CN114924559A - Intelligent vehicle path planning method for controlling virtual force direction - Google Patents
Intelligent vehicle path planning method for controlling virtual force direction Download PDFInfo
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- 230000008569 process Effects 0.000 claims description 3
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- 230000004888 barrier function Effects 0.000 description 10
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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Abstract
The invention discloses an intelligent vehicle path planning method for controlling a virtual force direction, which improves an artificial potential field, wherein the artificial potential field comprises a target point virtual attraction potential field, an obstacle virtual repulsion potential field and a road boundary virtual repulsion potential field, aiming at the defects that a target cannot be reached and is easy to fall into a local minimum value point and the like in the field of intelligent vehicle path planning by using a traditional artificial potential field method; the virtual attraction potential field of the target point is divided into two action areas, the virtual repulsion potential field of the obstacle is optimized to be an oval action area, and a dotted line potential field between lanes is added into the virtual repulsion potential field of the road boundary. The method controls the virtual attraction direction of the target point to the intelligent vehicle and the virtual repulsion direction of the obstacle to the intelligent vehicle in the virtual force field of the improved artificial potential field in the mutually vertical directions, avoids the situation that the intelligent vehicle falls into a local minimum value point because the virtual resultant force is 0, ensures that the intelligent vehicle can smoothly avoid the obstacle to reach the target point, and has simple algorithm and good real-time performance.
Description
Technical Field
The invention relates to the field of intelligent vehicle path planning, in particular to an intelligent vehicle path planning method for controlling the direction of virtual force.
Background
The traditional artificial potential field algorithm is simple in logic and simple in model structure, and the intelligent vehicle advances along the direction of virtual resultant force by constructing a target point virtual attraction potential field and an obstacle virtual repulsion potential field. The common barrier vehicle potential field is a circular field, and when the field meets the safety distance required by the barrier vehicle in the transverse direction, the unnecessary safety distance of the intelligent vehicle in the lateral direction can be increased; only two boundaries of a road are considered in the setting of the road boundary potential field, and a potential field of a dotted line between lanes is not set, so that the problem that an intelligent vehicle occupies two lanes for running exists; when the intelligent vehicle, the obstacle and the target point are on the same straight line, the intelligent vehicle is easy to fall into a local minimum point, and the basic reason is that the virtual repulsion force and the virtual attraction force on the intelligent vehicle are opposite and equal in size at a certain point, so that the virtual resultant force on the intelligent vehicle is 0 and the intelligent vehicle cannot advance.
Aiming at the problems of the traditional artificial potential field in the intelligent vehicle path planning, the barrier potential field can be optimized into an elliptical acting area to reduce the lateral safety distance of the intelligent vehicle; an inter-lane dotted line potential field can be added in the road boundary potential field to avoid the intelligent vehicle from occupying two lanes for driving; aiming at the problem of the local minimum value, the Liyuhao and the like invent an artificial potential field path planning method (CN201911086388.0) based on escape force fuzzy control, when the intelligent vehicle is detected to be trapped in the local minimum value point, the method helps the intelligent vehicle to escape from the local minimum value point by increasing the escape force and carrying out fuzzy control on the escape force, but the complexity of the whole algorithm is increased; the creep strength and the like invent a path planning method, a device and equipment (CN202010690134.6) based on an improved artificial potential field method, when the intelligent vehicle is detected to be trapped in a local minimum point, the intelligent vehicle is helped to escape from the local minimum point by using a variable-step simulated annealing algorithm, but the calculation amount and the complexity of the algorithm are increased.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent vehicle path planning method for controlling the direction of virtual force aiming at the problem of artificial potential field path planning in the background technology.
The invention adopts the following technical scheme for solving the technical problems:
an intelligent vehicle path planning method for controlling a virtual force direction comprises the following steps:
step 1), obtaining global environment information of a current road section, establishing a coordinate system, defining a direction parallel to a road as an X axis, defining a direction perpendicular to the road as a Y axis, wherein a dotted line between two lanes corresponds to a Y-axis 0 coordinate, and defining an intersection point of a perpendicular line made from an intelligent vehicle starting point to the dotted line of the lane and the dotted line of the lane as a coordinate origin; the global environment information comprises an intelligent vehicle starting point position, an obstacle position and a target point position, wherein the obstacle position comprises the position of a dynamic obstacle vehicle and the position of a static obstacle;
step 2), acquiring road information of the current road section according to the established coordinate system, and establishing a target point virtual attraction potential field, an obstacle virtual repulsion potential field and a road boundary virtual repulsion potential field, wherein the target point virtual attraction potential field is used for guiding the intelligent vehicle to advance to a target point; the obstacle virtual repulsive force potential field is used for guiding the intelligent vehicle to avoid the obstacle in the driving process; the road boundary virtual repulsive force potential field is used for limiting the intelligent vehicle not to cross the lane boundary and simultaneously not to occupy the driving of two lanes; the direction of virtual repulsion generated by the road boundary virtual repulsion potential field is vertical to the X axis;
and 3) executing a direction control strategy of the virtual attraction and the virtual repulsion according to the conditions of the virtual repulsion and the virtual attraction on the real-time position of the intelligent vehicle until the intelligent vehicle enters a preset target position, wherein the direction control strategy of the virtual attraction and the virtual repulsion is as follows:
step 3.1), the virtual gravitation direction of the target point to the intelligent vehicle is controlled through the Euclidean distance delta d between the intelligent vehicle and the target point, and the radius is d 0 The circle divides the action area of the potential field of the target point into two areas when delta d>d 0 Controlling the virtual gravitation direction of the target point to the intelligent vehicle to be positive along the X axis; when Δ d<d 0 Controlling the direction of the virtual attraction of the target point to the intelligent vehicle to be the direction in which the intelligent vehicle points to the target point;
step 3.2), the virtual repulsion direction of the intelligent vehicle is controlled by the obstacle through the lane where the obstacle is located, and when the obstacle is located in the right lane, the virtual repulsion direction of the obstacle to the intelligent vehicle is controlled to be in the positive direction along the Y axis; when the obstacle is in the left lane, the direction of the virtual repulsion force of the obstacle to the intelligent vehicle is controlled to be negative along the Y axis;
and 3.3), the direction of the virtual attraction force borne by the intelligent vehicle is vertical to the direction of the virtual repulsion force, and the virtual repulsion force does not have a component opposite to the virtual attraction force.
As a further optimization scheme of the intelligent vehicle path planning method for controlling the direction of the virtual force, the obstacle virtual repulsive force potential field in the step 2)
The virtual gravitational potential field of the target point is
With a radius d 0 The circle divides the virtual gravitational potential field of the target point into two areas;
virtual repulsion function of road boundaries
Wherein k is rep Denotes the coefficient of gain of repulsion, k att Is a gravitational gain coefficient, k edge Is a road boundary virtual repulsive force gain factor,
representing the euclidean distance between the smart car and the obstacle,
representing the Euclidean distance, rho, between the intelligent vehicle and the target point 0 Indicating a safe distance by
Optimizing the action range of the potential field of the obstacle into an ellipse, wherein | X-X 0 I is the absolute value of the difference value between the abscissa of the intelligent vehicle and the abscissa of the obstacle, and I Y-Y 0 I is the absolute value of the difference value between the longitudinal coordinate of the intelligent vehicle and the longitudinal coordinate of the obstacle, A is the transverse safe distance for avoiding the collision between the intelligent vehicle and the obstacle, B is the longitudinal safe distance for the intelligent vehicle to cross the obstacle, and d is oneAnd the standard width of the side lane, w is the width of the automobile, and v is the running speed of the intelligent automobile.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
(1) the improved artificial potential field algorithm optimizes the potential field action area of the obstacle vehicle into an ellipse on the basis of the traditional artificial potential field, and reduces unnecessary lateral movement distance when the intelligent vehicle crosses the obstacle vehicle; the potential field of the target point is divided into two areas according to the distance, and the direction of virtual attraction is controlled according to different areas, so that the intelligent vehicle is better pulled to avoid obstacles; a dotted line potential field between lanes is increased, so that the intelligent vehicle is prevented from occupying two lanes for running;
(2) based on an improved artificial potential field method, the virtual repulsion direction of an obstacle to an intelligent vehicle and the virtual attraction direction of a target point to the intelligent vehicle are controlled in two mutually perpendicular directions, so that the virtual resultant force is avoided to be 0, and the problem that the intelligent vehicle falls into a local minimum point is fundamentally avoided;
(3) the intelligent vehicle is prevented from falling into the local minimum point by controlling the direction of the virtual force, and the algorithm is simple and has good real-time performance.
Drawings
FIG. 1 is a control flow diagram of an intelligent vehicle routing method of the present invention for controlling virtual force direction;
FIG. 2 is a schematic diagram of virtual forces exerted on an intelligent vehicle according to the present invention at different positions;
FIG. 3 is a diagram illustrating a potential field distribution of a virtual repulsive force at a road boundary according to the present invention;
FIG. 4 is a distribution diagram of road environment information coordinates according to the present invention;
fig. 5 is a diagram of a simulation result of the intelligent vehicle path planning for controlling the direction of the virtual force according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in fig. 1, the invention discloses an intelligent vehicle path planning method for controlling a virtual force direction, which comprises the following steps:
step 1), acquiring global environment information of a current road section, establishing a coordinate system, defining a direction parallel to a road as an X axis, defining a direction vertical to the road as a Y axis, wherein a dotted line between two lanes corresponds to a Y axis 0 coordinate, and defining an intersection point of a vertical line from an intelligent vehicle starting point to the dotted line of the lane and the dotted line of the lane as a coordinate origin; the global environment information comprises an intelligent vehicle starting point position, an obstacle position and a target point position, wherein the obstacle position comprises the position of a dynamic obstacle vehicle and the position of a static obstacle;
step 2), acquiring road information of the current road section according to the established coordinate system, and establishing a target point virtual attraction potential field, an obstacle virtual repulsion potential field and a road boundary virtual repulsion potential field, wherein the target point virtual attraction potential field is used for guiding the intelligent vehicle to advance to a target point; the obstacle virtual repulsive force potential field is used for guiding the intelligent vehicle to avoid the obstacle in the driving process; the road boundary virtual repulsive force potential field is used for limiting the intelligent vehicle not to cross the lane boundary and simultaneously not to occupy the driving of two lanes; the direction of virtual repulsion generated by the road boundary virtual repulsion potential field is vertical to the X axis;
and 3) executing a direction control strategy of the virtual attraction and the virtual repulsion according to the conditions of the virtual repulsion and the virtual attraction on the real-time position of the intelligent vehicle until the intelligent vehicle enters a preset target position, wherein the direction control strategy of the virtual attraction and the virtual repulsion is as follows:
step 3.1), the virtual gravitation direction of the intelligent vehicle is controlled by the target point through the Euclidean distance delta d between the intelligent vehicle and the target point, and the radius is d 0 The circle divides the action area of the potential field of the target point into two areas when delta d>d 0 Controlling the virtual gravitation direction of the target point to the intelligent vehicle to be positive along the X axis; when Δ d<d 0 Controlling the direction of the virtual attraction of the target point to the intelligent vehicle to be the direction in which the intelligent vehicle points to the target point;
step 3.2), the virtual repulsion direction of the intelligent vehicle is controlled by the obstacle through the lane where the obstacle is located, and when the obstacle is located in the right lane, the virtual repulsion direction of the obstacle to the intelligent vehicle is controlled to be in the positive direction along the Y axis; when the obstacle is in the left lane, controlling the virtual repulsion direction of the obstacle to the intelligent vehicle to be negative along the Y axis;
and 3.3), the virtual attraction direction and the virtual repulsion direction of the intelligent vehicle are perpendicular to each other, and the virtual repulsion does not have a component opposite to the virtual attraction.
Virtual repulsive force field of the obstacle in the step 2)
The virtual gravitational potential field of the target point is
With a radius d 0 The circle divides the virtual gravitational potential field of the target point into two areas;
virtual repulsion function of road boundaries
Wherein k is rep Denotes the repulsive force gain coefficient, k att Is a gravitational gain coefficient, k edge A gain factor of a road boundary virtual repulsive force,
representing the euclidean distance between the smart car and the obstacle,
representing the Euclidean distance, rho, between the intelligent vehicle and the target point 0 Indicating a safe distance by
Optimizing the action range of the potential field of the obstacle into an ellipse, wherein | X-X 0 I is the absolute value of the difference value between the horizontal coordinate of the intelligent vehicle and the horizontal coordinate of the obstacle, Y-Y 0 I is the absolute value of the difference value between the longitudinal coordinate of the intelligent vehicle and the longitudinal coordinate of the obstacle, A is the transverse safe distance for avoiding the collision between the intelligent vehicle and the obstacle, B is the longitudinal safe distance for the intelligent vehicle to cross the obstacle, d is the standard width of a lane on one side, w is the width of the vehicle, and v is the running speed of the intelligent vehicle。
As the intelligent vehicle moves forward, the intelligent vehicle is stressed at different positions as shown in figure 2, and when the intelligent vehicle is at the starting point, the radius of the intelligent vehicle not at the target point is d 0 Receives the virtual attraction generated by the target point along the positive direction of the X axis in the circular area; when the intelligent vehicle contacts with an elliptic acting area of an obstacle (static and dynamic) on the right lane, the intelligent vehicle receives a virtual repulsive force along the positive direction of the Y axis, the direction of the virtual attractive force is kept unchanged, and the intelligent vehicle drives into the left lane from the right lane under the action of virtual resultant force; when the intelligent vehicle contacts with an elliptic action area of a left lane barrier (static and dynamic), the intelligent vehicle receives a virtual repulsive force along the negative direction of the Y axis, the direction of the virtual attractive force is kept unchanged, and the intelligent vehicle drives from the left lane to the right lane under the action of the virtual fitting force; when the intelligent vehicle enters a target point, the radius is d 0 The virtual attraction direction of the target point to the intelligent vehicle is changed to be pointed to the target point by the intelligent vehicle, and the intelligent vehicle is pulled to reach the target point.
In the right lane, the road boundary potential field acting region is as shown in fig. 3, and in the region [ -d, -d + w/2], the road boundary potential field generates a virtual repulsive force along the positive direction of the Y axis, and the virtual repulsive force is larger, so that the intelligent vehicle is prevented from driving out of the lane; no potential field is applied in the region [ -d + w/2, -w/2], and the virtual force is 0; in the region [ -w/2,0], a road boundary potential field generates a virtual repulsive force along the negative direction of the Y axis, the virtual force is small, the intelligent vehicle is prevented from occupying two lanes to drive simultaneously, and when the intelligent vehicle contacts an obstacle potential field action region and needs to change lanes, the road boundary virtual repulsive force can be overcome to drive the intelligent vehicle to change lanes; the action region of the road boundary potential field of the left lane and the action region of the right lane are symmetrical about the X axis, and the direction of the virtual repulsive force in the corresponding region is opposite to that of the right lane.
In order to verify the effectiveness and feasibility of the method, the method is used for path planning simulation in a Matlab software platform, the road environment is arranged as shown in figure 4, and the position coordinate of the starting point of the intelligent vehicle is set to be 0,1.75]In the left lane; three static obstacles are arranged, the coordinates of which are respectively [10,1.75 ]]，[25,-1.5]，[90,1.75](ii) a Two dynamic barrier vehicles are arranged, and the coordinates of the starting point of the No. 1 barrier vehicle are [40,1.5 ]]Run in the reverse direction of the intelligent vehicle No. 2The starting point coordinate of the obstacle vehicle is [45, -1.5 ]]The intelligent vehicle runs in the same direction as the intelligent vehicle; set the coordinates of the target point to [99,1.75 ]]In the left lane; meanwhile, the standard width d of the road-taking sign is 3.5 meters, the width w of the automobile is 1.6 meters, and the safe distance rho is 0 In the elliptic action area of the obstacle, the radius d of the circle dividing the target point area is taken as 5 meters A and 2 meters B 0 5 m.
The simulation result of the path planning of the intelligent vehicle for controlling the virtual force direction is shown in fig. 5, and when an obstacle moves forward and meets [10,1.75 ]]When a static barrier is located, the intelligent vehicle, the barrier and the target point are in the same straight line, the intelligent vehicle smoothly avoids the first barrier and subsequently smoothly avoids [ 25-1.5 ]]When the intelligent vehicle approaches to the No. 1 obstacle vehicle which drives in opposite directions, the intelligent vehicle smoothly drives from the left lane to the right lane and subsequently smoothly avoids the No. 2 obstacle vehicle which drives in the same direction and the No. 90,1.75 obstacle vehicles]A static barrier is arranged, and finally the intelligent vehicle enters the vehicle with the radius d 0 The virtual gravitation direction is changed in the circular area, and the intelligent vehicle smoothly arrives at the target point.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. An intelligent vehicle path planning method for controlling virtual force direction is characterized by comprising the following steps:
step 1), obtaining global environment information of a current road section, establishing a coordinate system, defining a direction parallel to a road as an X axis, defining a direction perpendicular to the road as a Y axis, wherein a dotted line between two lanes corresponds to a Y-axis 0 coordinate, and defining an intersection point of a perpendicular line made from an intelligent vehicle starting point to the dotted line of the lane and the dotted line of the lane as a coordinate origin; the global environment information comprises an intelligent vehicle starting point position, an obstacle position and a target point position, wherein the obstacle position comprises the position of a dynamic obstacle vehicle and the position of a static obstacle;
step 2), acquiring road information of the current road section according to the established coordinate system, and establishing a target point virtual attraction potential field, an obstacle virtual repulsion potential field and a road boundary virtual repulsion potential field, wherein the target point virtual attraction potential field is used for guiding the intelligent vehicle to advance to a target point; the obstacle virtual repulsive force potential field is used for guiding the intelligent vehicle to avoid the obstacle in the driving process; the road boundary virtual repulsive force potential field is used for limiting the intelligent vehicle not to cross the lane boundary and simultaneously not to occupy the driving of two lanes; the direction of virtual repulsion generated by the road boundary virtual repulsion potential field is vertical to the X axis;
step 3), executing a direction control strategy of the virtual attraction and the virtual repulsion according to the conditions of the virtual repulsion and the virtual attraction on the real-time position of the intelligent vehicle until the intelligent vehicle enters a preset target position, wherein the direction control strategy of the virtual attraction and the virtual repulsion is as follows:
step 3.1), the virtual gravitation direction of the intelligent vehicle is controlled by the target point through the Euclidean distance delta d between the intelligent vehicle and the target point, and the radius is d 0 The circle divides the action area of the potential field of the target point into two areas when delta d>d 0 Controlling the virtual gravitation direction of the target point to the intelligent vehicle to be positive along the X axis; when Δ d<d 0 Controlling the direction of the virtual attraction of the target point to the intelligent vehicle to be the direction in which the intelligent vehicle points to the target point;
step 3.2), the virtual repulsion direction of the intelligent vehicle is controlled by the obstacle through the lane where the obstacle is located, and when the obstacle is located in the right lane, the virtual repulsion direction of the obstacle to the intelligent vehicle is controlled to be in the positive direction along the Y axis; when the obstacle is in the left lane, the direction of the virtual repulsion force of the obstacle to the intelligent vehicle is controlled to be negative along the Y axis;
and 3.3), the direction of the virtual attraction force borne by the intelligent vehicle is vertical to the direction of the virtual repulsion force, and the virtual repulsion force does not have a component opposite to the virtual attraction force.
2. An intelligent vehicle path planning method for controlling virtual force direction according to claim 1, wherein the obstacle virtual repulsive potential field in step 2) is
The virtual gravitational potential field of the target point is
With a radius d 0 The circle divides the virtual gravitational potential field of the target point into two areas;
virtual repulsion function of road boundaries
Wherein k is rep Denotes the repulsive force gain coefficient, k att Is a gravitational gain coefficient, k edge Is a road boundary virtual repulsive force gain factor,
representing the euclidean distance between the smart car and the obstacle,
representing the Euclidean distance, rho, between the intelligent vehicle and the target point 0 Indicating a safe distance by
Optimizing the action range of the potential field of the obstacle into an ellipse, wherein | X-X 0 I is the absolute value of the difference value between the abscissa of the intelligent vehicle and the abscissa of the obstacleValue, | Y-Y 0 The absolute value of the difference value between the vertical coordinate of the intelligent vehicle and the vertical coordinate of the obstacle, A is the transverse safe distance for avoiding the collision between the intelligent vehicle and the obstacle, B is the longitudinal safe distance for the intelligent vehicle to cross the obstacle, d is the standard width of a lane on one side, w is the width of the vehicle, and v is the running speed of the intelligent vehicle.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115540896A (en) * | 2022-12-06 | 2022-12-30 | 广汽埃安新能源汽车股份有限公司 | Path planning method, path planning device, electronic equipment and computer readable medium |
| CN117055559A (en) * | 2023-08-30 | 2023-11-14 | 苏州大成运和智能科技有限公司 | Automatic driving vehicle obstacle avoidance method for improving artificial potential field method |
| CN117584952A (en) * | 2024-01-16 | 2024-02-23 | 北京理工大学 | Method and system for constructing dynamic artificial potential field of off-road environment and electronic equipment |
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2022
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115540896A (en) * | 2022-12-06 | 2022-12-30 | 广汽埃安新能源汽车股份有限公司 | Path planning method, path planning device, electronic equipment and computer readable medium |
| CN115540896B (en) * | 2022-12-06 | 2023-03-07 | 广汽埃安新能源汽车股份有限公司 | Path planning method and device, electronic equipment and computer readable medium |
| CN117055559A (en) * | 2023-08-30 | 2023-11-14 | 苏州大成运和智能科技有限公司 | Automatic driving vehicle obstacle avoidance method for improving artificial potential field method |
| CN117584952A (en) * | 2024-01-16 | 2024-02-23 | 北京理工大学 | Method and system for constructing dynamic artificial potential field of off-road environment and electronic equipment |
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