Automatic parking path planning method and system and parking control equipment

文档序号：161751 发布日期：2021-10-29 浏览：19次中文

阅读说明：本技术 一种自动泊车路径规划方法及系统、泊车控制设备 (Automatic parking path planning method and system and parking control equipment ) 是由黄辉翁茂楠王玉龙李智陈泽武苏威霖于 2020-04-28 设计创作，主要内容包括：本发明涉及一种自动泊车路径规划方法及其系统、泊车控制设备,所述方法包括：周期性地获取当前车辆状态、车辆周围空闲车位在世界坐标系中的车位坐标信息；所述车辆状态包括车辆在世界坐标系中的车辆坐标信息及姿态；基于当前车辆状态采用多层级树状搜索方式进行泊车路径搜索,判定每一树节点所对应的车辆状态是否满足预设泊入条件,并根据判定结果获得与车位中位线带中任一条线相切的若干泊车路径；其中所述预设泊入条件为在树节点所对应的车辆状态下是否存在一条泊车路径与空闲车位的车位中位线带中任一条线相切；根据预设筛选规则对所述若干泊车路径进行筛选得到最优路径。实施本发明,能够实现动态路径规划,提高自动泊车路径规划的灵活性、鲁棒性和适应力。(The invention relates to an automatic parking path planning method, an automatic parking path planning system and a parking control device, wherein the method comprises the following steps: the method comprises the steps of periodically obtaining the current vehicle state and parking space coordinate information of free parking spaces around the vehicle in a world coordinate system; the vehicle state comprises vehicle coordinate information and a posture of the vehicle in a world coordinate system; the method comprises the steps that parking path searching is conducted in a multi-level tree searching mode based on the current vehicle state, whether the vehicle state corresponding to each tree node meets a preset parking condition or not is judged, and a plurality of parking paths tangent to any line in a median strip in a parking space are obtained according to the judgment result; the preset parking condition is that whether a parking path is tangent to any line in a parking space median line of an idle parking space or not in the vehicle state corresponding to the tree node; and screening the parking paths according to a preset screening rule to obtain an optimal path. By implementing the method and the system, dynamic path planning can be realized, and the flexibility, robustness and adaptability of automatic parking path planning are improved.)

1. An automatic parking path planning method, comprising:

step S1, parking space coordinate information of the current vehicle state and the free parking spaces around the vehicle in the world coordinate system is periodically acquired; the vehicle state comprises vehicle coordinate information and a posture of the vehicle in a world coordinate system;

step S2, based on the current vehicle state, adopting a multi-level tree search mode to search parking paths, judging whether the vehicle state corresponding to each tree node meets a preset parking condition, and obtaining a plurality of parking paths tangent to any line in the median strip of the parking space according to the judgment result; the preset parking condition is that whether a parking path is tangent to any line in a parking space median line of an idle parking space or not in the vehicle state corresponding to the tree node;

and step S3, screening the parking paths according to a preset screening rule to obtain an optimal path.

2. The automatic parking path planning method according to claim 1, wherein the step S2 specifically includes:

performing first-layer search based on the current vehicle state, wherein the first layer comprises n first-layer tree nodes, each first-layer tree node represents the vehicle state of the vehicle after the vehicle executes a preset driving action, and whether the vehicle state corresponding to each first-layer tree node meets a preset parking condition is judged;

if the vehicle state corresponding to a certain first-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the median strip of the parking space and corresponds to the first-layer tree node according to the judgment result;

if the vehicle state corresponding to a certain first-layer tree node does not meet the preset parking condition, performing second-layer search based on the vehicle state corresponding to the first-layer tree node, and judging whether the vehicle state corresponding to each second-layer tree node meets the preset parking condition, if the vehicle state corresponding to a certain second-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the parking space median strip and corresponds to the second-layer tree node according to a judgment result; wherein: the first layer of tree nodes correspond to n second layer of tree nodes, and each second layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

3. The automatic parking path planning method according to claim 2, wherein the step S2 further includes:

if the vehicle states corresponding to all the tree nodes of the second layer do not meet the preset parking condition, performing third-layer search based on the vehicle states corresponding to all the tree nodes of the second layer, and judging whether the vehicle state corresponding to each third-layer tree node meets the preset parking condition, if the vehicle state corresponding to one third-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the parking space median line and corresponds to the third-layer tree node according to a judgment result; wherein: the second layer of tree nodes correspond to n third layer of tree nodes, and each third layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

4. The automated parking path planning method according to claim 3, further comprising:

and step S4, generating a driving instruction of the current period according to the optimal path and the preset driving actions corresponding to the first-layer tree nodes, or the first-layer tree nodes and the second-layer tree nodes, or the first-layer tree nodes, the second-layer tree nodes and the third-layer tree nodes corresponding to the optimal path, and sending the driving instruction to a vehicle driving executing mechanism.

5. The automatic parking path planning method according to claim 4, wherein the step S4 further includes:

if the optimal path in the previous period comprises multiple paths, and when the optimal path in the current period is generated, the vehicle driving executing mechanism does not execute the driving instruction corresponding to the optimal path in the previous period, the driving instruction in the current period is generated according to the preset driving action corresponding to the tree node corresponding to the optimal path in the current period and the preset driving action corresponding to the tree node corresponding to the path in the previous period, and the driving instruction is sent to the vehicle driving executing mechanism.

6. The automatic parking path planning method according to claim 1, wherein the step S3 specifically includes:

acquiring coordinate information of a travelable area in a world coordinate system;

judging whether the parking paths meet preset drivable conditions or not according to the coordinate information of the drivable area in a world coordinate system; the preset travelable condition is that when the vehicle travels according to the parking path, a plurality of preset position points of the vehicle body do not exceed the travelable area;

if at least one parking path meets the preset drivable condition, scoring the parking paths meeting the preset drivable condition according to a first preset scoring rule, and outputting the parking path with the highest score as an optimal path;

and if at least one parking path does not meet the preset drivable condition, scoring all parking paths according to a second preset scoring rule, and outputting the parking path with the highest score as the optimal path.

7. The automatic parking path planning method according to claim 6, wherein the obtaining of the parking space coordinate information of the free parking spaces around the vehicle in the world coordinate system specifically comprises:

the method comprises the steps of obtaining environment images of a plurality of paths of vehicle-mounted fisheye cameras, carrying out image recognition on the plurality of paths of environment images to obtain parking space coordinate information of free parking spaces around a vehicle in a camera coordinate system, and obtaining parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system according to the parking space coordinate information of the free parking spaces around the vehicle in the camera coordinate system.

8. The automatic parking path planning method according to claim 6, wherein determining whether the parking paths satisfy a preset travelable condition specifically includes:

calculating i of the vehicle body₁A first preset position point and the feasible pointDistance to a boundary of a driving region;

if the car body i₁If the distance between each first preset position point and the boundary of the travelable area is greater than or equal to the first distance, judging whether the parking paths meet preset travelable conditions or not;

if the car body i₁If the distance between the first preset position point and the boundary of the travelable area is less than the first distance, i is calculated₂The distance between a second preset position point and the boundary of the travelable area is determined according to the distance from the second preset position point to the boundary of the travelable area₂Judging whether the parking paths meet preset travelable conditions or not according to the distance between the second preset position points and the boundary of the travelable area;

if the car body i₁If the distance between the first preset position point and the boundary of the travelable area is less than the second distance, i is calculated₂A second predetermined position point, i₂The distance between a third preset position point and the boundary of the travelable area is determined according to the distance from the third preset position point to the boundary of the travelable area₂A second predetermined position point, i₃Judging whether the parking paths meet preset travelable conditions or not according to the distance between the third preset position point and the boundary of the travelable area;

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

9. The automatic parking path planning method according to claim 6, wherein the acquiring coordinate information of the travelable region in the world coordinate system specifically includes:

and obtaining a panoramic image of the surrounding environment of the vehicle according to the environment image of the multi-channel vehicle-mounted fisheye camera, performing image semantic segmentation on the panoramic image to obtain coordinate information of a travelable area in a panoramic image coordinate system, and obtaining coordinate information of the travelable area in a world coordinate system according to the coordinate information of the travelable area in the panoramic image coordinate system.

10. The automatic parking path planning method according to claim 6, wherein the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

wherein V represents a route score, L represents a route length, D represents the number of turns of a route multi-segment, W represents the sum of steering wheel angles of the route multi-segment, N represents the closest distance of a vehicle body of a vehicle execution route to the edge of a travelable area, and D_lIndicating the distance of the vehicle to the parking space after the execution of the path, D_aIndicating the angular difference between the vehicle and the parking space after the execution of the path, W_a、W_b、W_c、W_d、W_e、W_fRespectively, are preset weight values.

11. An automated parking path planning system, comprising:

the information acquisition unit is used for periodically acquiring the current vehicle state and the parking space coordinate information of the free parking spaces around the vehicle in the world coordinate system; the vehicle state comprises vehicle coordinate information and a posture of the vehicle in a world coordinate system;

the route searching unit is used for searching parking routes by adopting a multi-level tree searching mode based on the current vehicle state, judging whether the vehicle state corresponding to each tree node meets a preset parking condition or not, and obtaining a plurality of parking routes tangent to any line in the median strip of the parking space according to a judgment result; the preset parking condition is that whether a parking path is tangent to any line in a parking space median line of an idle parking space or not in the vehicle state corresponding to the tree node; and

and the path screening unit is used for screening the parking paths according to a preset screening rule to obtain an optimal path.

12. The automatic parking path planning system according to claim 11, wherein the path search unit specifically includes:

the first search unit is used for performing first-layer search based on the current vehicle state, the first layer comprises n first-layer tree nodes, each first-layer tree node represents the vehicle state of the vehicle after the vehicle executes a preset driving action, and whether the vehicle state corresponding to each first-layer tree node meets a preset parking condition is judged;

the first path obtaining unit is used for obtaining a parking path which corresponds to a certain first-layer tree node and is tangent to any line in a parking space median strip according to a judgment result when a vehicle state corresponding to the first-layer tree node meets a preset parking condition;

the second search unit is used for performing second-layer search based on the vehicle state corresponding to a certain first-layer tree node when the vehicle state corresponding to the first-layer tree node does not meet the preset parking condition, and judging whether the vehicle state corresponding to each second-layer tree node meets the preset parking condition or not, wherein the first-layer tree node corresponds to n second-layer tree nodes, and each second-layer tree node represents the vehicle state after the vehicle executes a preset driving action; and

and the second path acquisition unit is used for acquiring a parking path which corresponds to a certain second-layer tree node and is tangent to any line in the parking space median line according to the judgment result when the vehicle state corresponding to the second-layer tree node meets the preset parking condition.

13. The automatic parking path planning system according to claim 12, wherein the path search unit specifically includes:

the third path obtaining unit is used for performing third-layer search based on the vehicle states corresponding to all the tree nodes on the second layer when the vehicle states corresponding to all the tree nodes on the second layer do not meet the preset parking condition, judging whether the vehicle state corresponding to each third-layer tree node meets the preset parking condition or not, and if the vehicle state corresponding to one third-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the parking space median line zone and corresponds to the third-layer tree node according to a judgment result; wherein: the second layer of tree nodes correspond to n third layer of tree nodes, and each third layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

14. The automated parking path planning system of claim 13, further comprising:

and the driving control unit is used for generating a driving instruction of the current period according to the optimal path and the preset driving actions corresponding to the first-layer tree nodes, or the first-layer tree nodes and the second-layer tree nodes, or the first-layer tree nodes, the second-layer tree nodes and the third-layer tree nodes corresponding to the optimal path, and sending the driving instruction to the vehicle driving executing mechanism.

15. The automated parking path planning system according to claim 14, wherein the vehicle control unit is further configured to:

when the optimal path in the previous period comprises multiple paths and the optimal path in the current period is generated, the vehicle driving executing mechanism does not execute the driving instruction corresponding to the optimal path in the previous period, the driving instruction in the current period is generated according to the preset driving action corresponding to the tree node corresponding to the optimal path in the current period and the preset driving action corresponding to the tree node corresponding to the path in the previous period, and the driving instruction is sent to the vehicle driving executing mechanism.

16. The automatic parking path planning system according to claim 11, wherein the path filtering unit specifically includes:

the driving area acquisition unit is used for acquiring coordinate information of a drivable area in a world coordinate system;

the driving judging unit is used for judging whether the parking paths meet preset driving conditions or not according to the coordinate information of the drivable region in the world coordinate system; the preset travelable condition is that when the vehicle travels according to the parking path, a plurality of preset position points of the vehicle body do not exceed the travelable area;

the first path optimization unit is used for scoring the parking paths meeting the preset drivable condition according to a first preset scoring rule and outputting the parking path with the highest score as an optimal path when at least one parking path meets the preset drivable condition; and

and the second path optimization unit is used for scoring all parking paths according to a second preset scoring rule and outputting the parking path with the highest score as the optimal path when at least one parking path does not meet the preset travelable condition.

17. The automatic parking path planning system according to claim 16, wherein the information obtaining unit specifically includes:

a vehicle information acquisition unit for acquiring a current vehicle state; and

and the parking space information acquisition unit is used for acquiring environment images of the plurality of paths of vehicle-mounted fisheye cameras, carrying out image recognition on the plurality of paths of environment images to obtain parking space coordinate information of free parking spaces around the vehicle in a camera coordinate system, and acquiring parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system according to the parking space coordinate information of the free parking spaces around the vehicle in the camera coordinate system.

18. The automatic parking path planning method according to claim 16, wherein the travel determination unit specifically includes:

a distance calculation unit for calculating i of the vehicle body according to the coordinate information of the travelable region in the world coordinate system₁The distance between each first preset position point and the boundary of the travelable area;

a first determination unit for determining i of the vehicle body₁When the distance between the first preset position point and the boundary of the travelable area is greater than or equal to the first distance, judging whether the parking paths meet the preset travelable area or notConditions;

a second determination unit for determining i of the vehicle body₁When the distance between the first preset position point and the boundary of the travelable area is less than the first distance, calculating i₂The distance between a second preset position point and the boundary of the travelable area is determined according to the distance from the second preset position point to the boundary of the travelable area₂Judging whether the parking paths meet preset travelable conditions or not according to the distance between the second preset position points and the boundary of the travelable area; and

a third determination unit for determining i of the vehicle body₁When the distance between the first preset position point and the boundary of the travelable area is less than the second distance, calculating i₂A second predetermined position point, i₂The distance between a third preset position point and the boundary of the travelable area is determined according to the distance from the third preset position point to the boundary of the travelable area₂A second predetermined position point, i₃Judging whether the parking paths meet preset travelable conditions or not according to the distance between the third preset position point and the boundary of the travelable area;

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

19. The automated parking path planning system according to claim 16, wherein the travel area acquisition unit is specifically configured to:

20. The automatic parking path planning system according to claim 16, wherein the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

21. A parking control apparatus characterized by comprising: the automated parking path planning system according to any one of claims 11-20; alternatively, a memory and a processor, the memory having stored therein computer readable instructions, which when executed by the processor, cause the processor to execute the automated parking path planning method according to any one of claims 1 to 10.

Technical Field

The invention relates to the technical field of automatic parking path planning, in particular to an automatic parking path planning method and system and a parking control device.

Background

Route planning is an important function in automated parking technology. The current automatic parking path planning algorithm generally adopts a geometric planning method, and comprises known college project schemes, schemes of most automobile related enterprises, and even schemes of marketed automobiles with automatic parking mostly adopt the geometric planning method.

In implementing the present invention, the inventors found that the most used path planning schemes based on geometric mapping currently have the following disadvantages:

the planning ability is not enough, and the robustness is not strong, can't deal with all scenes, and each kind of scene or different grade type parking stall all need write corresponding geometric planning strategy, because the planning ability is not enough, hardly let the car realize the path planning in optional position gesture, leads to the difficult dynamic path planning that realizes of geometric mode, mostly can only adopt static planning.

Disclosure of Invention

The invention aims to provide an automatic parking path planning method and system and a parking control device, so as to realize dynamic path planning and improve the flexibility, robustness and adaptability of automatic parking path planning.

In a first aspect, the present invention provides an automatic parking path planning method, including:

and step S3, screening the parking paths according to a preset screening rule to obtain an optimal path.

Preferably, the step S2 specifically includes:

Preferably, the step S2 specifically further includes:

Preferably, the method further comprises:

Preferably, the step S4 specifically further includes:

Preferably, the step S3 specifically includes:

acquiring coordinate information of a travelable area in a world coordinate system;

Preferably, the method for acquiring the parking space coordinate information of the free parking spaces around the vehicle in the world coordinate system specifically comprises the following steps:

Preferably, the determining whether the parking paths satisfy a preset travelable condition specifically includes:

calculating i of the vehicle body₁The distance between each first preset position point and the boundary of the travelable area;

if the car body i₁If the distance between the first preset position point and the boundary of the travelable area is less than the second distance, i is calculated₂A second predetermined position point, i₂The distance between a third preset position point and the boundary of the travelable area is determined according to the distance from the third preset position point to the boundary of the travelable area₂A second predetermined position point, i₃Determining the number of berths by the distance between a third predetermined location point and the boundary of the travelable regionWhether the vehicle path meets a preset travelable condition or not;

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

Preferably, the acquiring of the coordinate information of the travelable area in the world coordinate system specifically includes:

Preferably, the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

In a second aspect, the present invention further provides an automatic parking path planning system, including:

and the path screening unit is used for screening the parking paths according to a preset screening rule to obtain an optimal path.

Preferably, the path searching unit specifically includes:

Preferably, the system further comprises:

and the driving control unit is used for generating a driving instruction of the current period according to the optimal path and the first layer of tree nodes corresponding to the optimal path or the preset driving actions corresponding to the first layer of tree nodes and the second layer of tree nodes, and sending the driving instruction to the vehicle driving executing mechanism.

Preferably, the driving control unit is further configured to:

Preferably, the path screening unit specifically includes:

the driving area acquisition unit is used for acquiring coordinate information of a drivable area in a world coordinate system;

Preferably, the information acquiring unit specifically includes:

a vehicle information acquisition unit for acquiring a current vehicle state; and

the parking space information acquisition unit is used for acquiring environment images of the plurality of paths of vehicle-mounted fisheye cameras, carrying out image recognition on the plurality of paths of environment images to obtain parking space coordinate information of free parking spaces around the vehicle in a camera coordinate system, and acquiring parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system according to the parking space coordinate information of the free parking spaces around the vehicle in the camera coordinate system;

preferably, the travel determination unit specifically includes:

a first determination unit for determining i of the vehicle body₁When the distance between each first preset position point and the boundary of the travelable area is greater than or equal to a first distance, judging whether the parking paths meet preset travelable conditions or not;

a second determination unit for determining i of the vehicle body₁The distance between the first preset position point and the boundary of the travelable area is less than the first preset position pointWhen the distance is over, calculate i₂The distance between a second preset position point and the boundary of the travelable area is determined according to the distance from the second preset position point to the boundary of the travelable area₂Judging whether the parking paths meet preset travelable conditions or not according to the distance between the second preset position points and the boundary of the travelable area; and

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

Preferably, the driving area obtaining unit is specifically configured to:

Preferably, the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

wherein V represents a route score, L represents a route length, D represents a number of turns of a route multi-segment, W represents a sum of steering wheel angles of the route multi-segment, and N represents a vehicleDistance of vehicle body of vehicle execution path to nearest edge of travelable area, D_lIndicating the distance of the vehicle to the parking space after the execution of the path, D_aIndicating the angular difference between the vehicle and the parking space after the execution of the path, W_a、W_b、W_c、W_d、W_e、W_fRespectively, are preset weight values.

In a third aspect, the present invention also provides a parking control apparatus comprising: the automatic parking path planning system according to the first aspect; or, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the automatic parking path planning method according to the second aspect.

The technical scheme at least has the following advantages: according to a preset time period, parking space coordinate information of a current vehicle state and free parking spaces around the vehicle in a world coordinate system is periodically obtained, then a multi-level tree-shaped searching mode is adopted to search parking paths based on the current vehicle state, specifically, a preset driving action is executed in the current vehicle state to obtain a new tree node, whether the vehicle state corresponding to each tree node meets a preset parking condition or not is judged, the preset parking condition is that whether a parking path is tangent to any line in a parking space median line zone of the free parking space or not exists in the vehicle state corresponding to the tree node or not is judged, and a corresponding parking path can be obtained if the preset parking condition is met, so that a plurality of parking paths tangent to any line in the parking space median line zone are obtained according to a judgment result; and finally, screening the parking paths obtained in the previous step according to a preset screening rule (such as the simplicity of the prior parking operation) to obtain an optimal path. It should be noted that, in the above technical scheme, the path planning capability is stronger through a multi-layer search method, and the method is applicable to planning any vehicle attitude at any position, and dynamic path planning can be realized by periodically obtaining an optimal path, so that the method has strong universality and scene adaptability, can eliminate the problem of identification precision caused by single identification, and accumulated errors generated by self-positioning, greatly reduces the precision requirement on each link, and enables the whole system to have strong robustness.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of an automatic parking path planning method according to an embodiment of the present invention.

FIG. 2 is a flow chart of path search according to an embodiment of the present invention.

FIG. 3 is a tree structure diagram in path search according to an embodiment of the present invention.

FIG. 4 is a diagram illustrating a scenario where a vehicle status of a tree node satisfies a predetermined parking condition according to an embodiment of the present invention.

Fig. 5 is a schematic diagram illustrating a scenario where a vehicle state of a tree node does not satisfy a predetermined parking condition according to an embodiment of the present invention.

Fig. 6 is a flowchart of an automatic parking path planning method according to another embodiment of the present invention.

Fig. 7 is a schematic view illustrating a scenario in which a dynamically planned path cannot be completely consistent in an application example of the present invention.

Fig. 8 is a detailed information diagram of the optimal path searched in the previous cycle in an application example of the present invention.

Fig. 9 is a schematic view of a scenario of dynamic planned path consistency in an application example of the present invention.

FIG. 10 is a schematic diagram of a vehicle in a drivable region in accordance with an embodiment of the present invention.

Fig. 11 is a schematic view of a scenario in which a vehicle body exceeds a drivable area in an application example of the present invention.

FIG. 12 is a diagram illustrating an exemplary optimal path according to the present invention.

Fig. 13 is a schematic diagram illustrating specific information for obtaining an optimal path through tree search in an embodiment of the present invention.

Fig. 14 is a schematic diagram of a framework of an automatic parking path planning system according to an embodiment of the present invention.

Fig. 15 is a schematic diagram of a framework of an automatic parking path planning system according to another embodiment of the present invention.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

In addition, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail so as not to obscure the present invention.

As shown in fig. 1, an embodiment of the present invention provides an automatic parking path planning method, which includes the following steps S101 to S103:

s101, periodically acquiring the current vehicle state and parking space coordinate information of free parking spaces around the vehicle in a world coordinate system; the vehicle state comprises vehicle coordinate information and a posture of the vehicle in a world coordinate system;

specifically, in step S101, according to a preset time period, for example, 5 seconds, 10 seconds, etc., the current vehicle state and the parking space coordinate information are periodically obtained for planning a path in the current period, where the path planning is to plan a driving path that enables the vehicle to enter an empty parking space based on the vehicle coordinate information and the posture of the current vehicle in the world coordinate system.

Step S102, based on the current vehicle state, a multi-level tree-shaped searching mode is adopted to search parking paths, whether the vehicle state corresponding to each tree node meets a preset parking condition or not is judged, and a plurality of parking paths tangent to any line in a median strip of a parking space are obtained according to the judgment result; the preset parking condition is that whether a parking path is tangent to any line in a parking space median line of an idle parking space or not in the vehicle state corresponding to the tree node;

specifically, a new tree node can be obtained by executing a preset driving action (for example, a front wheel turns left by 15 degrees and continuously moves forward by 2m) in the current vehicle state, and whether the vehicle state corresponding to each tree node meets a preset parking condition is determined, where the preset parking condition is that whether a parking path is tangent to any line in the parking space median line zone of an idle parking space in the vehicle state corresponding to the tree node or not, and a corresponding parking path can be obtained when the preset parking condition is met, so that a plurality of parking paths tangent to any line in the parking space median line zone are obtained according to the determination result.

It should be noted that the multi-level tree search mode refers to calculating and executing n preset driving actions with the current vehicle state (coordinate and posture) as a starting parent node, to obtain n first-level tree nodes representing n vehicle states after the vehicle executes the n preset driving actions, and further, when a certain first-level tree node T after executing the first preset driving action does not satisfy a preset parking condition, further executing n preset driving actions based on the first-level tree node T1 to obtain n second-level tree nodes, where the n second-level tree nodes are child nodes of the first-level tree node T1 representing n vehicle states after the vehicle executes the first preset driving action and then executes the n preset driving actions. Similarly, when a certain second-level tree node T2 does not satisfy the preset parking condition, n preset driving actions are further executed based on the second-level tree node T2 to obtain n third-level tree nodes, and thus, theoretically, lower-level search can be continuously performed. Based on the search efficiency and the calculation amount consumption, the technician can set the number of search layers according to the actual technical requirements.

And S103, screening the parking paths according to a preset screening rule to obtain an optimal path.

Specifically, although a plurality of parking paths tangent to any line in the median band of the parking space can be obtained through the processing of step S102, the vehicle needs only one most suitable path, and therefore, a preset screening rule can be set according to the problem concerned in the automatic parking process, for example, the complexity of the driving action (for example, the driving action of the parking path corresponding to the first-level tree node is simpler than that of the parking path corresponding to the second-level tree node), the steering wheel angle, and the like are considered.

The method searches the optimal path, and finally controls the vehicle to execute according to the parameters of the multiple paths through the self-positioning of the vehicle. And then periodically and circularly executing the steps S101-103, and dynamically identifying the dynamic path planning until the parking is completed.

The parking lot is only a path planned once, the actual execution process cannot be planned once, because a real system is difficult to ensure that the vehicle runs according to the planned path strictly, and the parking lot cannot be parked well finally due to the path execution error and the parking lot identification error, the parking lot identification and the path planning are dynamically updated in real time in the whole parking process, the updating time of the path planning is carried out according to a preset time period, an optimal path is planned again according to the current position of the vehicle every other preset time period, and the similarity with the optimal path planned at the last time is ensured as far as possible through a path evaluation system.

It should be noted that, by applying the method of the present embodiment, the path planning capability is strong, and the method can automatically adapt to path planning in different scenes and different states, including position and attitude of different vehicles, adaptive parallel parking spaces, vertical parking spaces, and skew parking spaces, and automatically avoid obstacles and be in a drivable area. Different rules do not need to be compiled according to scene conditions such as different parking place types, parking place states, driving areas and the like, the goal is achieved intelligently, programming workload is greatly reduced, and the problem that the rules cannot completely cover all parking conditions is solved.

In addition, by applying the method of the embodiment, dynamic path planning can be very easily realized, the parking process is also the process in different postures, the dynamic path planning in the parking process can be realized, and the requirements of various links on precision (including environment coordinate detection precision, self-positioning precision and the like) are greatly reduced after the dynamic planning is realized, so that the whole parking system is simpler, and the system robustness is stronger.

In one embodiment, the path search is performed recursively using a tree search with depth prioritization of two levels, as shown in FIG. 2. The coordinate of the vehicle is the center of the rear axle of the vehicle, and the vehicle can enter an idle parking space by searching and finding the combination of multiple paths.

Step S102 described in this embodiment specifically includes substeps S201 to S203:

step S201, performing first-layer search based on the current vehicle state, wherein the first layer comprises n first-layer tree nodes, each first-layer tree node represents the vehicle state of the vehicle after the vehicle executes a preset driving action, and whether the vehicle state corresponding to each first-layer tree node meets a preset parking condition is judged;

specifically, the preset traveling motions in this embodiment include 90 corresponding to 9 front wheel angles and 10 travel distances, where the front wheel steering angle wa _ list is [ min _ wa, min _ wa × 0.8, min _ wa × 0.5, min _ wa × 0.2,0, max _ wa × 0.2, max _ wa × 0.5, max _ wa × 0.8, max _ wa ], min _ wa and max _ wa are respectively a right rotation limit angle and a left rotation limit angle of the front wheel of the vehicle, and the algorithm is actually measured, and if min _ wa-30 ℃ and max _ wa-30 ℃ of the current vehicle are equal to, wa _ list is [ -30, -24, -15, -6,0,6,15,24,30 ]; the travel options are l _ list [ -5, -3, -2, -1, -0.5,0.5,1,2,3,5], 10 options, with the unit m, and negative values indicating reverse. From the above, it can be seen that there are 90 choices of 9 × 10, such as l 2 and wa 15, which indicate that the route is a 15-degree left turn of the front wheel and continues to move forward by 2 m.

It should be noted that, the number of actions and the specific numerical value can be adjusted according to actual requirements.

Step S202, if the vehicle state corresponding to a certain first-layer tree node meets a preset parking condition, a parking path which is tangent to any line in a parking space median zone and corresponds to the first-layer tree node is obtained according to a judgment result;

for example, as in the tree structure of FIG. 3, xya0 represents the world coordinates xy of the vehicle at the start of the search, and the vehicle attitude angle a, the first level search starts: after a preset driving action is attempted to be executed (for example, L is 0.5, wa is 0, which indicates that the vehicle is moving straight forward by 0.5m), the vehicle state is changed to xya10 (coordinates and posture), whether the vehicle state xya10 meets the preset parking condition is determined, if yes, a parking path tangent to any line in the parking space median line zone based on the state xya10 can be obtained, and the parking path does not exceed the parking space median line zone, as shown in the left side of fig. 4, when the vehicle is in the vehicle state, a path can be found to be tangent to any line in the parking space median line zone, and the turning radius > of the path is the minimum turning radius of the vehicle, that is, R > -R _ Min, which indicates that the path meets the condition, and when the path is executed, the vehicle state is dynamically planned as shown in the right side of fig. 4, so that it is obviously easier to find a trajectory tangent to the parking space median line zone. When the vehicle is in this state, it indicates that the vehicle state satisfies the preset parking condition.

In this embodiment, the width of the median line zone of the parking space is preferably, but not limited to, 0.3m, that is, 0.15m on the left and right of the median line of the parking space is the boundary of the line zone.

Step S203, if the vehicle state corresponding to a certain first-layer tree node does not meet the preset parking condition, performing second-layer search based on the vehicle state corresponding to the first-layer tree node, and judging whether the vehicle state corresponding to each second-layer tree node meets the preset parking condition, if the vehicle state corresponding to a certain second-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the parking space median line and corresponds to the second-layer tree node according to the judgment result; wherein: the first layer of tree nodes correspond to n second layer of tree nodes, and each second layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

Specifically, as in the tree structure of fig. 3, xya0 represents the world coordinates xy of the vehicle at the start of the search, and the vehicle attitude angle a, the first level search starts: after an attempt to perform a preset driving action (for example, L is 0.5, wa is 0, which indicates that the vehicle is moving straight forward by 0.5m), the vehicle state is changed to xya10 (coordinates and posture), it is determined whether the vehicle state xya10 satisfies a preset parking condition, for example, as shown in fig. 5, the preset parking condition is not satisfied, and when not satisfied, the attempt to perform a preset forming action is continued based on the vehicle state xya10 (for example, L is 0.5, wa is 0, which indicates that the vehicle is moving straight forward by 0.5m), and the vehicle state enters xya 20. And judging whether the vehicle state xya20 meets a preset parking condition, if so, obtaining a parking path tangent to any line in the median strip of the parking space based on the state xya20, and if not, because the preset current search depth is 2 layers at most, further searching cannot be continued.

In a specific embodiment, the step S102 further includes a step S204;

step S204, if the vehicle states corresponding to all the tree nodes of the second layer do not meet the preset parking condition, performing third-layer search based on the vehicle states corresponding to all the tree nodes of the second layer, judging whether the vehicle state corresponding to each third-layer tree node meets the preset parking condition, and if the vehicle state corresponding to one third-layer tree node meets the preset parking condition, obtaining a parking path which is tangent to any line in the parking space median line zone and corresponds to the third-layer tree node according to a judgment result; wherein: the second layer of tree nodes correspond to n third layer of tree nodes, and each third layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

Specifically, in the embodiment, two-layer search is preferentially adopted, which can cope with most of conventional parking stalls, but when facing a relatively narrow parking stall, that is, a parking stall requiring a combination of more paths, the two-layer search may not find a path, and only when the two-layer search fails to find a path, the three-layer search is adjusted, that is, the content of step S204, so as to save the search time and improve the search efficiency. When the three-layer search is adjusted, parking in relatively narrow parking spaces can be faced.

As shown in fig. 6, in a specific embodiment, the method further includes:

and S104, generating a driving instruction of the current period according to the optimal path and the preset driving actions corresponding to the first-layer tree nodes, or the first-layer tree nodes and the second-layer tree nodes, or the first-layer tree nodes, the second-layer tree nodes and the third-layer tree nodes corresponding to the optimal path, and sending the driving instruction to a vehicle driving executing mechanism.

Specifically, after receiving the driving instruction, the vehicle driving executing mechanism executes the driving instruction, and performs a driving preset driving action in the current time period.

For example, if the vehicle executes the preset driving action 01 based on the initial state to obtain the first-layer tree node T1, executes the preset driving action 02 based on the initial state to obtain the second-layer tree node T2, and the final optimal path is from the second-layer tree node T2 to park in an idle parking space, the generated driving instruction is to drive according to the optimal path after sequentially executing the preset driving action 01 and the preset driving action 02.

In an embodiment, the step S104 further includes:

Specifically, the cycle time of the path planning in this embodiment is preferably, but not limited to, 0.5s, and since the dynamic path planning cycle is short (0.5s), each planning cannot take a lot, especially the first section of path, or even if the planned path is a path for successful parking, a single planning is reasonable, but the whole planned path is not reasonable on the time axis, and the vehicle will shake back and forth or shake continuously in direction when being executed. Assuming that the planned paths are completely inconsistent every 0.5 second, a new path and completely different initial actions appear after the vehicle performs the last planned path action for 0.5s, so that the vehicle is difficult to continuously perform the planned different paths in periodic operation. For such a situation, the embodiment proposes the content of step S104 to handle the problem of dynamic path planning path consistency, that is, adding weights for influencing the current path planning according to the rotation angle of the front wheel of the current vehicle and the forward/backward movement, so as to achieve the purpose that the current planned path is consistent with the initial driving direction of the original path, and the current action is not changed as much as possible, so as to stabilize the vehicle operation.

Specifically, on the premise that the overall environment is not changed, the ideal effect of dynamic path planning is that the currently planned path is the same as the last planned remaining path. However, only l _ list [ -5, -3, -2, -1, -0.5,0.5,1,2,3,5] is selected for the searched route, and 10 choices are provided, the lowest precision is 0.5m, as the first section of the previous route advances 0.5m, if 0.2 m is left after execution and no walking is performed, the remaining route of the previous route is different inevitably in the searched route result, and the inconsistency of dynamically planned routes occurs. As shown in fig. 7, the dynamically planned path cannot be completely consistent during the course of the path. Therefore, the present embodiment adds the route left unexecuted in the last executed route segment to the searchable route selection l _ list, adds wa _ list to the front wheel steering angle of the last executed route,

l_list＝[node_not_moved_len,-5,-3,-2,-1,-0.5,0.5,1,2,3,5]

wa_list＝[lastpash_node_wa,-30,-24,-15,-6,0,6,15,24,30]

the information of the optimal path searched in the previous cycle is shown in fig. 8, and the table format is [ wa, L, class ], where wa is radian representation, L is meter, and class represents type (0 is the searched path, 1 represents the tangent arc calculated in simple parking, and 2 is the final straight backing part). The following table shows that the path is: 1. the front wheel rotates right by 0.273 radian and advances by 3 meters; 2. the front wheel rotates left 0.5469 radian and advances 3 meters; 3. the front wheel rotates rightwards by 0.492 radian, and reverses by 5.6628 meters; 4. the front wheel is rotated to 0 degree and the back is rotated to 2.85 meters.

Illustratively, when the next search begins (after 0.5s), the vehicle has traveled 0.8m on the above route, i.e., 2.2m of the first leg of the route remains unexecuted. And starting a new round of path search, adding 2.2m optional strokes into the current search stroke table l _ list, and adding-0.273/PI multiplied by 180-degree optional angles into the steering table wa _ list. If the next search is started (after 0.5s), the vehicle travels 3.7m according to the above path, namely the first path is executed completely, and the second path is not executed for 2.3 m. Then the current search stroke table l _ list adds 2.3m of selectable strokes and the steering table wa _ list adds-0.492/PI × 180 degree of selectable angles.

Based on the above processing, as long as the environment is not changed, the first segment of the searched path has a high probability of being the remaining part of the last path execution, and the consistency of the paths searched twice is high. As shown in fig. 9, the path consistency of the entire dynamic planning is high.

In a specific embodiment, the step S103 specifically includes sub-steps S301 to S304:

s301, acquiring coordinate information of a travelable area in a world coordinate system;

step S302, judging whether the parking paths meet preset drivable conditions or not according to coordinate information of the drivable region in a world coordinate system; the preset travelable condition is that when the vehicle travels according to the parking path, a plurality of preset position points of the vehicle body do not exceed the travelable area;

specifically, each searched route needs to ensure that any position of the vehicle body on the passing route cannot exceed the drivable area, otherwise, collision is possible. According to the invention, whether all the primary vehicle bodies are in the travelable area can be judged at intervals of 2 meters on a single line segment path or at the line segment end point.

For example, as shown in fig. 10, the vehicle body may be described by a plurality of first preset position points, for example, positions at 4 corners and at two sides of front, rear, left and right wheels, and if the plurality of first preset position points all fall within the travelable region polygon, the vehicle may be within the travelable region with a high probability, and if any one of the plurality of first preset position points is not within the travelable region, the vehicle may be not within the travelable region.

It should be noted that the travelable region is described by an irregular polygon, and whether a point is in the polygon is determined, and ray method or library function may be used, which is not described herein.

Step S303, if at least one parking path meets the preset drivable condition, scoring the parking path meeting the preset drivable condition according to a first preset scoring rule, and outputting the parking path with the highest score as an optimal path;

and step S304, if at least one parking path does not meet the preset drivable condition, scoring all parking paths according to a second preset scoring rule, and outputting the parking path with the highest score as an optimal path.

In a specific embodiment, the step S301 includes the following sub-steps S401 to S403:

s401, obtaining an environment image of a 4-channel vehicle-mounted fisheye camera;

specifically, 4-channel vehicle fisheye cameras are respectively installed on two sides of a vehicle body and are all 180-degree fisheye cameras.

S402, carrying out image recognition on the multi-channel environment image to obtain parking space coordinate information of free parking spaces around the vehicle in a camera coordinate system;

specifically, a parking space angle and an empty parking space are identified by a deep learning neural network based on 4 fisheye original images, a central point is accurately extracted from each extracted parking space angle picture through another neural network, after a central point pixel coordinate of the parking space angle is obtained, the pixel coordinate can be converted into a real coordinate based on an automobile coordinate system through internal and external parameters of a camera, and then the automobile coordinate system can be converted into a world coordinate system according to self-positioning. And projecting the parking space angular points and the coordinates of the empty parking spaces identified from the fisheye images onto a low-dimensional overlooking map, and calculating the world coordinates of the parking spaces.

It should be noted that the neural network is well known to those skilled in the art for extracting the angular point of the vacant parking space, and therefore, detailed description of the network result of the neural network and the technical principle thereof is not provided herein. Step S403, acquiring parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system according to the parking space coordinate information of the free parking spaces around the vehicle in a camera coordinate system;

in an embodiment, the step S302 specifically includes:

step S501, calculating i of the vehicle body₁The distance between each first preset position point and the boundary of the travelable area;

step S502, if the vehicle body i₁If the distance between each first preset position point and the boundary of the travelable area is greater than or equal to the first distance, judging whether the parking paths meet preset travelable conditions or not;

step S503, if the vehicle body i₁If the distance between the first preset position point and the boundary of the travelable area is less than the first distance, i is calculated₂The distance between a second preset position point and the boundary of the travelable area is determined according to the distance from the second preset position point to the boundary of the travelable area₂Judging whether the parking paths meet preset travelable conditions or not according to the distance between the second preset position points and the boundary of the travelable area;

step S504, if the vehicle body i₁If the distance between the first preset position point and the boundary of the travelable area is less than the second distance, i is calculated₂A second predetermined position point, i₂The distance between a third preset position point and the boundary of the travelable area is determined according to the distance from the third preset position point to the boundary of the travelable area₂A second predetermined position point, i₃Judging whether the parking paths meet preset travelable conditions or not according to the distance between the third preset position point and the boundary of the travelable area;

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

Specifically, when the number of points describing the position of the vehicle body is small, the description is sparse, which leads to a certain degree of erroneous judgment, as shown in fig. 11. Therefore, in the present embodiment, the number of the position points describing the vehicle body is further optimized, and in the present embodiment, the first distance and the second distance are preferably, but not limited to, 1 meter and 0.5 meter, i₁，i₂，i₃Preferably but not limited to 6, 2, respectively; then there are:

the 6 first preset position points are respectively the positions of 4 corners and the two sides of the left rear wheel and the right rear wheel;

the 2 second preset position points are respectively the positions of the two sides of the left front wheel and the right front wheel;

the 2 third preset position points are respectively the middle positions of the vehicle head and the vehicle tail.

Specifically, the conventional judgment adopts 6-point judgment, namely the distance between 6 first preset position points and the boundary of the travelable area is more than or equal to 1 meter; at this time, when none of the 6 first preset position points exceeds the drivable area during the driving of the vehicle along the parking path, the parking path satisfies the preset drivable condition.

When any one of the 6 first preset position points is less than 1 m away from the boundary of the travelable area, changing the position to 8 points, namely adding 2 second preset position points to the 6 first preset position points; at this time, when the vehicle does not exceed the drivable area at the 6 first preset position points and the 2 second preset position points in the driving process along the parking path, the parking path satisfies the preset drivable condition.

When any one of the 6 first preset position points is less than 0.5m away from the boundary of the travelable area, 10 points are adopted for judgment, namely 6 first preset position points, 2 second preset position points and 2 third preset position points are added. At this time, when the vehicle does not exceed the drivable area at all of the 6 first preset position points, the 2 second preset position points and the 2 third preset position points in the driving process along the parking path, the parking path satisfies the preset drivable condition. In a specific embodiment, the step S301 specifically includes the following sub-steps S601 to S603:

s601, obtaining a panoramic image of the surrounding environment of the vehicle according to the environment image of the multi-channel vehicle-mounted fisheye camera;

s602, performing image semantic segmentation on the panoramic image to obtain coordinate information of a travelable area in a panoramic image coordinate system;

and S603, obtaining coordinate information of the travelable area in a world coordinate system according to the coordinate information of the travelable area in the panoramic image coordinate system.

In a specific embodiment, the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

wherein V represents a route score, L represents a route length, D represents the number of turns of a route multi-segment, W represents the sum of steering wheel angles of the route multi-segment, N represents the distance between a vehicle body of a vehicle execution route and the edge of a travelable area, and W represents the distance between the vehicle body of the vehicle execution route and the edge of the travelable area_a、W_b、W_c、W_dRespectively, are preset weight values.

Specifically, the score increases as the route is shorter, the number of turns is smaller, the change in the steering wheel angle is smaller, and the distance from the edge is longer.

Illustratively, as shown in fig. 12-13, the optimal path and its specific information of the current cycle of an example, it is known from the information output by the above example that 6 complete paths can be searched in the current cycle, the value of the optimal path is 9014.83, the search depth is 2, and the elapsed time is 0.0351 s. The best path in the current cycle is shown in fig. 12, specifically shown in a path (numpy) part in fig. 13, and specifically represented as [ wa, L, class ], where wa is denoted by radian, L is expressed in meters, and class denotes a type (0 is a searched path, 1 denotes a tangent arc calculated in simple parking, and 2 is a final straight backing part). From the above table, the path is: 1. the front wheel rotates right by 0.273 radian and advances by 3 meters; 2. the front wheel rotates left 0.5469 radian and advances 3 meters; 3. the front wheel rotates rightwards by 0.492 radian, and reverses by 5.6628 meters; 4. the front wheel is rotated to 0 degree and the back is rotated to 2.85 meters. Namely a planned complete parking path. Wherein 1 and 2 are paths obtained according to a search list, and 3 and 4 are tangent arc paths and final straight backing paths directly calculated after meeting preset parking conditions.

In a specific embodiment, the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

wherein V represents the path score, L represents the path length, D represents the number of turns of the multi-segment line segment of the path, and W represents the number of pathsSum of steering wheel angles of segment lines, N represents the closest distance of the vehicle body of the vehicle execution path to the edge of the travelable area, D_lIndicating the distance of the vehicle to the parking space after the execution of the path, D_aIndicating the angular difference between the vehicle and the parking space after the execution of the path, W_a、W_b、W_c、W_d、W_e、W_fRespectively, are preset weight values.

Specifically, since the search depth is shallow, when the drivable area is narrow and multiple parking sections are needed, a path meeting the conditions cannot be searched, at this time, the search target is modified to be the path closest to the parking space and having the closest angle, and on the basis of the first preset scoring rule, the distance between the vehicle and the parking space after executing the path and the angle difference between the vehicle and the parking space after executing the path are increased and used as score references.

As shown in fig. 14, another embodiment of the present invention further provides an automatic parking path planning system, including:

the system comprises an information acquisition unit 1, a storage unit and a display unit, wherein the information acquisition unit is used for periodically acquiring the current vehicle state and the parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system; the vehicle state comprises vehicle coordinate information and a posture of the vehicle in a world coordinate system;

the path searching unit 2 is used for searching parking paths by adopting a multi-level tree searching mode based on the current vehicle state, judging whether the vehicle state corresponding to each tree node meets a preset parking condition or not, and obtaining a plurality of parking paths tangent to any line in the median strip of the parking space according to the judgment result; the preset parking condition is that whether a parking path is tangent to any line in a parking space median line of an idle parking space or not in the vehicle state corresponding to the tree node; and

and the path screening unit 3 is used for screening the parking paths according to a preset screening rule to obtain an optimal path.

In an embodiment, the path searching unit 2 specifically includes:

the first search unit 21 is configured to perform a first-level search based on a current vehicle state, where the first level includes n first-level tree nodes, each first-level tree node represents a vehicle state after the vehicle performs a preset driving action, and determines whether a vehicle state corresponding to each first-level tree node meets a preset parking condition;

the first path obtaining unit 22 is configured to, when a vehicle state corresponding to a certain first-layer tree node meets a preset parking condition, obtain, according to a determination result, a parking path corresponding to the first-layer tree node, which is tangent to any line in a parking space median band;

a second searching unit 23, configured to, when a vehicle state corresponding to a certain first-layer tree node does not satisfy a preset parking condition, perform a second-layer search based on the vehicle state corresponding to the first-layer tree node, and determine whether a vehicle state corresponding to each second-layer tree node satisfies the preset parking condition, where the first-layer tree node corresponds to n second-layer tree nodes, and each second-layer tree node represents a vehicle state after the vehicle performs a preset driving action; and

and the second path obtaining unit 24 is configured to, when the vehicle state corresponding to a certain second-level tree node meets a preset parking condition, obtain, according to the determination result, a parking path corresponding to the second-level tree node, which is tangent to any line in the parking space median band.

In an embodiment, the path searching unit 2 further includes:

a third path obtaining unit 25, configured to, when the vehicle states corresponding to all tree nodes on the second layer do not satisfy the preset parking condition, perform a third layer search based on the vehicle states corresponding to all tree nodes on the second layer, and determine whether the vehicle state corresponding to each third layer tree node satisfies the preset parking condition, and if the vehicle state corresponding to a certain third layer tree node satisfies the preset parking condition, obtain, according to the determination result, a parking path that is tangent to any line in the median band in the parking space and corresponds to the third layer tree node; wherein: the second layer of tree nodes correspond to n third layer of tree nodes, and each third layer of tree node represents a vehicle state after the vehicle executes a preset driving action.

As shown in fig. 15, in a specific embodiment, the system further includes:

and the driving control unit 4 is configured to generate a driving instruction of the current period according to the optimal path and the first-layer tree nodes corresponding to the optimal path, or preset driving actions corresponding to the first-layer tree nodes and the second-layer tree nodes, and send the driving instruction to the vehicle driving execution mechanism 5.

In a specific embodiment, the driving control unit 4 is further specifically configured to:

when the optimal path in the previous period includes multiple paths and the optimal path in the current period is generated, the vehicle driving executing mechanism does not execute the driving instruction corresponding to the optimal path in the previous period, the driving instruction in the current period is generated according to the preset driving action corresponding to the tree node corresponding to the optimal path in the current period and the preset driving action corresponding to the tree node corresponding to the path in the previous period, and the driving instruction is sent to the vehicle driving executing mechanism 5.

In an embodiment, the path filtering unit 3 specifically includes:

a travel region acquisition unit 31 for acquiring coordinate information of a travelable region in a world coordinate system;

the driving determination unit 32 is configured to determine whether the parking paths meet preset drivable conditions according to coordinate information of the drivable area in a world coordinate system; the preset travelable condition is that when the vehicle travels according to the parking path, a plurality of preset position points of the vehicle body do not exceed the travelable area;

the first path optimization unit 33 is configured to, when at least one parking path meets a preset drivable condition, score the parking path meeting the preset drivable condition according to a first preset scoring rule, and output the parking path with the highest score as an optimal path; and

and the second path optimization unit 34 is configured to, when at least one parking path does not meet the preset travelable condition, score all parking paths according to a second preset scoring rule, and output the parking path with the highest score as the optimal path.

In an embodiment, the driving determination unit 32 includes:

wherein the first distance > the second distance, i₁，i₂，i₃Are all even numbers > 0.

In a specific embodiment, the information obtaining unit 1 specifically includes:

a vehicle information acquisition unit 11 for acquiring a current vehicle state; and

the parking space information acquiring unit 12 is configured to acquire environment images of multiple paths of vehicle-mounted fisheye cameras, perform image recognition on the multiple paths of environment images to obtain parking space coordinate information of free parking spaces around the vehicle in a camera coordinate system, and acquire parking space coordinate information of the free parking spaces around the vehicle in a world coordinate system according to the parking space coordinate information of the free parking spaces around the vehicle in the camera coordinate system;

in an embodiment, the driving area obtaining unit 31 is specifically configured to:

In a specific embodiment, the first preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d

wherein V represents a route score, L represents a route length, D represents the number of turns of a route multi-segment, W represents the sum of steering wheel angles of the route multi-segment, N represents the distance between a vehicle body of a vehicle execution route and the edge of a travelable area, and W represents the distance between the vehicle body of the vehicle execution route and the edge of the travelable area_a、W_b、W_c、W_dRespectively, are preset weight values.

In a specific embodiment, the second preset scoring rule is expressed by the following expression:

V＝L×W_a+D×W_b+W×W_c+N×W_d+D_l×W_e+D_a×W_f

The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It should be noted that the system of the foregoing embodiment corresponds to the method of the foregoing embodiment, and therefore, portions of the system of the foregoing embodiment that are not described in detail can be obtained by referring to the content of the method of the foregoing embodiment, and are not described again here.

Also, the automatic parking path planning system according to the above-described embodiment may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product.

An embodiment of the present invention further provides a parking control apparatus, including a memory and a processor, where the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, cause the processor to execute the steps of the automatic parking path planning method according to the foregoing embodiment.

Of course, the parking control device may further have a wired or wireless network interface, a keyboard, an input/output interface, and other components to facilitate input and output, and the parking control device may further include other components for implementing the functions of the device, which is not described herein again.

Illustratively, the computer program may be divided into one or more units, which are stored in the memory and executed by the processor to accomplish the present invention. The one or more units may be a series of instruction segments of a computer program capable of performing a specific function, which is used to describe the execution process of the computer program in the parking control apparatus.

The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the parking control apparatus and connecting various portions of the overall parking control apparatus using various interfaces and lines.

The memory may be configured to store the computer program and/or the unit, and the processor may implement various functions of the parking control apparatus by executing or executing the computer program and/or the unit stored in the memory and calling data stored in the memory. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

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Automatic parking path planning method and system and parking control equipment

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