles from the images or videos collected by the perception module.

[0053] S203: Identify a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and construct a virtual static obstacle using the identified vehicles traveling in the opposite direction.

[0054] Among them, an obstructing vehicle can be identified from the obstacles, and according to the fact that the driving direction of the obstructing vehicle is opposite to the driving path direction of the current vehicle, the obstructing vehicle is identified as a reverse-traveling vehicle.

[0055] Among them, all identified vehicles traveling in the opposite direction can be traversed, and based on the geometric boundaries of the vehicles traveling in the opposite direction, a virtual static obstacle is constructed by extending a set distance to the left side of the driving direction of the vehicles traveling in the opposite direction, and a static obstacle map is constructed based on the virtual static obstacles. This application constructs the dynamic reverse traffic flow as a static obstacle, which can realize the staticization of dynamic problems.

[0056] S204, referring to the current position and posture of the current vehicle, the current target position and posture of the current vehicle and the virtual static obstacle, and using a preset algorithm to generate a left turn planning path for the current vehicle.

[0057] The current position of the current vehicle is taken as the starting point of the planned path, the current target position of the vehicle is taken as the end point of the planned path, and the virtual static obstacles in the static obstacle map are taken as the obstacles in the planned path. The hybrid A* algorithm is used to search for the planned path to obtain the left turn planned path of the current vehicle that meets the kinematic constraints.

[0058] The hybrid A* algorithm is an algorithm that takes into account the actual motion constraints of the object on the basis of the A* algorithm. The hybrid A* algorithm performs heuristic search in a continuous coordinate system and can ensure that the generated trajectory satisfies the kinematic constraints of the vehicle. The difference between the hybrid A* algorithm and the ordinary A* algorithm is that the path planned by the hybrid A* algorithm takes into account the kinematic constraints of the vehicle, that is, it satisfies the maximum curvature constraint of the vehicle.

[0059] This application identifies several vehicles traveling in the opposite direction from obstacles according to preset rules, and uses the identified vehicles traveling in the opposite direction to construct a virtual static obstacle; then, the current position of the current vehicle, the target position of the current vehicle, and the virtual static obstacle can be referred to, and a preset algorithm can be used to generate a planned left turn path for the current vehicle. Since the solution of this application is to staticize the dynamic problem and construct the dynamic reverse traffic flow as a static obstacle, a preset algorithm that can handle static obstacles can be used, such as a hybrid A* algorithm, to generate a collision-free driving path that satisfies kinematic constraints, and the driving path can be used as the planned left turn path for the current vehicle, so that given the starting position and target position of the current vehicle, that is, the self-vehicle, the reverse traffic flow can be avoided and the unprotected left turn intersection can be safely passed.

[0060] Figure 3 It is a second flow chart of the path planning method shown in the present application. Figure 4 It is a schematic diagram of the application framework of the path planning method shown in this application.

[0061] The present application provides an obstacle avoidance path planning method, which obtains the current vehicle target posture based on high-precision map information and navigation information, obtains the current vehicle current posture based on positioning information, and obtains all obstacle vehicle information from the perception module. On this basis, the vehicle traveling in the opposite direction can be judged according to the position and posture of the obstacle vehicle. On this basis, the dynamic problem is staticized, and a static obstacle map that takes into account the reverse traffic flow is established. Finally, a hybrid A* algorithm that can handle static obstacles is used to search for a collision-free left turn planning path that satisfies kinematic constraints. The hybrid A* algorithm is a graph search algorithm that is improved on the A* algorithm. The difference between it and the ordinary A* algorithm is that the path planned by the hybrid A* algorithm takes into account the kinematic constraints of the vehicle, that is, it satisfies the maximum curvature constraint of the vehicle.

[0062] Motion constraints are a type of constraints in dynamic systems. The constraint equations contain not only the coordinates of each particle, but also the velocity of each particle. Since the velocity of a particle is mathematically expressed as the differential of the particle coordinates with respect to time, it is also called a "differential constraint". The kinematic constraints of a vehicle generally refer to the lateral (y-axis) and normal (z-axis) velocities of a vehicle when it is driving normally on the road, unless it skids or jumps. The vehicle's motion is subject to kinematic constraints, so the vehicle cannot achieve instantaneous lateral movement. Front-wheel drive vehicles must rely on the steering of the front wheels to achieve operations such as changing lanes and turning, and cannot drive too fast on curves.

[0063] The hybrid A* algorithm is an improvement of the A* algorithm. The hybrid A* algorithm satisfies the kinematic constraints of the vehicle. In the hybrid A* algorithm, the Reed-Shepp curve or the Dubins curve can be used to connect with the target, and the body contour is added to determine whether it collides with an obstacle. If there is no collision with the obstacle, the curve is retained and a path is generated; if there is a collision with the obstacle, the curve is abandoned, and the node is re-searched from the open list and re-expanded. The Reeds-Shepp curve is composed of several arcs with fixed radii and a straight line segment, and the radius of the arc is the minimum turning radius of the vehicle. The Dubins curve is similar to the Reeds-Shepp curve, but with an additional constraint: the vehicle can only drive forward and cannot move backward (reverse gear cannot be engaged).

[0064] See also Figure 3 and Figure 4 , the method comprising:

[0065] S301. Obtain the current position and target position of the current vehicle based on high-precision map information, navigation information, and positioning information.

[0066] For example, the target position of the current vehicle is obtained based on the high-precision map information and navigation information of the current vehicle and recorded as X_t, and the current position of the current vehicle is obtained based on the positioning information of the current vehicle and recorded as X_e.

[0067] S302: Obtain identified obstacles through a perception module.

[0068] The present application can collect images or videos in front of or to the side of the current vehicle through the perception module of the current vehicle, and use the recognition technology in the relevant technology to identify obstacles from the images or videos collected by the perception module. The obstacle detection can be image-based obstacle detection, laser radar-based obstacle detection, or obstacle detection based on the fusion of vision and laser radar, etc., which is not limited by the present application.

[0069] S303: Identify a vehicle traveling in the opposite direction from obstacles.

[0070] Identify the obstacle vehicle from the obstacles, traverse all the obstacle vehicles identified from the perception module, and identify the obstacle vehicle as a reverse-traveling vehicle based on the fact that the driving direction of the obstacle vehicle is opposite to the driving path direction of the current vehicle.

[0071] For example, based on the current vehicle's current posture X_e, the current vehicle's target posture X_t and the identified obstacle vehicle, it is determined whether the obstacle vehicle is a reverse-traveling vehicle (also called a reverse obstacle vehicle) based on the geometric relationship and the orientation angle.

[0072] See also Figure 5 , assuming that the current vehicle is 501 and the obstacle vehicle is 502, the curve is the planned driving path 503 of the current vehicle 501. Since the driving direction of the obstacle vehicle 502 is opposite to the driving path direction of the current vehicle 501, it can be identified that the obstacle vehicle 502 is a reverse driving vehicle.

[0073] For example, the center point of the obstacle vehicle 502 can be projected onto the driving path 503 of the current vehicle 501 to find the projection point, and then the heading of the projection point can be obtained. If the heading angle of the projection point is opposite to the heading angle of the obstacle vehicle 502, it means that the obstacle vehicle 502 is a vehicle traveling in the opposite direction.

[0074] S304: construct a virtual static obstacle using the vehicle traveling in the opposite direction.

[0075] Traverse all the vehicles traveling in the opposite direction identified in the previous step, extend a set distance to the left of their direction of travel based on their geometric boundaries, and construct static virtual static obstacles. All these virtual static obstacles form a static obstacle map. This application constructs the dynamic opposite traffic flow as static obstacles, which can realize the staticization of dynamic problems.

[0076] S305 , referring to the current position and posture of the current vehicle, the current target position and posture of the current vehicle, and the virtual static obstacle, a hybrid A* algorithm is used to generate a left turn planning path for the current vehicle.

[0077] The current vehicle posture X_e is used as the starting point of the planned path, the current vehicle target posture X_t is used as the end point of the planned path, and according to the static obstacle map established in the previous step, the virtual static obstacles in the static obstacle map are used as obstacles in the planned path. Considering kinematic constraints and comfort, a set algorithm such as the hybrid A* algorithm is used to search for the planned path. Since the hybrid A* algorithm is an algorithm that satisfies vehicle kinematics, the hybrid A* algorithm can ultimately generate a drivable path that satisfies kinematic constraints and can avoid reverse traffic.

[0078] The current vehicle turns left at the left-turn intersection according to the generated drivable path, thereby avoiding the opposite traffic flow and improving driving safety.

[0079] For example, see Figure 6 , is a schematic diagram comparing the current vehicle left turn path generated by the present application and the current vehicle left turn path generated by the related technology.

[0080] The current vehicle position X_e and the current vehicle target position X_t are as follows: Figure 6 If the reverse traffic is not considered, the hybrid A* algorithm is directly used to search for the path according to the relevant technical method. The searched path is Figure 6 The blue path 101 is shown in the figure. However, this is obviously unreasonable because the blue path 101 rushes into the opposite traffic flow and intersects with the driving path of the obstacle vehicle, thus generating a conflict. Figure 6 The black box with an arrow in the middle represents the reverse-moving vehicles 602 of the reverse traffic flow that are screened out using geometric relationships based on the information provided by the perception module. Based on these reverse-moving vehicles 602, a set distance is stretched along the left side of their forward direction to create a rectangular virtual static obstacle, such as Figure 6 As shown in the middle gray filled cuboid 603, all these cuboids 603 form a static obstacle map. Finally, the current vehicle's current posture is used as the starting point of the planned path, the current vehicle's target posture is used as the end point of the planned path, and the virtual static obstacles in the static obstacle map are used as obstacles in the planned path. The hybrid A* algorithm is used to search for the planned path, and finally a left turn planned path 604 of the current vehicle 601 that satisfies the kinematic constraints and can avoid the reverse traffic is generated.

[0081] The present application solution staticizes dynamic problems and constructs dynamic reverse traffic flow as static obstacles. It can quickly generate a collision-free path from a specified posture to another specified posture in free space as a planned path, that is, generate a left-turn collision-free path from the current posture to the target posture, which can ensure safety while ensuring path comfort, thereby improving the efficiency of the autonomous driving system at unprotected left-turn intersections.

[0082] Corresponding to the aforementioned method for implementing the application function, the present application also provides a vehicle.

[0083] Figure 7 It is a schematic diagram of the structure of the vehicle shown in this application.

[0084] See also Figure 7 The vehicle 70 provided in the present application includes: a posture acquisition module 71, a perception module 72, a virtual static obstacle construction module 73, and a path planning module 74.

[0085] The posture acquisition module 71 is used to acquire the current posture of the current vehicle and the target posture of the vehicle. The posture acquisition module 71 can acquire the target posture of the current vehicle according to the high-precision map information and navigation information of the current vehicle, and acquire the current posture of the current vehicle according to the positioning information of the current vehicle.

[0086] The perception module 72 is used to obtain the identified obstacle. The perception module 72 can use the recognition technology in the relevant technology to identify the obstacle from the image or video collected by the perception module.

[0087] The virtual static obstacle construction module 73 is used to identify a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and construct a virtual static obstacle using the identified number of vehicles traveling in the opposite direction.

[0088] The path planning module 74 is used to refer to the current vehicle's current posture, the current vehicle's target posture and the virtual static obstacles, and use a preset algorithm to generate a left turn planning path for the current vehicle. The path planning module 74 uses the current vehicle's current posture as the starting point of the planning path, the current vehicle's target posture as the end point of the planning path, and the virtual static obstacles in the static obstacle map as the obstacles in the planning path. It uses a hybrid A* algorithm to search for the planning path and obtains a left turn planning path for the current vehicle that meets the kinematic constraints.

[0089] The virtual static obstacle construction module 73 may include: a reverse vehicle recognition module 731 and a static image construction module 732 .

[0090] The reverse vehicle identification module 731 is used to identify an obstructing vehicle from obstacles, and identify the obstructing vehicle as a reverse-traveling vehicle based on the fact that the driving direction of the obstructing vehicle is opposite to the driving path direction of the current vehicle.

[0091] The static map construction module 732 is used to traverse all identified vehicles traveling in the opposite direction, and based on the geometric boundaries of the vehicles traveling in the opposite direction, construct virtual static obstacles extending a set distance to the left of the driving direction of the vehicles traveling in the opposite direction, and construct a static obstacle map based on the virtual static obstacles.

[0092] The vehicle provided by the present application can identify several vehicles traveling in the opposite direction from obstacles according to preset rules, and use the identified several vehicles traveling in the opposite direction to construct a virtual static obstacle; then, the current position of the current vehicle, the target position of the current vehicle and the virtual static obstacle can be referred to, and a preset algorithm can be used to generate a left turn planning path for the current vehicle. Since the present application solution is to staticize the dynamic problem and construct the dynamic reverse traffic flow as a static obstacle, a preset algorithm that can handle static obstacles can be used, such as a hybrid A* algorithm, to generate a collision-free driving path that satisfies kinematic constraints, and the driving path can be used as the left turn planning path for the current vehicle, so that given the starting position and target position of the current vehicle, i.e., the self-vehicle, the reverse traffic flow can be avoided and the unprotected left turn intersection can be safely passed.

[0093] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0094] Figure 8 It is a schematic diagram of the structure of the vehicle shown in this application.

[0095] See also Figure 8 , vehicle 1000 includes a memory 1010 and a processor 1020 .

[0096] The processor 1020 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0097] The memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0098] The memory 1010 stores executable codes, and when the executable codes are processed by the processor 1020 , the processor 1020 can execute part or all of the methods described above.

[0099] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0100] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0101] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A path planning method, characterized in that: include: Get the current vehicle's current position and the current vehicle's target position; Obtain identified obstacles; Identifying a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and constructing a virtual static obstacle using the identified number of vehicles traveling in the opposite direction; Referring to the current position of the current vehicle, the current target position of the vehicle and the virtual static obstacle, a preset algorithm is used to generate a left turn planning path for the current vehicle; The step of constructing a virtual static obstacle by using the identified plurality of vehicles traveling in the opposite direction comprises: All identified vehicles traveling in the opposite direction are traversed, and based on the geometric boundaries of the vehicles traveling in the opposite direction, virtual static obstacles are extended to a set distance on the left side of the driving direction of the vehicles traveling in the opposite direction to construct a static obstacle map according to the virtual static obstacles.

2. The method according to claim 1, characterized in that The step of identifying a plurality of vehicles traveling in the opposite direction from the obstacles according to a preset rule comprises: An obstructing vehicle is identified from the obstacle, and according to the fact that the traveling direction of the obstructing vehicle is opposite to the traveling path direction of the current vehicle, the obstructing vehicle is identified as a vehicle traveling in the opposite direction.

3. The method according to claim 1, characterized in that The obtaining of the current vehicle's current posture and the current vehicle's target posture comprises: The target position of the current vehicle is obtained based on the high-precision map information and navigation information of the current vehicle, and the current position of the current vehicle is obtained based on the positioning information of the current vehicle.

4. The method according to claim 1, characterized in that: The step of referring to the current position of the current vehicle, the target position of the current vehicle and the virtual static obstacle and using a preset algorithm to generate a left turn planning path for the current vehicle includes: Taking the current posture of the current vehicle as the starting point of the planned path, the current target posture of the current vehicle as the end point of the planned path, and the virtual static obstacles in the static obstacle map as the obstacles in the planned path, a hybrid A* algorithm is used to search for the planned path to obtain a left turn planned path for the current vehicle that meets the kinematic constraints.

5. A vehicle, characterized in that: include: A posture acquisition module is used to obtain the current posture of the current vehicle and the current target posture of the current vehicle; A perception module, used to obtain and identify obstacles; A virtual static obstacle construction module, used to identify a number of vehicles traveling in the opposite direction from the obstacles according to a preset rule, and construct a virtual static obstacle using the identified number of vehicles traveling in the opposite direction; A path planning module, used to refer to the current position of the current vehicle, the current target position of the current vehicle and the virtual static obstacle, and use a preset algorithm to generate a left turn planning path for the current vehicle; The virtual static obstacle construction module includes: The static map construction module is used to traverse all identified vehicles traveling in the opposite direction, and based on the geometric boundaries of the vehicles traveling in the opposite direction, to construct virtual static obstacles extending a set distance to the left of the driving direction of the vehicles traveling in the opposite direction, and to construct a static obstacle map based on the virtual static obstacles.

6. The vehicle according to claim 5, characterized in that The virtual static obstacle construction module includes: The reverse vehicle identification module is used to identify an obstructing vehicle from the obstacle, and identify the obstructing vehicle as a reverse-traveling vehicle based on the fact that the traveling direction of the obstructing vehicle is opposite to the traveling path direction of the current vehicle.

7. The vehicle according to claim 5, characterized in that: The path planning module uses the current posture of the current vehicle as the starting point of the planned path, the current target posture of the vehicle as the end point of the planned path, and the virtual static obstacles in the static obstacle map as obstacles in the planned path, and uses a hybrid A* algorithm to search for the planned path to obtain a left turn planned path for the current vehicle that meets kinematic constraints.

8. A computer-readable storage medium having executable codes stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the method according to any one of claims 1 to 4.

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