Automatic parking reverse path planning method, device, equipment and medium

Abstract

The invention discloses an automatic parking reverse path planning method and device, equipment and a medium. The method comprises the steps that the maximum track range of a current vehicle driving in all directions based on a given starting pose is determined according to the vehicle performance; acquiring an obstacle grid map, and determining a target parking space position and a target vehicle pose in the obstacle grid map; taking a set position when the head of the vehicle is driven out of the parking space as a middle vehicle pose, taking the middle vehicle pose as a starting pose, superposing a maximum track range in the direction of the target vehicle pose on the barrier grid map, and analyzing to obtain a track range capable of avoiding the barrier; random sampling is carried out in the track range, path point selection is carried out from the middle vehicle pose, a reverse path planning sequence is obtained, and a forward path planning sequence is obtained through overturning. According to the invention, a path planning problem of parking demands in a complex environment can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic parking planning, and in particular relates to an automatic parking reverse path planning method, device, equipment and medium. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the acceleration of urbanization, the problem of parking difficulties has become increasingly prominent, especially in narrow or complex environments. Automatic parking systems face challenges in path planning efficiency and safety. Traditional automatic parking path planning methods mainly use forward path planning strategies, that is, starting from the current position of the vehicle, looking for a path to the target parking space. This method has significant limitations when dealing with complex environments (such as narrow alleys and areas with multiple obstacles). Forward path planning often requires real-time calculation and adjustment of the path, which has high requirements for computing resources, and in environments with many dynamic obstacles, the efficiency and reliability of path planning will be significantly reduced.

[0004] In recent years, the reverse optimization automatic parking path planning method has gradually attracted attention. The core of this method is to start from the target parking space and reversely build a path until it connects with the current position of the vehicle. Compared with forward planning, reverse planning has more advantages for scenes with complex parking environments. However, the current path planning lacks consideration of the dynamic characteristics of the vehicle, and the efficiency of path planning needs to be improved. Summary of the invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a method, device, equipment and medium for reverse path planning of automatic parking, which can more efficiently and intuitively handle the path planning problem of parking requirements in complex environments.

[0006] To achieve the above object, a first aspect of the present invention provides a reverse path planning method for automatic parking, comprising the following steps:

[0007] Determine the maximum trajectory range of the current vehicle when traveling in each direction based on a given starting position according to the vehicle performance;

[0008] Obtain an obstacle grid map and determine the target parking space position and target vehicle posture in the obstacle grid map;

[0009] The set position when the front of the vehicle drives out of the parking space is used as the intermediate vehicle posture, the intermediate vehicle posture is used as the starting posture, the maximum trajectory range in the direction of the target vehicle posture is superimposed on the obstacle grid map, and the trajectory range that can avoid obstacles is analyzed;

[0010] Random sampling is performed within the trajectory range, and the distance constraint to the obstacle and the shortest overall path are used as constraint conditions. Path points are selected from the middle vehicle posture to obtain a reverse path planning sequence, which is then flipped to obtain a forward path planning sequence.

[0011] Furthermore, the vehicle performance includes an optional range of a front wheel turning angle and a driving step length, and sampling is performed within the optional range of the front wheel turning angle and the driving step length of the current vehicle to obtain a plurality of combinations of front wheel turning angles and driving step lengths; based on each group of front wheel turning angles and driving step lengths, a vehicle running trajectory is generated in different driving directions to obtain a trajectory range in different driving directions.

[0012] Furthermore, according to the size of the vehicle and the maximum trajectory range of the vehicle when moving forward, the initial position of the adjustment direction that can drive out of the parking space without crossing the line is determined, and this is used as the setting position of the intermediate vehicle posture.

[0013] Furthermore, after obtaining the trajectory range that can avoid obstacles, it is also determined whether the trajectory range can cover the vehicle in the initial vehicle posture. If not, on the obstacle grid map, based on the last superimposed trajectory range, the maximum trajectory range of the vehicle is superimposed again, and it is ensured that: the trajectory range obtained after the second superposition and obstacle avoidance analysis can cover the vehicle in the initial vehicle posture, can avoid obstacles, intersects with the trajectory range obtained by the last superposition, and the intersection is larger than the set size of the vehicle; if a trajectory range that meets the above conditions is found, the trajectory ranges obtained by the two superpositions are merged as the range of random sampling.

[0014] Furthermore, if a trajectory range that meets the above conditions cannot be found, the intermediate vehicle posture is reset; if a trajectory range that meets the above conditions is still not found after multiple resets, it is considered that a direction switch is required.

[0015] Furthermore, the path point selection process is as follows: starting from the position corresponding to the intermediate vehicle posture and ending at the position corresponding to the initial vehicle posture, a search random tree is constructed based on the sampling points, and a search box is set. Each time, the nodes in a search box are searched, and the node that minimizes the cost function is selected as the next path point; the path point p i The cost function formula is:

[0016]

[0017] Among them, d(start,p i ) is the distance from the target pose to the path point p i The distance, d obs (p i ) is the path point p i The inverse of the distance to the nearest obstacle, is the vehicle's arrival point pi The deviation of the front wheel turning angle between the time when the last path point is reached and the time when the last path point is reached, d θ (p i ) is the vehicle's arrival path point p i The heading angle deviation from the target posture at time d xy (p i ) is the path point p i The distance deviation from the target pose, λ, ω θ ,ω xy are all weight coefficients.

[0018] A second aspect of the present invention provides an automatic parking reverse path planning device, comprising:

[0019] The vehicle trajectory range calculation module is configured to: determine the maximum trajectory range of the current vehicle when it travels in each direction based on a given starting position according to the vehicle performance;

[0020] The target vehicle posture determination module is configured to: obtain an obstacle grid map, and determine the target parking space position and the target vehicle posture in the obstacle grid map;

[0021] The sampling range determination module is configured to: take the set position when the front of the vehicle drives out of the parking space as the intermediate vehicle posture, take the intermediate vehicle posture as the starting posture, superimpose the maximum trajectory range in the direction of the target vehicle posture on the obstacle grid map, and analyze and obtain the trajectory range that can avoid obstacles;

[0022] The path point reverse optimization module is configured to: perform random sampling within the trajectory range, set the distance constraint to the obstacle and the shortest overall path as constraints, select path points from the intermediate vehicle posture, obtain a reverse path planning sequence, and flip it to obtain a forward path planning sequence.

[0023] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the method described.

[0024] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described when executed by a processor.

[0025] A fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the method described is implemented.

[0026] One or more of the above technical solutions determine the possible driving range of the vehicle according to the performance of the current vehicle, and then obtain the trajectory range that the vehicle can reach when driving out of the parking space under the premise of avoiding obstacles. The trajectory range is used as the sampling range. Starting from the target posture, sampling points are selected point by point based on the set constraints, and the optimal path is selected to expand and construct the path until the path reaches the initial posture of the vehicle, thereby realizing reverse path construction, which is particularly suitable for occasions where the space around the parking space is narrow. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is a flow chart of a reverse path planning method for automatic parking in an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of a portion of a vehicle path range in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain 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 construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0031] In the description of the embodiments of the present application, the term “including” and similar terms should be understood as open inclusion, that is, “including but not limited to.” The term “based on” should be understood as “based at least in part on.”

[0032] One or more embodiments of the present invention provide a reverse path planning method for automatic parking, which effectively improves the efficiency and safety of automatic parking by combining reverse path construction and optimization with iterative sampling and RRT* algorithm, and provides an innovative solution for solving parking needs in narrow or complex environments. Specifically, Figure 1 and 2 As shown, the method includes:

[0033] Step 1: Determine the maximum trajectory range of the current vehicle when it travels in all directions based on a given starting position according to the vehicle performance;

[0034] Step 2: Obtain the obstacle grid map and determine the target parking space position and target vehicle posture in the obstacle grid map;

[0035] Step 3: Taking the set position when the front of the vehicle drives out of the parking space as the intermediate vehicle posture, taking the intermediate vehicle posture as the starting posture, superimposing the maximum trajectory range in the direction of the target vehicle posture on the obstacle grid map, and analyzing to obtain the trajectory range that can avoid obstacles;

[0036] Step 4: Perform random sampling within the trajectory range, set the distance constraint to the obstacle and the shortest overall path as constraints, select path points starting from the middle vehicle posture, obtain the reverse path planning sequence, and flip it to obtain the forward path planning sequence.

[0037] By determining the possible driving range of the vehicle based on the current vehicle performance, and then obtaining the trajectory range that the vehicle can reach when driving out of the parking space under the premise of avoiding obstacles, the trajectory range is used as the sampling range, starting from the target posture, and selecting and constructing the path point by point based on the sampling points. Through iterative optimization, the optimal path is selected each time to expand until the path reaches or approaches the current posture of the vehicle, thus realizing the reverse path construction. It is particularly suitable for occasions where the space around the parking space is narrow. This process makes full use of the intuitiveness and efficiency of reverse planning, and significantly improves the flexibility and adaptability of path planning.

[0038] In step 1, the vehicle performance includes the front wheel angle and the optional range of the driving step length η. The front wheel steering angle and the optional range of the driving step length of the current vehicle are sampled to obtain multiple combinations of front wheel steering angles and driving step lengths; according to each group of front wheel steering angles and driving step lengths, the vehicle running tracks are generated in different driving directions to obtain the track ranges in different driving directions.

[0039] Specifically, when the driving direction D is fixed, the front wheel turning angle of the vehicle and driving step length η to form a set of control variables; generate different control variables The corresponding vehicle running trajectory; the generation of different control variables The corresponding vehicle trajectory is: The kinematic model of the Ackerman steering vehicle is used, and the second-order Runge-Kutta method is used to construct the vehicle state transfer equation:

[0040]

[0041] In the formula, a set of control variables input into the vehicle state transfer equation in the i-th iteration is recorded as

[0042]

[0043] p i+1 To use the control variable u iThe vehicle posture after the state transfer under control, including the lateral position x after the state transfer i+1 , vertical position y i+1 , heading angle θ i+1 ;p i is the vehicle posture before state transfer, including the lateral position x before state transfer i , vertical position y i , heading angle θ i ; D is the driving direction; L is the vehicle wheelbase.

[0044] Based on the vehicle's front wheel angle The maximum and minimum values ​​of the driving step length η can be used to obtain the trajectory boundaries of the vehicle under different driving directions, and then the trajectory range under different driving directions. Among them, different driving directions include left front, right front, left rear, and right rear.

[0045] It can be understood that when "trajectory" is mentioned in this article, it refers to a center point of the vehicle, such as the center of the rear bumper of the vehicle model, the center of the ellipse of the vehicle model, etc.

[0046] In the step 2, the obstacle grid map may be determined based on the surrounding environment data. In fact, the surrounding environment data may be acquired based on point cloud data or in other ways, which are not specifically limited here.

[0047] After obtaining the obstacle grid map and determining the target parking space position in the obstacle grid map, it is also determined based on the size of the current vehicle whether there is enough space left and right when the vehicle is parked in the target parking space. On this basis, the target vehicle posture of the vehicle in the parking space is determined. In general, the target vehicle posture is when the vehicle is parked in the middle of the parking space parallel to the side parking space line. In special cases, if there is an obstacle (such as other vehicles) pressing the line on one side, it can deviate from the center of the parking space if enough space can be reserved on the left and right.

[0048] The target pose is used as the planning starting point and is represented by coordinates (x, y) and angle θ to provide a clear starting point for subsequent reverse path planning.

[0049] In step 3, it is assumed that the state of the steering wheel is recorded as the initial state, and the three states of left and right based on the initial state. Generally speaking, whether it is backing into a parking space or parallel parking, the state of the steering wheel needs to be changed at least once. For example, when backing into a parking space, the steering wheel needs to be returned to the center position after the rear of the vehicle enters the parking space; when parallel parking, the steering wheel needs to be turned in the opposite direction after the vehicle enters the parking space. Based on this, in this embodiment, the position and posture of the steering wheel state adjustment after the rear of the vehicle enters the parking space is recorded as the intermediate vehicle posture.

[0050] For vertical and diagonal parking spaces, the intermediate vehicle posture is the posture when the front of the vehicle drives out of the parking space in the initial state of the steering wheel. For side parking spaces, the intermediate vehicle posture is the posture when the front of the vehicle drives out of the parking space at a certain angle to the target vehicle posture and points to the outside of the parking space.

[0051] As a specific implementation method, the initial position of the adjustment direction that can drive out of the parking space without crossing the line can be determined according to the vehicle size and the maximum trajectory range of the vehicle when it moves forward, and this can be used as the setting position of the intermediate vehicle posture.

[0052] As mentioned above, whether it is reversing into a garage or parallel parking, the state of the steering wheel needs to be changed at least once, and the driving direction may also need to be adjusted. Based on this, in step 4, it is first determined whether the trajectory range can cover the vehicle in the initial vehicle position.

[0053] If it can be covered, it is considered that the initial vehicle posture can be reached within the current trajectory range. The trajectory range is randomly sampled, and the distance constraint to the obstacle and the shortest overall path are used as constraints. Path points are selected from the middle vehicle posture to obtain a reverse path planning sequence, which is then flipped to obtain a forward path planning sequence.

[0054] If it cannot be covered, it is considered that the initial vehicle posture cannot be reached within the current trajectory range. In this case, as a specific implementation method, on the obstacle grid map, based on the last superimposed trajectory range, the maximum trajectory range of the vehicle is superimposed again, and it is ensured that: (1) the trajectory range obtained after superimposition and obstacle avoidance analysis can cover the vehicle under the initial vehicle posture, (2) it can avoid obstacles, and (3) it intersects with the trajectory range obtained by the last superposition, and the intersection is larger than the set size of the vehicle (to ensure that the vehicle can pass through the intersection). If a trajectory range that meets the above conditions is found, the trajectory ranges obtained by the two superpositions are merged, and random sampling is performed within the range. The distance constraint with the obstacle and the shortest overall path are used as constraints. Path points are selected from the middle vehicle posture to obtain a reverse path planning sequence, which is flipped to obtain a forward path planning sequence.

[0055] If there is no trajectory range that meets the above conditions, return to step 3 and reset the intermediate vehicle posture. If there is still no trajectory range that meets the above conditions after resetting multiple times, it is considered that direction switching is required. Since the space near the parking space is often narrow, not all positions meet the requirements for adjusting the direction. Therefore, first, according to the size of the vehicle and the maximum trajectory range of the vehicle when reversing, determine the adjustable space closest to the parking space and the vehicle's starting posture when adjusting in the adjustable space. The vehicle's starting posture is used as the second intermediate vehicle posture. On the obstacle grid map, the maximum trajectory range of the vehicle is superimposed to meet the following requirements: (1) The trajectory range obtained after superposition and obstacle avoidance analysis can cover the vehicle in the initial vehicle posture, and (2) it can avoid obstacles; Based on the obtained trajectory range, determine the target direction that the vehicle needs to adjust, and record the vehicle posture after adjusting the direction as the third intermediate vehicle posture. Random sampling is performed in the trajectory range between the first intermediate vehicle posture and the second intermediate vehicle posture, and between the third intermediate vehicle posture and the target vehicle posture, and path planning is performed based on the above method.

[0056] Understandably, in order to ensure the accuracy and success rate of the above solution and avoid excessive algorithm complexity, a reference distance between the vehicle and the target parking space can be given so that it is not too far. By limiting the path sampling points to the reachable range of the vehicle trajectory, it is ensured that the subsequent sampling points are all points that the vehicle can reach, thereby improving the success rate of path planning.

[0057] The above random sampling can be achieved by constructing a search tree or graph. Since these points are limited to the possible driving trajectory of the vehicle, the accuracy of path planning is guaranteed.

[0058] In step 4, the distance constraint to the obstacle and the shortest overall path are used as constraint conditions, and the path point is selected from the intermediate vehicle posture. Specifically, the RRT* algorithm can be used to ensure the efficiency and safety of the path. Specifically, the position corresponding to the intermediate vehicle posture is used as the starting point, and the position corresponding to the initial vehicle posture is used as the end point. A search random tree is constructed based on the sampling points, and a search box is set. Each time the nodes in a search box are searched, the node that minimizes the cost function is selected as the next path point. Specifically, the cost function of each point is defined as:

[0059]

[0060] Among them, C(p i ) is the path point p i The cost function, d(start,p i ) is the distance from the target pose to the path point p i distance, λ is the obstacle penalty coefficient, which is used to constrain path planning to ensure the shortest path; d obs (pi ) is the path point p i The reciprocal of the distance to the nearest obstacle, used to constrain the distance to the obstacle and keep the vehicle away from the obstacle as much as possible; is the weight of the front wheel angle change term, is the vehicle's arrival point p i The deviation of the front wheel turning angle between the time when the vehicle reaches the previous path point and the time when the vehicle reaches the previous path point; ω θ is the heading angle deviation weight, dθ(p i ) is the vehicle's arrival path point p i The heading angle deviation from the target posture at ω is used to make the vehicle's posture closer to the initial posture; xy is the weight of the vehicle position deviation term, d xy (p i ) is the path point p i By optimizing the cost function, the present invention can generate a parking path that is both efficient and safe.

[0061] By adjusting the weight ω θ ,ω xy , the cost function of the path can be optimized. Specifically, the closer to the initial vehicle posture, the more the heading angle deviation weight ω is increased. θ The cost function takes multiple factors into consideration at the same time, eliminating the need for additional collision detection, and can simultaneously approach the initial vehicle position in terms of position and attitude, thus improving the efficiency of path planning.

[0062] During the path planning process, the vehicle posture gradually approaches the initial posture by adjusting each path point based on the shortest distance to the target position, getting closer to the initial position, the smallest adjustment of the front wheel angle, and the heading angle getting closer to the initial posture.

[0063] The optimized path is output in reverse, and combined with the target vehicle posture and the intermediate vehicle posture, the parking trajectory from the current vehicle posture to the target parking posture is obtained to achieve automatic parking.

[0064] It is understandable that further verification can be performed based on the obtained path, and smooth operation can be performed within a reasonable range to avoid discomfort caused by frequent adjustments of direction in a short period of time.

[0065] Based on the above method, one or more embodiments of the present invention further provide an automatic parking reverse path planning device, comprising:

[0066] The vehicle trajectory range calculation module is configured to: determine the maximum trajectory range of the current vehicle when it travels in each direction based on a given starting position according to the vehicle performance;

[0067] The target vehicle posture determination module is configured to: obtain an obstacle grid map, and determine the target parking space position and the target vehicle posture in the obstacle grid map;

[0068] The sampling range determination module is configured to: take the set position when the front of the vehicle drives out of the parking space as the intermediate vehicle posture, take the intermediate vehicle posture as the starting posture, superimpose the maximum trajectory range in the direction of the target vehicle posture on the obstacle grid map, and analyze and obtain the trajectory range that can avoid obstacles;

[0069] The path point reverse optimization module is configured to: perform random sampling within the trajectory range, set the distance constraint to the obstacle and the shortest overall path as constraints, select path points from the intermediate vehicle posture, obtain a reverse path planning sequence, and flip it to obtain a forward path planning sequence.

[0070] One or more embodiments of the present invention further provide an electronic device that can be used to implement the automatic parking reverse path planning method in the above embodiments. The electronic device includes one or more processors, one or more memories coupled to the processors, and a communication module coupled to the processors.

[0071] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD) or other magnetic storage and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM), or other volatile memory that does not last during the power outage duration. The computer program may be stored in the ROM. When the processor executes the computer program, the above-mentioned automatic parking reverse path planning method is implemented.

[0072] In some embodiments, the program may be tangibly contained in a computer-readable medium, which may be included in a device (such as in a memory) or other storage device accessible by the device. The program may be loaded from the computer-readable medium to the RAM for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, and the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned automatic parking reverse path planning method.

[0073] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a server or terminal, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a server or terminal or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (such as a floppy disk, a hard disk and a tape, etc.), an optical medium (such as a digital video disk (digital video disk, DVD), etc.), or a semiconductor medium (such as a solid-state hard disk, etc.).

[0074] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0075] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A reverse path planning method for automatic parking, characterized in that: The following steps are involved: Determine the maximum trajectory range of the current vehicle when traveling in each direction based on a given starting position according to the vehicle performance; Obtain an obstacle grid map and determine the target parking space position and target vehicle posture in the obstacle grid map; The set position when the front of the vehicle drives out of the parking space is used as the intermediate vehicle posture, the intermediate vehicle posture is used as the starting posture, the maximum trajectory range in the direction of the target vehicle posture is superimposed on the obstacle grid map, and the trajectory range that can avoid obstacles is analyzed; Random sampling is performed within the trajectory range, and the distance constraint to the obstacle and the shortest overall path are used as constraint conditions. Path points are selected from the middle vehicle posture to obtain a reverse path planning sequence, which is then flipped to obtain a forward path planning sequence.

2. The automatic parking reverse path planning method according to claim 1, characterized in that: The vehicle performance includes an optional range of the front wheel turning angle and the driving step length. Sampling is performed within the optional range of the front wheel turning angle and the driving step length of the current vehicle to obtain multiple combinations of front wheel turning angle and driving step length. According to each group of front wheel turning angle and driving step length, the vehicle running trajectory is generated in different driving directions to obtain the trajectory range in different driving directions.

3. The automatic parking reverse path planning method according to claim 1, characterized in that: According to the size of the vehicle and the maximum trajectory range of the vehicle when moving forward, the initial position of the adjustment direction that can drive out of the parking space without crossing the line is determined, and this is used as the setting position of the intermediate vehicle posture.

4. The automatic parking reverse path planning method according to claim 1, characterized in that: After obtaining the trajectory range that can avoid obstacles, it is also determined whether the trajectory range can cover the vehicle in the initial vehicle posture. If not, on the obstacle grid map, based on the last superimposed trajectory range, the maximum trajectory range of the vehicle is superimposed again, and it is ensured that: the trajectory range obtained after the second superposition and obstacle avoidance analysis can cover the vehicle in the initial vehicle posture, can avoid obstacles, intersects with the trajectory range obtained by the last superposition, and the intersection is larger than the set size of the vehicle; if a trajectory range that meets the above conditions is found, the trajectory ranges obtained by the two superpositions are merged as the range of random sampling.

5. The automatic parking reverse path planning method according to claim 1, characterized in that: If the trajectory range that meets the above conditions cannot be found, the intermediate vehicle posture is reset; if the trajectory range that meets the above conditions is still not found after multiple resets, it is considered that a direction switch is required.

6. The automatic parking reverse path planning method according to claim 1, characterized in that: The path point selection process is as follows: starting from the position corresponding to the intermediate vehicle posture and ending at the position corresponding to the initial vehicle posture, a search random tree is constructed based on the sampling points, and a search box is set. Each time, the nodes in a search box are searched, and the node with the smallest cost function is selected as the next path point. i The cost function formula is: Among them, d(start, p i ) is the distance from the target pose to the path point p i The distance, d obs (p i ) is the path point p i The inverse of the distance to the nearest obstacle, is the vehicle's arrival path point p i The deviation of the front wheel turning angle between the time when the last path point is reached and the time when the last path point is reached, d θ (p i ) is the vehicle's arrival path point p i The heading angle deviation from the target posture at time d xy (p i ) is the path point p i The distance deviation from the target pose, λ, ω θ ,ω xy are all weight coefficients.

7. An automatic parking reverse path planning device, characterized in that: include: The vehicle trajectory range calculation module is configured to: determine the maximum trajectory range of the current vehicle when it travels in each direction based on a given starting position according to the vehicle performance; The target vehicle posture determination module is configured to: obtain an obstacle grid map, and determine the target parking space position and the target vehicle posture in the obstacle grid map; The sampling range determination module is configured to: take the set position when the front of the vehicle drives out of the parking space as the intermediate vehicle posture, take the intermediate vehicle posture as the starting posture, superimpose the maximum trajectory range in the direction of the target vehicle posture on the obstacle grid map, and analyze and obtain the trajectory range that can avoid obstacles; The path point reverse optimization module is configured to: perform random sampling within the trajectory range, set the distance constraint to the obstacle and the shortest overall path as constraints, select path points from the intermediate vehicle posture, obtain a reverse path planning sequence, and flip it to obtain a forward path planning sequence.

8. An electronic device comprising a processor and a memory, wherein the memory stores computer instructions, characterized in that: When the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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