CN112606830A - Two-section type autonomous parking path planning method based on mixed A-star algorithm - Google Patents
Two-section type autonomous parking path planning method based on mixed A-star algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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Abstract
The invention discloses a two-section type autonomous parking path planning method based on a mixed A-x algorithm, which comprises the following steps: dividing a parking path into a first section and a second section; the first section is a path from the vehicle entering the parking lot to the point of the minimum parking distance, and the second section is a path from the point of the minimum parking distance to the point of the parking end; when the distance between the vehicle and the parking ending point is the minimum parking distance, judging that the vehicle reaches the minimum parking distance point; and planning the path of the first section by adopting a first heuristic function and planning the path of the second section by adopting a second heuristic function through a mixed A-x algorithm. According to the two-section type autonomous parking path planning method based on the mixed A-x algorithm, the parking path is divided into two parts, and different heuristic functions are respectively adopted for path planning on the two parts of paths by combining the characteristics of the two parts of paths, so that the optimal path can be obtained in the path searching process with the minimum iteration times, and the path planning efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of autonomous parking path planning, and particularly relates to a two-section autonomous parking path planning method based on a mixed A-star algorithm.
Background
With the development of automobile technology and the improvement of living standard of people, the automobile traveling becomes necessary for the life of people, and along with the increase of crowdedness of roads and parking lots, the automobile traveling is more and more crowded. Meanwhile, the requirement of people on the traveling quality of the automobile is higher and higher, and the realization of parking is always a headache for a driver group. In recent years, research on autonomous parking systems is gradually carried out, and how to plan a path from entering a parking lot to realizing specific parking is a matter worth considering. The autonomous parking path planning itself includes a forward driving process from entering a parking lot or a cell to the vicinity of a parking spot and a reverse parking process for parking from the vicinity of the parking spot. And because the requirements of the two paths on the automobile are different, different node expansion modes should be adopted for the two paths. However, the existing research generally only researches one section of the route, and two sections of the route are not considered on the same problem. Therefore, research on two-segment autonomous parking route planning is needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a two-section type autonomous parking path planning method based on a hybrid A-star algorithm.
The technical scheme provided by the invention is as follows:
a two-segment autonomous parking path planning method based on a hybrid A-star algorithm comprises the following steps:
dividing a parking path into a first section and a second section; the first section is a path from the vehicle entering the parking lot to the point of the minimum parking distance, and the second section is a path from the point of the minimum parking distance to the point of the parking end;
when the distance between the vehicle and the parking termination point is the minimum parking distance, judging that the vehicle reaches the minimum parking distance point;
performing path planning on the first section by adopting a first heuristic function through a hybrid A-algorithm;
wherein the first heuristic function is:
cost(Ni，Ni+1)＝dis(Ni，Ni+1)*(1+a*backcost+b*turncost)；
performing path planning on the second section by adopting a second heuristic function through a hybrid A-algorithm;
wherein the second heuristic function is:
cost(Ni,Ni+1)＝dis(Ni,Ni+1)*{(1+a*backcost+b*turncost+cost[theta(Ni),theta(Ni+1)]}；
in the formula, cost (N)i,Ni+1) Representing the current node NiExtension to the next node Ni+1The required cost of; dis (N)i,Ni+1) Representing the current node NiExtension to the next node Ni+1Back cost represents the distance from the current node NiExtension to the next node Ni+1The cost of reversing and steering; turncost denotes the current node NiExtension to the next node Ni+1Steering cost of theta (N)i) Indicating that the vehicle is at the current node NiThe course angle of (d); theta (N)i+1) Indicating that the vehicle is at the next node Ni+1The course angle of (d); cost [ theta (N)i),theta(Ni+1)]The representation represents the heading angle theta (N) from the current nodei) Extend to the next node heading angle theta (N)i+1) The cost of the need; a. b represent weight coefficients, respectively.
Preferably, when the vehicle is parked vertically, the minimum parking distance is calculated by:
S＝sinθ*Rmin+L；
wherein R isminA minimum turning radius representing that the vehicle does not collide with the parking space; theta represents the minimum turning radius R of the vehicle to avoid collision with the parking spaceminA front wheel corner when exiting a parking space; l represents a vehicle body length.
Preferably, when parallel parking is performed, the method for calculating the minimum parking distance includes:
S＝sin(θ1+θ2)*Rmin+L+L2；
wherein R isminA minimum turning radius representing that the vehicle does not collide with the parking space; theta1Indicating the angle of rotation, theta, of the vehicle when it leaves the parking space, deviating from the original direction2A corner indicating that the vehicle exits from the parking space to recover the posture of the vehicle body; l represents a vehicle body length, L2Indicating the distance of the center of the front wheel of the vehicle from the center of the body.
Preferably, before the parking path planning, the method further includes: and rasterizing and dispersing the environment modeling map.
Preferably, the two-stage autonomous parking path planning method based on the hybrid a-x algorithm further includes: when path planning is carried out, judging whether a vehicle at the next node collides with an environmental barrier or not when node expansion is carried out each time, if the vehicle is judged to collide with the environmental barrier, abandoning the expansion, and carrying out node expansion planning again;
wherein, if the front pose is: [ x ] of0,y0,theta0]And the pose of the next node is as follows: [ x, y, theta ]](ii) a Then
x＝x0+D*cos(theta0)
y＝y0+D*sin(theta0)；
theta＝theta0+D/L*tan(delta)
Where G denotes a resolution of a grid in the environment modeling map, D denotes a resolution of a pose change per node expansion, and delta denotes a steering angle change value per node expansion.
Preferably, in the first heuristic function, a value range of backcost is 5 to 10, and a value range of turncost is 5 to 10.
Preferably, in the second heuristic function, backscost is 1 and turncost is 1.
The invention has the beneficial effects that:
according to the two-section type autonomous parking path planning method based on the mixed A-x algorithm, the parking path is divided into two parts according to the minimum parking distance required by the vehicle, and different heuristic functions are respectively adopted for path planning on the two parts of paths by combining the characteristics of the two parts of paths, so that the optimal path can be obtained by the path searching process with the minimum iteration times, the path planning efficiency is improved, and simple and efficient parking is realized.
Drawings
Fig. 1 is a flowchart of a two-stage autonomous parking route planning method based on a hybrid a-x algorithm according to the present invention.
Fig. 2a-2b are schematic diagrams of the hybrid a-algorithm Reeds-Shepp node expansion according to the present invention.
Fig. 3 is a schematic diagram illustrating a calculation process of a minimum parking distance when the vehicle is parked vertically according to the present invention.
Fig. 4 is a schematic diagram of a calculation process of parallel parking distances during vertical parking according to the present invention.
FIG. 5 is a diagram illustrating a design basis of a first heuristic function according to the present invention.
FIG. 6 is a diagram illustrating a design basis of a second heuristic function according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the present invention provides a two-stage autonomous parking route planning method based on a hybrid a-x algorithm, including the following steps:
discretization of environment modeling map
(1) The hybrid a-x algorithm is mainly suitable for global path planning in the case where the environment map is known. Since the node expansion method of the hybrid a-star algorithm is performed based on the grid map, the known environment map needs to be discretized first. When grid discretization is carried out, the resolution G of the grid, the resolution D of the pose change of each node expansion and the turning radian delta which is the largest in vehicle dynamics need to be considered.
As shown in fig. 2, each time a node is expanded, if the current pose is [ x, y, theta ], the pose calculation formula of the next node is as follows:
x＝x+D*cos(theta)；
y＝y+D*sin(theta)；
theta＝theta+D/L*tan(delta)；
where G denotes the resolution of a grid in the environment modeling map, and D denotes the resolution of the pose change per node expansion.
Before the hybrid a search is performed, the shortest path searched based on the hybrid a algorithm is required to be used as the heuristic value of the hybrid a algorithm, and the maximum number of times n of expansion of the two grid nodes is equal to G/D.
Two, parking path segment design
1. Calculation of minimum parking distance
And calculating the required minimum parking distance according to the size of the vehicle, the parking space information, the minimum turning radius and the parking mode, wherein vertical parking and parallel parking are mainly considered.
The present invention provides for the definition of a minimum parking distance: minimum turning radius R of vehicle not colliding with parking spaceminThe distance from the parking space to the vehicle body parallel to the advancing direction of the vehicle. The minimum parking distance S is obtained mainly based on the parking space information, the minimum turning radius R, and the vehicle body length L. Firstly, the minimum turning radius R which does not collide with the parking space can be solved according to the parking space informationmin. Wherein the minimum turning radius R is provided by the factory unit of the vehicle. At the same time, the minimum parking distance S is determined, and the minimum turning radius R at which the vehicle does not collide with the parking space must be knownminThe minimum value is obtained by calculating the front wheel rotation angle theta and the vehicle body length L when the parking space is driven outThe parking distance S includes a turning travel distance and a straight travel distance. In order to express the solution of the minimum parking distance more clearly, the invention provides a specific solution method of the minimum parking distance of vertical parking and horizontal parking.
(1) And (3) vertical parking: as shown in fig. 3, the minimum parking distance should be satisfied as long as a distance required to exit from the parking space at the maximum steering wheel angle and restore the normal forward driving posture without collision of the vehicle body with the parking space. And simultaneously satisfies the minimum turning radius and the collision-free constraint. With the center of the parking space and the vehicle head as reference points, the vehicle has the minimum turning radius R which does not collide with the parking spaceminThe size of the corner of the front wheel when the parking space is driven out is theta, and the minimum parking distance S can be obtained through the vehicle body dimension information. S is the length L from the center of the rear wheel to the head1And RminSum of sin θ. However, in order to take account of the errors of the path planning algorithm and to ensure the realization of a specific parking, the minimum parking distance S is given a value that is greater than the theoretical value. Based on the above considerations and simplifying the calculation, S ═ sin θ R is selectedmin+ L is used as a calculation formula for the minimum parking distance. The theoretical calculation of the minimum parking distance is shown in fig. 3.
(2) Parallel parking: as shown in fig. 4, the minimum parking distance when parallel parking should satisfy: the distance required for driving out from the parking space at the maximum steering wheel corner and recovering the normal forward driving posture is obtained under the condition that the vehicle body does not collide with the parking space; and simultaneously satisfying the minimum turning radius and the collision-free constraint. For parallel parking, the direction of the vehicle in the parking space is the same as the direction of the vehicle after exiting the parking space, and then the wheel angle of the exiting vehicle includes: angle of rotation theta deviating from original direction when vehicle leaves parking space1And the angle of rotation for restoring the posture of the vehicle body is theta2. Taking the front and back symmetrical planes of the vehicle body as reference planes, the vehicle has the minimum turning radius R which does not collide with a parking spaceminThe magnitude of the accumulated front wheel rotation angle when the vehicle is driven out of the parking space is theta1+θ2At this time, the minimum parking distance S can be obtained from the vehicle body size information. S is the length L from the center of the rear wheel to the head1Distance L between the center of the front wheel and the center of the vehicle body2And Rmin*sin(θ1+θ2) The sum of (1). In order to take account of the errors of the path planning algorithm and to ensure the realization of a specific parking, the minimum parking distance S is also given a value which is greater than the theoretical value. Based on the above considerations, and simplifying the calculation, S ═ sin (θ) may be selected1+θ2)*Rmin+L+L2. The theoretical calculation of the minimum parking distance is shown in fig. 4.
2. Design of two-segment heuristic function
The parking path is divided into two parts according to the minimum parking distance required by the vehicle, namely a forward driving process from entering a parking lot or a cell to a minimum parking distance point (the position of the center of mass of the vehicle when the distance between the parking end point and the minimum parking distance point is the minimum parking distance), and a backing-in process for parking from the minimum parking distance point.
And designing different heuristic functions based on a mixed A-star algorithm according to the driving characteristics of the vehicles of the two paths. During forward driving, the vehicle always drives towards a target point, and the obstacle avoidance is mainly considered, and the steering and reversing behaviors are less as much as possible. The heuristic function should give a larger weight to the steering cost and the reversing cost. In the process of backing up, the requirement on the pose of the vehicle is high, so that the heuristic function gives a larger weight to the minimum turning radius and gives a smaller weight to the steering and backing cost, and the optimal path is obtained by the minimum iteration times in the path searching process.
After the minimum parking distance is solved, the parking path should be segmented based on the minimum parking distance S, and different heuristic functions should be designed for planning the two paths according to the driving characteristics of the two paths. When the hybrid A-x algorithm is used for searching the path, the path of the expanded node is mainly carried out through a Reeds-Shepp curve, so that the reversing and steering behaviors of the node expansion process are mainly considered when the hybrid A-x algorithm is designed. FIGS. 2a-2b show the manner of nodes expanded by the Reeds-Shepp curve, and mainly relate to the expansion directions of front and rear 3 nodes.
(1) From the point of entry into the parking lot to the minimum parking distance: the section of the pathMainly in the forward driving process at low speed, the vehicle is far away from the target point at the moment, so the vehicle is considered to always drive along the target point, and the posture constraint of the vehicle is not excessively concerned. Meanwhile, when the vehicle runs in the low-speed forward direction, the steering and reversing behaviors can greatly increase the time for reaching a target point or the energy of a driver, so that the planned path does not have more steering and reversing behaviors, and a steering cost turn and a reversing cost back are given larger values as much as possible in the constraint of the heuristic function, so that the search iteration times, the steering behaviors and the reversing behaviors are reduced, and the path is ensured to be relatively excellent. If a cost function is used to represent the current node NiExtension to the next node Ni+1The cost function form of the time prediction is as follows:
cost(Ni，Ni+1)＝dis(Ni，Ni+1)*(1+a*backcost+b*turncost)；
in the above formula, backscost and turncost are respectively at the current node NiExtension to the next node Ni+1The reversing cost and the steering cost are both 5-10, and the backscost and the turncost are given by the invention; thereby serving to reduce reverse and steering behavior. a and b respectively represent the weight of each parameter, and can be respectively taken as the ratio of the length of the reversing path and the steering path to the length of the whole path; for between two adjacent nodes, 1 may be directly taken. The length of the turning path and the reversing path occupying the node expansion path is mainly determined, and for two adjacent nodes, only one path is generally used, so that 1 is directly taken.
Fig. 6 is used for a simple illustration of how to plan the segment of the path by reducing the steering and reversing behavior, thereby providing a basis for the design of the heuristic function of the segment of the path. As shown in fig. 6, when a vehicle enters the parking lot in the illustrated pose and wants to reach the target point G, the preferred extended path should be path 2, if path planning is performed based on the heuristic function. Because no turns are needed when the path nodes are initially expanded and the entire path only needs one turn, while path 1 needs two turns.
(2) From the point of minimum parking distance to the point of achieving parking:the path is mainly used for parking in a correct vehicle pose by steering and backing, and cannot collide with a parking space in the parking process. Therefore, the steering cost turncost and the backing cost backcost should be given smaller values, even without adding extra steering cost and backing cost. And the dynamic course angle and minimum turning radius constraints should be increased to ensure that the vehicle can park in a more optimal path. Use cost [ theta (Ni), theta (Ni +1) ]]To represent the heading angle theta (N) from the current nodei) Extend to the next node heading angle theta (N)i+1) And the required cost is achieved, so that the pose is reduced, and the error of the final arrival end point and the target end point pose is ensured to be within a threshold range. Then at this point if a cost function is used to represent the current node NiExtension to the next node Ni+1The cost function form of the heuristic function two is as follows:
cost(Ni,Ni+1)＝dis(Ni,Ni+1)*{(1+a*backcost+b*turncost+cost[theta(Ni),theta(Ni+1)]}；
and judging whether the node can be expanded under the minimum turning radius without collision, and if not, abandoning the expansion of the node. In the above formula, backscost and turncost are respectively at the current node NiExtension to the next node Ni+1Cost of backing up and steering, cost [ theta (Ni), theta (Ni +1)]Representing the required cost of course angular changes for both poses. The backscost and the turncost are respectively assigned as 1, so that additional limitation on reversing and steering behaviors is not performed; let cost [ theta (N)i),theta(Ni+1)]The value is 5 times to 10 times of the change values of the two pose radians. And ending after the path node of the segment is expanded to the position and posture error with the terminal point within a certain range, namely realizing the whole path searching process by a mixed A-x algorithm. and a and b respectively represent the weight of each parameter, and can be taken as the ratio of the length of the reversing path and the steering path to the length of the whole path. For between two adjacent nodes, 1 may be directly taken.
Fig. 6 shows a specific planning result of the path, which is used to show the main movement pattern of the vehicle when parking is specifically realized. As can be seen from fig. 6, this section mainly consists of a turning action and a reversing action. Further explaining the meaning and effectiveness of the segmentation, a basis is provided for the design of the heuristic function of the path planning of the second segment.
Third, generation of path
The node expansion mode of the hybrid A-algorithm is mainly based on the hybrid A-algorithm and an RS curve, wherein the left graph of the second graph represents six node expansion directions of the RS curve, namely, forward straight running, left turning and right turning, backward straight running, left turning and right turning, and each turning angle change is delta. When the node expansion of the first section of path is mainly based on a heuristic function I and node expansion is carried out from the current grid, a mixed A-x algorithm is used for searching, and the node expansion times n are judged firstly1And whether the pose is less than n-G/D or not is judged, and RS connection judgment is carried out once every five expanded nodes, namely whether smooth connection of the current pose to the next grid can be realized or not is judged by traversing 48 RS curves. After RS connection, discretizing the RS curve, namely realizing the expansion of the RS curve through discrete poses, and if the current pose is [ x, y, theta ] during each node expansion]The pose calculation formula of the next node is as follows:
x＝x+D*cos(theta)；
y＝y+D*sin(theta)；
theta＝theta+D/L*tan(delta)；
and (3) performing path collision detection while node expansion, namely detecting whether the vehicle model collides with an environmental barrier, abandoning the RS curve if the vehicle model collides with the environmental barrier, searching again, and finally obtaining a path from the vehicle entering the parking lot to the minimum parking distance point. Specifically, it is determined whether the vehicle reaches the parking end point by determining whether the position error between the vehicle position [ x, y, theta ] and the end point is within a threshold range, that is, when the position of the vehicle is [ x ± Δ x, y ± Δ y, theta ± Δ theta ]. According to experience, delta x is less than or equal to 0.05 meter, delta y is less than or equal to 0.5 meter, and delta theta is less than or equal to 0.05 radian. Similarly, the path planning from the minimum parking point to the specific process of realizing parking is mainly performed based on the heuristic function two, and the node expansion mode and the collision detection are similar to the path planning of the first section.
The invention mainly designs the parking path in a segmented mode based on a mixed A-x algorithm. Compared with the existing research that only the path planning is considered near the parking space, the method has more practical significance and can save the parking waiting time. Secondly, in the path planning process, the parking path is segmented based on the characteristics of the mixed A-x algorithm, so that the expansion number of nodes can be reduced as much as possible in the two paths, and the path calculation time is saved.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A two-segment autonomous parking path planning method based on a hybrid A-star algorithm is characterized by comprising the following steps:
dividing a parking path into a first section and a second section; the first section is a path from the vehicle entering the parking lot to the point of the minimum parking distance, and the second section is a path from the point of the minimum parking distance to the point of the parking end;
when the distance between the vehicle and the parking termination point is the minimum parking distance, judging that the vehicle reaches the minimum parking distance point;
performing path planning on the first section by adopting a first heuristic function through a hybrid A-algorithm;
wherein the first heuristic function is:
cost(Ni，Ni+1)＝dis(Ni，Ni+1)*(1+a*backcost+b*turncost)；
performing path planning on the second section by adopting a second heuristic function through a hybrid A-algorithm;
wherein the second heuristic function is:
cost(Ni,Ni+1)＝dis(Ni,Ni+1)*{(1+a*backcost+b*turncost+cost[theta(Ni),theta(Ni+1)]}；
in the formula, cost (N)i,Ni+1) Representing the current node NiExtension to the next node Ni+1The required cost of; dis (N)i,Ni+1) Representing the current node NiExtension to the next node Ni+1Back cost represents the distance from the current node NiExtension to the next node Ni+1The cost of reversing and steering; turncost denotes the current node NiExtension to the next node Ni+1Steering cost of theta (N)i) Indicating that the vehicle is at the current node NiThe course angle of (d); theta (N)i+1) Indicating that the vehicle is at the next node Ni+1The course angle of (d); cost [ theta (N)i),theta(Ni+1)]The representation represents the heading angle theta (N) from the current nodei) Extend to the next node heading angle theta (N)i+1) The cost of the need; a. b represent weight coefficients, respectively.
2. The two-stage autonomous parking path planning method based on the hybrid a-x algorithm according to claim 1, wherein the minimum parking distance is calculated when the vehicle is parked vertically by:
S＝sinθ*Rmin+L；
wherein R isminA minimum turning radius representing that the vehicle does not collide with the parking space; theta represents the minimum turning radius R of the vehicle to avoid collision with the parking spaceminA front wheel corner when exiting a parking space; l represents a vehicle body length.
3. The two-stage autonomous parking path planning method based on the hybrid a-x algorithm according to claim 2, wherein the minimum parking distance is calculated by:
S＝sin(θ1+θ2)*Rmin+L+L2；
wherein R isminA minimum turning radius representing that the vehicle does not collide with the parking space; theta1Indicating the angle of rotation, theta, of the vehicle when it leaves the parking space, deviating from the original direction2A corner indicating that the vehicle exits from the parking space to recover the posture of the vehicle body; l represents a vehicle body length, L2Indicating the distance of the center of the front wheel of the vehicle from the center of the body.
4. The two-stage autonomous parking path planning method based on the hybrid a-algorithm according to claim 2 or 3, characterized by further comprising, before performing parking path planning: and rasterizing and dispersing the environment modeling map.
5. The hybrid a-algorithm-based two-segment autonomous parking path planning method according to claim 4, further comprising: when path planning is carried out, judging whether a vehicle at the next node collides with an environmental barrier or not when node expansion is carried out each time, if the vehicle is judged to collide with the environmental barrier, abandoning the expansion, and carrying out node expansion planning again;
wherein, if the front pose is: [ x ] of0,y0,theta0]And the pose of the next node is as follows: [ x, y, theta ]](ii) a Then
Where G denotes a resolution of a grid in the environment modeling map, D denotes a resolution of a pose change per node expansion, and delta denotes a steering angle change value per node expansion.
6. The two-stage autonomous parking path planning method based on the hybrid A-algorithm according to claim 5, wherein in the first heuristic function, a back cost has a value range of 5-10, and a turn cost has a value range of 5-10.
7. The two-stage autonomous parking path planning method based on the hybrid a-algorithm according to claim 6, characterized in that in the second heuristic function, back cost is 1 and turn cost is 1.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN113219990A (en) * | 2021-06-02 | 2021-08-06 | 西安电子科技大学 | Robot path planning method based on adaptive neighborhood and steering cost |
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| CN113830079A (en) * | 2021-10-19 | 2021-12-24 | 同济大学 | Online planning method and system for continuous curvature parking path with any initial pose |
| CN113830079B (en) * | 2021-10-19 | 2023-09-01 | 同济大学 | Method and system for online planning continuous curvature parking path of arbitrary initial pose |
| CN113978547B (en) * | 2021-10-21 | 2022-08-02 | 江铃汽车股份有限公司 | Automatic driving steering control method and system |
| CN113978547A (en) * | 2021-10-21 | 2022-01-28 | 江铃汽车股份有限公司 | Automatic driving steering control method and system |
| CN114255594A (en) * | 2021-12-28 | 2022-03-29 | 吉林大学 | Autonomous passenger-riding parking motion planning and motion control method |
| CN114255594B (en) * | 2021-12-28 | 2024-03-15 | 吉林大学 | Autonomous passenger parking motion planning and motion control method |
| CN114261388A (en) * | 2022-02-23 | 2022-04-01 | 吉林大学 | Anthropomorphic automatic parking path planning method |
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| CN115223389B (en) * | 2022-07-15 | 2023-11-21 | 西南交通大学 | Parking guidance path planning method based on dynamic road section cost |
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