CN118149851A

CN118149851A - Vehicle driving path planning method, device, computer equipment and storage medium

Info

Publication number: CN118149851A
Application number: CN202410346148.4A
Authority: CN
Inventors: 杨贻俊; 管越; 李政; 滕龙
Original assignee: Guoqi Zhikong Chongqing Technology Co ltd; Guoqi Intelligent Control Beijing Technology Co Ltd
Current assignee: Guoqi Zhikong Chongqing Technology Co ltd; Guoqi Intelligent Control Beijing Technology Co Ltd
Filing date: 2024-03-25
Publication date: 2024-06-07

Abstract

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for planning a vehicle driving path, a computer device, and a storage medium, where the method includes: acquiring the speed of a target vehicle, the current position information of the target vehicle, surrounding sensing information within a preset range of the current position information and a preset space which is contained within the preset range and can be used for getting rid of the target vehicle; determining the current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space; acquiring a target coordinate point at a target position contained in a preset space; updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in a preset space to obtain a target curve path; and taking the target curve path as a final planning path or taking a specific curve path formed by polymorphic combination as the final planning path according to the comparison result of the target curve path and the optimal solution.

Description

Vehicle driving path planning method, device, computer equipment and storage medium

Technical Field

The disclosure relates to the technical field of computers, and in particular relates to a planning method and device for a vehicle driving path, computer equipment and a storage medium.

Background

In the actual running process of the current automatic driving automobile, the automobile is required to make a decision in advance when encountering a congestion blocking road section so as to carry out the detouring behavior under the blocking condition, thereby achieving the high efficiency of the automatic driving automobile, more convenient processing of the blocking and the like, and especially on some unstructured roads, the automobile is required to carry out the active detouring behavior to improve the convenience of the automatic driving experience, and the automatic driving automobile is required to actively perform the detouring behavior in an automatic driving automobile system.

In the current vehicle intelligent escape route planning method, a escape route planning method can be performed based on a hybrid A method according to actual position information and perception information of a current host vehicle. However, in the actual searching, the searching time is long and the optimal solution is not obtained due to the fact that the searching is possibly limited by some relatively complex road conditions, and finally, a proper path cannot be provided for the host vehicle to execute. The method for machine learning also comprises the following steps of: the method has the advantages that the current dilemma type is intelligently identified through a machine learning mode according to the parameters of the vehicle sensor by collecting the operation data of a driver, the getting-out scheme is formulated according to the current type, but the premise of the scheme needs a large amount of data for training, and various successfully getting-out related data and methods need to be collected for scheme generation, so that some situations which do not appear in a training set can be encountered in actual operation, the actual operation is not efficient and can not achieve the expected effect, manual intervention is also needed, and meanwhile, the method is not intelligent and convenient.

Therefore, when the related technology is used for planning the intelligent escape path of the vehicle, the problem that the optimal path cannot be obtained and the generation process is not intelligent and convenient exists.

Disclosure of Invention

In view of the above, the present disclosure provides a method, an apparatus, a computer device, and a storage medium for planning a vehicle driving path, so as to solve the problem that an optimal path cannot be obtained and a generating process is not intelligent and convenient enough when a vehicle intelligent escaping and obstacle avoidance path is planned in a related technology.

In a first aspect, the present disclosure provides a method for planning a vehicle travel path, the method including:

Acquiring the speed of a target vehicle, the current position information of the target vehicle, surrounding sensing information within a preset range of the current position information and a preset space which is contained within the preset range and can be used for getting rid of the target vehicle;

Determining the current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space;

Under the condition that the mode state is the target mode state, acquiring a target coordinate point at a target position contained in a preset space;

updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in a preset space to obtain a target curve path, wherein the first random tree is generated by taking the current position information as a root node;

And taking the target curve path as a final planning path or taking a specific curve path formed by polymorphic combination as the final planning path according to the comparison result of the target curve path and the optimal solution, wherein the polymorphic combination is determined according to the current position information, the current course angle of the target vehicle and the turning radius.

In the embodiment of the disclosure, a mode state of the target vehicle is determined by acquiring a speed of the target vehicle, current position information of the target vehicle, surrounding sensing information within a preset range of the current position information and a preset space which is contained in the preset range and can be used for escaping by the target vehicle, a target coordinate point at a target position which is contained in the preset space is acquired under the condition that the mode state is the target mode state, then a generated first random tree is updated according to the current position information, the target coordinate point and any plurality of coordinate points in the preset space to obtain a target curve path, and then a specific curve path which is formed by combining the target curve path as a final planning path or a multi-form determined by the current position information, the heading angle and the turning radius of the target vehicle is determined according to a comparison result of the target curve path and an optimal solution is determined as the final planning path. The method has the advantages that the solving modes of the two curve paths are combined, the final planning path is determined, the actual problem is solved, meanwhile, path planning under the complex condition can be considered, when the proper optimal solution path cannot be acquired based on the target curve path, the successful capability of the detouring path planning under the complex extreme condition is greatly improved by adopting the mode of acquiring the specific curve path, the effect is better, the performance is more stable, and the problems that the optimal path cannot be acquired when the intelligent detouring obstacle avoidance path of the vehicle is planned in the related technology, and the generating process is not intelligent and convenient are solved.

In an alternative embodiment, determining a current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space includes:

And determining that the current mode state of the target vehicle is the target mode state when the vehicle speed is a preset value, the number of obstacles determined by the surrounding sensing information is smaller than a first preset threshold value, and a preset space exists adjacent to the current position information.

In the embodiment of the disclosure, the mode state of the target vehicle is determined by judging the vehicle speed, the surrounding obstacles and the preset space, so as to lay a cushion for the path planning of the target vehicle.

In an alternative embodiment, updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in the preset space to obtain a target curve path includes:

acquiring a plurality of coordinate points in a preset space;

Updating a first random tree according to the inclusion relation between the plurality of coordinate points and the coverage range of the obstacle existing in the preset space and the position relation between the current position information and the plurality of coordinate points;

Determining a new node to be generated according to the updated first random tree;

Adding the new node into the updated first random tree according to the inclusion relation between the new node and the coverage area of the obstacle existing in the preset space until the distance between the new node and the target coordinate point is smaller than a second preset threshold value, generating a second random tree, and taking the connecting lines among all the nodes contained in the second random tree as a planning path;

And carrying out smoothing treatment on the planned path to obtain a target curve path.

In the embodiment of the disclosure, the first random tree is updated through the inclusion relation between a plurality of coordinate points in the preset space and the coverage range of the obstacle existing in the preset space and the position relation between the current position information and the plurality of coordinate points, meanwhile, new nodes are generated according to the updated first random tree, a second random tree is obtained, and then a planning path is determined.

In an alternative embodiment, updating the first random tree according to the inclusion relationship between the plurality of coordinate points and the coverage of the obstacle existing in the preset space and the positional relationship between the current position information and the plurality of coordinate points includes:

if the coordinate points are contained in the coverage area of the obstacle, selecting the coordinate points in a preset space again;

If any reference coordinate point in the plurality of coordinate points is not contained in the coverage area of the obstacle, adding the reference coordinate point into the first random tree to obtain an updated first random tree;

If the plurality of coordinate points are not contained in the coverage area of the obstacle or the preset number of coordinate points are not contained in the coverage area of the obstacle, selecting a specific coordinate point closest to the current position information from the plurality of coordinate points or the preset number of coordinate points, and adding the specific coordinate point into the first random tree to obtain an updated first random tree.

In an alternative embodiment, adding the new node to the updated first random tree according to the inclusion relationship between the new node and the coverage of the obstacle existing in the preset space includes:

acquiring the growth direction and the growth distance of the new node;

determining whether to add the new node into the updated first random tree according to the growth direction, the growth distance and the inclusion relation between the new node and the coverage area of the obstacle existing in the preset space;

And when the growth direction meets the preset direction, the growth distance meets the preset distance, and the new node is not included in the coverage range of the obstacle, adding the new node into the updated first random tree.

In the embodiment of the disclosure, whether the new node is added into the updated first random tree is confirmed by acquiring the growth direction and the growth distance of the new node and the inclusion relation between the new node and the coverage range of the obstacle existing in the preset space, so that the next node of the random tree is conveniently and regularly obtained, and the finally obtained planning path is in a seal and circulated mode and accords with the driving habit of the target vehicle.

In an alternative embodiment, according to a comparison result of the target curve path and the optimal solution, taking the target curve path as a final planned path or taking a specific curve path composed of polymorphic combinations as the final planned path, includes:

Judging whether the target curve path is an optimal solution or not;

under the condition that the target curve path is the optimal solution, the target curve path is used as a final planning path;

Under the condition that the target curve path is not the optimal solution, acquiring the current course angle and turning radius of the target vehicle;

Determining a polymorphic combination corresponding to the target vehicle and a running direction and a running distance of each form according to the current position information, the current course angle and the turning radius;

Obtaining a specific curve path according to the running direction and the running distance;

The specific curve path is taken as a final planning path.

In the embodiment of the disclosure, the obtained target curve path is compared with the optimal solution path, if the target curve path is the optimal solution, the target curve path is directly used as the final planning path, and if the target curve path is not the optimal solution, the path planning operation under the complex condition is performed by obtaining the specific curve path, and the two modes are combined, so that the solution of the actual problem is more facilitated, and the success rate of the detouring path planning under the complex extreme condition is improved.

In an alternative embodiment, determining the corresponding polymorphic combination of the target vehicle and the driving direction and the driving distance of each form according to the current position information, the current course angle and the turning radius comprises:

determining a polymorphic combination and a driving direction of each form according to the current position information and the current course angle;

Determining new heading coordinates of the target vehicle according to the driving direction, the current position information and the turning radius;

And determining the driving distance of each form according to the new heading coordinate.

In the embodiment of the disclosure, the driving direction of each form in the polymorphic combination is determined through the current position information, and then the driving distance of each form in the polymorphic combination is determined based on the new heading coordinate, so that the solving space is increased under some complex extreme conditions.

In a second aspect, the present disclosure provides a planning apparatus for a vehicle travel path, the apparatus comprising:

The first acquisition module is used for acquiring the speed of the target vehicle, the current position information of the target vehicle, the surrounding sensing information within the preset range of the current position information and a preset space which is contained in the preset range and can be used for getting rid of the trapping of the target vehicle;

The determining module is used for determining the current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding perception information and the preset space;

the second acquisition module is used for acquiring a target coordinate point at a target position contained in a preset space under the condition that the mode state is a target mode state;

The updating module is used for updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in a preset space to obtain a target curve path, wherein the first random tree is generated by taking the current position information as a root node;

The setting module is used for taking the target curve path as a final planning path or taking a specific curve path formed by polymorphic combination as the final planning path according to the comparison result of the target curve path and the optimal solution, wherein the polymorphic combination is determined according to the current position information, the current course angle of the target vehicle and the turning radius.

In a third aspect, the present disclosure provides a computer device comprising: the vehicle travel path planning method according to the first aspect or any one of the embodiments thereof is implemented by the processor and the memory, the memory and the processor are in communication connection with each other, and the memory stores computer instructions, and the processor executes the computer instructions.

In a fourth aspect, the present disclosure provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the method for planning a vehicle travel path according to the first aspect or any one of the embodiments corresponding thereto.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the related art, the drawings that are required to be used in the description of the embodiments or the related art will be briefly described below, and it is apparent that the drawings in the following description are some embodiments of the present disclosure, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.

FIG. 1 is a flow diagram of a method of planning a vehicle travel path according to some embodiments of the present disclosure;

FIG. 2 is an overall flow diagram of a method of planning a vehicle travel path according to some embodiments of the present disclosure;

FIG. 3 is a block diagram of a vehicle path planning apparatus according to some embodiments of the present disclosure;

Fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person skilled in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.

In the actual running process of the current automatic driving automobile, when the automobile encounters a congestion and blocks a road section, the automobile is required to make a decision in advance so as to perform the escape behavior under the condition of blocking, thereby achieving the conditions of high efficiency, more convenience and quickness in handling the blocking and the like of the automatic driving automobile.

The existing method for planning out a getting rid of poverty route based on the mixed A method according to the actual position information and the perception information of the current host vehicle may be limited by some relatively complex road conditions in the actual searching, so that the situation that the searching is long in time consumption and the optimal solution is not obtained is caused, and finally, the method cannot be executed by providing a proper route for the host vehicle; the selection of the getting rid of poverty scene is performed by acquiring the operation data of the driver and the data of the sensor in combination with the machine learning method, and the method of making the scheme is performed for different scenes, so that a great amount of driving data and related data are required to be collected and trained, and the situation of searching for the advance in the training set is not necessarily generated in the actual operation, so that the method is not direct, efficient and convenient, and the expected path and the expected effect are not achieved.

In order to solve the above-described problems, according to an embodiment of the present disclosure, there is provided an embodiment of a vehicle travel path planning method, it is to be noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that herein.

In this embodiment, a method for planning a vehicle travel path is provided, fig. 1 is a flowchart of a method for planning a vehicle travel path according to an embodiment of the disclosure, and as shown in fig. 1, the method may be applied to a server side, and the method includes the following steps:

Step S101, obtaining a speed of the target vehicle, current position information of the target vehicle, surrounding sensing information within a preset range of the current position information, and a preset space included in the preset range for the target vehicle to get rid of the trapping.

Optionally, in the embodiment of the present disclosure, the current vehicle speed is obtained by first obtaining chassis information of the target vehicle, then obtaining current position information of the target vehicle through a vehicle GPS positioning system, and simultaneously obtaining surrounding sensing information of the current position information within a certain preset range (for example, within 3 meters) through a vehicle sensor. It is understood that the surrounding awareness information may be some obstacle information; the target vehicle may be any one of automobiles in road running.

In addition, it is also necessary to acquire whether a preset space in which the target vehicle can get rid of the jam exists within the preset range. It is understood that the preset space may be an area space of 5-10 target vehicle bodies, and if the preset space exists around the target vehicle currently, the target vehicle may enter the preset space to get rid of the detouring.

Step S102, determining the current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space.

Optionally, the mode state that the target vehicle should be currently in is determined according to the current speed of the target vehicle, the current position information of the target vehicle, the sensing information around the target vehicle and the condition of the preset space around the target vehicle. It should be noted that, the mode state may include a waiting state, an initial preheating state for entering the detouring, a formal entering the detouring state, and the like.

Step S103, when the mode state is the target mode state, acquiring a target coordinate point at a target position included in the preset space.

Optionally, in an embodiment of the present disclosure, the formally entered detouring state is taken as the target mode state. After determining that the target vehicle enters the target mode state, taking the current position information (corresponding to a coordinate point) of the target vehicle as a starting point, selecting a proper target position at a preset space available for detouring, and marking the target coordinate point as an end point.

It should be noted that, the appropriate target position herein refers to a coordinate point of the maximum number of body areas where the target vehicle can successfully achieve the detouring, and of course, the actual existence of the surrounding obstacle needs to be considered when determining the maximum number. After the surrounding obstacles are combined, a coordinate point with the target position being the ith vehicle body tail can be obtained, and i can be 8,7 and the like.

Step S104, updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in a preset space to obtain a target curve path, wherein the first random tree is generated by taking the current position information as a root node.

Optionally, the current position information of the target vehicle is taken as a starting point, the current position information is taken as a root node, the target coordinate point is taken as an end point, a first random tree is generated by the starting point and the end point, and a method of RRT (Rapidly-Exploring Random Tree, rapid random search tree) is used for participating in path planning calculation.

After the first random tree is obtained, the first random tree can be updated according to the current position information of the target vehicle, the target coordinate point and any plurality of coordinate points selected from a preset space, and then a target curve path is obtained.

Step S105, according to the comparison result of the target curve path and the optimal solution, taking the target curve path as a final planning path or taking a specific curve path composed of polymorphic combinations as the final planning path, wherein the polymorphic combinations are determined according to the current position information, the current course angle of the target vehicle and the turning radius.

Optionally, after the target curve path is obtained according to the RRT algorithm, the target curve path is compared with an optimal solution (i.e., a reasonable path without collision) to determine whether the target curve path is an optimal solution. If the target curve path is the optimal solution, the target curve path is directly used as a final planning path; if the solution is not the optimal solution, a specific curve path library is combined in a mode of generating a specific curve, so that a specific curve path formed by combining a plurality of modes of left turning, right turning and straight going is obtained, and a final planning path is obtained.

It should be understood that the polymorphism is actually three modes of left turn, right turn and straight run, and the determination modes of the three modes are determined by the current position information of the target vehicle, the current course angle of the target vehicle and the turning radius.

In some optional embodiments, determining the current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space includes:

Optionally, when the vehicle speed is a preset value (i.e. the vehicle speed is 0), the target vehicle is considered to enter a blocked environment state, the target vehicle enters an initial warm-up state of getting rid of the detour, then whether the target vehicle formally enters the state of getting rid of the detour is further required to judge whether the number of peripheral obstacles of the target vehicle is smaller than a first preset threshold (for example, 3), if the number of peripheral obstacles is smaller than the first preset threshold, the peripheral obstacles are considered to be less, then whether a preset space capable of getting rid of the detour exists in the vicinity of the target vehicle further, and if the preset space also exists, the target vehicle is controlled to formally enter the target mode state of getting rid of the detour.

It is understood that even if the vehicle speed of the target vehicle is 0, if the target vehicle senses that there are too many surrounding obstacles and there is no preset space in which escape is possible at all, the target vehicle continues to wait in the current state.

In some optional embodiments, updating the generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in the preset space to obtain a target curve path includes:

acquiring a plurality of coordinate points in a preset space;

Optionally, the first random tree is initialized by using the RRT algorithm, and the current position information of the target vehicle is used as a starting point of the random tree search, where the first random tree includes a node, that is, a root node, that is, the current position information is used as the root node. And randomly sampling from a preset space to obtain a plurality of sampled coordinate points, determining the coordinate points added into the first random tree to serve as newly added nodes according to the inclusion relation between the plurality of coordinate points and the coverage range of the obstacle existing in the preset space and the position relation between the current position information and the plurality of coordinate points, and updating the first random tree to obtain an updated first random tree.

Then, a new node to be generated in the updated first random tree needs to be obtained, and whether the new node is added to the updated first random tree can be determined according to the inclusion relation between the new node and the coverage range of the obstacle existing in the preset space. If the new node is not in the coverage area of the obstacle, the new node is considered to belong to a safety node, the new node can be added into the updated first random tree, otherwise, resampling is performed until the distance between the newly added new node and the target coordinate point is smaller than a second preset threshold (for example, 0.1 meter), the current new node is considered to be the last path node added into the updated first random tree, after all the path nodes are obtained, the random tree generated by the path nodes is called a second random tree, and then all the nodes in the second random tree are connected to obtain a planned path.

And carrying out smoothing treatment on the planned path, for example, carrying out smoothing treatment on the planned path through a B spline curve to obtain a target curve path. The B spline curve can be used for fitting the preposed path points into a curve as a target curve path, and the general expression of the B spline curve is as follows:

wherein t _min≤t≤t_max, d is more than or equal to 2 and n is more than or equal to n

In the above-mentioned formula(s),Represents a point coordinate vector on the curve, n is the control point/>Quantity of/(I)Representing the control point coordinates (i starts from 0), B _i,d(t) represents the polynomial coefficient of the control point coordinates influencing the weight (where i represents the index of the coordinates, n represents the highest power of the polynomial), d influences the degree of the B-spline curve, and t is the value when the curve is drawn.

In some optional embodiments, updating the first random tree according to the inclusion relationship between the plurality of coordinate points and the coverage of the obstacle existing in the preset space and the positional relationship between the current position information and the plurality of coordinate points includes:

Optionally, when the node is added to the first random tree, the inclusion relationship between the plurality of coordinate points and the coverage area of the obstacle existing in the preset space may be acquired first to update the inclusion relationship: if the plurality of coordinate points are included in the coverage area of the obstacle, selecting the coordinate points in a preset space again, and not executing the operation of adding the first random tree node; if any reference coordinate point in the plurality of coordinate points is not contained in the coverage area of the obstacle, adding the reference coordinate point into the first random tree to obtain an updated first random tree; if the plurality of coordinate points are not included in the coverage area of the obstacle or a preset number (such as 2 or more) of coordinate points are not included in the coverage area of the obstacle, a specific coordinate point closest to the current position information is selected according to the position relationship between the current position information and the coordinate points, and the specific coordinate point is added into the first random tree to obtain an updated first random tree.

acquiring the growth direction and the growth distance of the new node;

Optionally, since the current new node is generated after the updated first random tree, and the updated first random tree at least includes a root node (i.e., a starting point), a newly added leaf node, and a leaf node with a degree of 0 (i.e., an end point), the existing nodes in the first random tree are connected, so that a growth direction of the new node to be generated subsequently can be determined, and a growth distance of the new node is obtained at the same time, so that whether the new node is added into the updated first random tree is determined according to the growth direction, the growth distance, and an inclusion relationship between the new node and an obstacle coverage area existing in a preset space.

Further, the embodiment of the present disclosure may set a preset distance (for example, 0.5 m) for the growth of the new node, if the new node to be generated satisfies a preset direction corresponding to an existing node connection line in the first random tree, and meanwhile, the growth distance of the new node satisfies the preset distance, and the new node is not included in the coverage area of the obstacle, the new node is added into the updated first random tree.

In some alternative embodiments, the comparing the target curve path with the optimal solution according to the comparison result of the target curve path and the optimal solution, using the target curve path as a final planning path or using a specific curve path composed of polymorphic combinations as the final planning path, includes:

Judging whether the target curve path is an optimal solution or not;

The specific curve path is taken as a final planning path.

Optionally, under the condition that the target curve path is determined to be the optimal solution, taking the target curve path as a final planning path; and under the condition that the target curve path is not the optimal solution, acquiring the current course angle and turning radius of the target vehicle.

According to the current position information, the current course angle and the turning radius, the combination path of multiple forms of the target vehicle, the running direction and the running distance of each form can be judged. For example, the current position information of the target vehicle is (x, y), and the current heading angle is(/>An angle formed by the steering direction and the original advancing direction of the vehicle), wherein the azimuth coordinate isAt this time, the position coordinate is/>The driving direction is obtained by obtaining whether the target vehicle needs to turn left or right or go straight at present. And then, based on the turning radius R, the driving distance of each form is obtained.

After the running direction and the running distance of each form are determined, a specific curve path can be generated, and then the specific curve path is used as a final planning path.

Optionally, if the coordinates of the current position information of the target vehicle are (x, y), the heading angle isInitial azimuth coordinates areWhen the turning radius is R and the straight running distance is L, the new heading coordinate can be calculated by the related geometric relation formula as follows:

when the detouring path is generated in the left direction, the left-turn angle of the vehicle is assumed to be Turning radius R, then the new heading coordinate is:

When the detouring path is generated in the rightward direction, it is assumed that the right turn angle of the vehicle is also Turning radius R, then the new heading coordinate is:

normalizing the calculation mode, namely if R is 1, going through I.e. the length of the arc and straight line travelled at this time can be denoted L, then:

Wherein, Represents the travel distance at the time of left turn; /(I)Represents the travel distance at the time of right turn; Representing the distance travelled during straight-ahead travel.

For example, after knowing the initial coordinates (0, α) and the final coordinates (d, 0, β) of the vehicle, the path may be generated in a circular arc+straight line manner, and the driving direction is obtained according to the initial coordinates and the heading angle: the vehicle turns left by t circular arcs, then goes straight by p and turns left by q, and the normalized formula is combined with

L_q(S_p(L_t(0，0，α)))＝(d，0，β)

Solving the above formula:

(-sin(α)+p*cos(α+t)+sin(α+t+q)，cos(α)+p*sin(α+t)

-cos(α+t+q)，a+t+q)＝(0，0，d)

Continuing to solve the following can obtain:

Where t is the distance traveled by the vehicle first turning left; p is the travel distance of the rear straight run; q is the travel distance following the left turn.

In some alternative embodiments, as shown in fig. 2, fig. 2 is an overall flow chart of a planning method for a vehicle driving path according to some embodiments of the disclosure, and the specific flow is as follows:

Acquiring chassis information and current speed information of an automobile;

Judging whether the blocking situation exists or not;

If the device is in a blocking condition, entering a detouring initial mode; if the vehicle is not in the blocking condition, the vehicle continues to run;

after entering the detouring initial mode, judging whether a detouring space exists or not;

if the escape space does not exist, continuing waiting; if the escape space exists, selecting an end position in the escape space, and calling an RRT algorithm to calculate so as to acquire a path;

judging whether a proper path is successfully acquired at present;

if a proper path is obtained, carrying out optimization treatment by using a B spline curve; executing the subsequent path; if the proper path is not acquired, executing a method for generating a specific curve path library to acquire the path; and executing the subsequent path.

The present embodiment also provides a vehicle driving path planning device, which is used to implement the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The present embodiment provides a planning apparatus for a vehicle travel path, as shown in fig. 3, including:

The first obtaining module 301 is configured to obtain a speed of the target vehicle, current position information of the target vehicle, surrounding sensing information within a preset range of the current position information, and a preset space included in the preset range and available for the target vehicle to get rid of poverty;

The determining module 302 is configured to determine a current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding sensing information and the preset space;

A second obtaining module 303, configured to obtain, when the mode state is a target mode state, a target coordinate point at a target position included in a preset space;

The updating module 304 is configured to update the generated first random tree according to the current location information, the target coordinate point, and any plurality of coordinate points in the preset space, to obtain a target curve path, where the first random tree is generated by using the current location information as a root node;

The setting module 305 is configured to take the target curve path as a final planned path or take a specific curve path composed of a polymorphic combination as a final planned path according to a comparison result of the target curve path and the optimal solution, where the polymorphic combination is determined according to the current position information, the current heading angle of the target vehicle, and the turning radius.

In some optional embodiments, the determining module 302 is specifically configured to determine that the mode state in which the target vehicle is currently located is the target mode state when the vehicle speed is a preset value, the number of obstacles determined by the surrounding sensing information is less than a first preset threshold, and a preset space exists adjacent to the current location information.

In some optional embodiments, the updating module 304 is specifically configured to obtain a plurality of coordinate points in a preset space; updating a first random tree according to the inclusion relation between the plurality of coordinate points and the coverage range of the obstacle existing in the preset space and the position relation between the current position information and the plurality of coordinate points; determining a new node to be generated according to the updated first random tree; adding the new node into the updated first random tree according to the inclusion relation between the new node and the coverage area of the obstacle existing in the preset space until the distance between the new node and the target coordinate point is smaller than a second preset threshold value, generating a second random tree, and taking the connecting lines among all the nodes contained in the second random tree as a planning path; and carrying out smoothing treatment on the planned path to obtain a target curve path.

In some optional embodiments, the updating module 304 is specifically configured to, if the plurality of coordinate points are all included in the coverage area of the obstacle, re-select the coordinate points in the preset space; if any reference coordinate point in the plurality of coordinate points is not contained in the coverage area of the obstacle, adding the reference coordinate point into the first random tree to obtain an updated first random tree; if the plurality of coordinate points are not contained in the coverage area of the obstacle or the preset number of coordinate points are not contained in the coverage area of the obstacle, selecting a specific coordinate point closest to the current position information from the plurality of coordinate points or the preset number of coordinate points, and adding the specific coordinate point into the first random tree to obtain an updated first random tree.

In some optional embodiments, the updating module 304 is specifically configured to obtain a growth direction and a growth distance of the new node; determining whether to add the new node into the updated first random tree according to the growth direction, the growth distance and the inclusion relation between the new node and the coverage area of the obstacle existing in the preset space; and when the growth direction meets the preset direction, the growth distance meets the preset distance, and the new node is not included in the coverage range of the obstacle, adding the new node into the updated first random tree.

In some optional embodiments, the setting module 305 is specifically configured to determine whether the target curve path is an optimal solution; under the condition that the target curve path is the optimal solution, the target curve path is used as a final planning path; under the condition that the target curve path is not the optimal solution, acquiring the current course angle and turning radius of the target vehicle; determining a polymorphic combination corresponding to the target vehicle and a running direction and a running distance of each form according to the current position information, the current course angle and the turning radius; obtaining a specific curve path according to the running direction and the running distance; the specific curve path is taken as a final planning path.

In some optional embodiments, the setting module 305 is specifically configured to determine, according to the current location information and the current heading angle, a polymorphic combination and a driving direction of each form; determining new heading coordinates of the target vehicle according to the driving direction, the current position information and the turning radius; and determining the driving distance of each form according to the new heading coordinate.

The vehicle path planning apparatus in this embodiment is presented in the form of functional units, where the units refer to ASIC circuits, processors and memories executing one or more software or firmware programs, and/or other devices that can provide the above-described functions.

Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.

The embodiment of the disclosure also provides a computer device, which is provided with the planning device for the vehicle driving path shown in the figure 3.

Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the disclosure, as shown in fig. 4, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 4.

The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.

Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.

The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created from the use of the computer device of the presentation of a sort of applet landing page, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.

The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

The presently disclosed embodiments also provide a computer readable storage medium, and the methods described above according to the presently disclosed embodiments may be implemented in hardware, firmware, or as recordable storage medium, or as computer code downloaded over a network that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and is to be stored in a local storage medium, such that the methods described herein may be stored on such software processes on a storage medium using a general purpose computer, special purpose processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.

Although embodiments of the present disclosure have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the disclosure, and such modifications and variations are within the scope defined by the appended claims.

Claims

1. A method of planning a vehicle travel path, the method comprising:

acquiring the speed of a target vehicle, the current position information of the target vehicle, surrounding sensing information within a preset range of the current position information and a preset space which is contained in the preset range and can be used for getting rid of the trapping of the target vehicle;

Determining a current mode state of the target vehicle according to the vehicle speed, the current position information, the surrounding perception information and the preset space;

Acquiring a target coordinate point at a target position contained in the preset space under the condition that the mode state is a target mode state;

updating a generated first random tree according to the current position information, the target coordinate point and any plurality of coordinate points in a preset space to obtain a target curve path, wherein the first random tree is generated by taking the current position information as a root node;

2. The method according to claim 1, wherein the determining the mode state in which the target vehicle is currently in according to the vehicle speed, the current position information, the surrounding perceived information, and the preset space includes:

And when the vehicle speed is a preset value, the number of the obstacles determined by the surrounding sensing information is smaller than a first preset threshold value, and the preset space exists adjacent to the current position information, determining that the current mode state of the target vehicle is the target mode state.

3. The method of claim 1, wherein updating the generated first random tree according to the current location information, the target coordinate point, and any plurality of coordinate points in a preset space to obtain a target curve path includes:

acquiring a plurality of coordinate points in the preset space;

updating the first random tree according to the inclusion relation between the coordinate points and the coverage range of the obstacle existing in the preset space and the position relation between the current position information and the coordinate points;

Adding the new node into the updated first random tree according to the inclusion relation between the new node and the coverage area of the obstacle existing in the preset space until the distance between the new node and the target coordinate point is smaller than a second preset threshold value, generating a second random tree, and taking the connection lines between all the nodes included in the second random tree as a planning path;

And carrying out smoothing treatment on the planning path to obtain the target curve path.

4. The method according to claim 3, wherein the updating the first random tree according to the inclusion relationship between the plurality of coordinate points and the coverage of the obstacle existing in the preset space and the positional relationship between the current positional information and the plurality of coordinate points includes:

if the coordinate points are contained in the coverage area of the obstacle, selecting the coordinate points in the preset space again;

If any reference coordinate point in the coordinate points is not contained in the coverage range of the obstacle, adding the reference coordinate point into the first random tree to obtain an updated first random tree;

If the coordinate points are not contained in the coverage area of the obstacle or the preset number of coordinate points are not contained in the coverage area of the obstacle, selecting a specific coordinate point closest to the current position information from the coordinate points or the preset number of coordinate points, and adding the specific coordinate point into the first random tree to obtain an updated first random tree.

5. A method according to claim 3, wherein said adding the new node to the updated first random tree according to the inclusion relationship between the new node and the coverage of the obstacle existing in the preset space comprises:

Acquiring the growth direction and the growth distance of the new node;

And if the growth direction meets a preset direction, the growth distance meets a preset distance, and the new node is not included in the coverage range of the obstacle, adding the new node into the updated first random tree.

6. The method according to claim 1, wherein the step of taking the target curve path as a final planned path or taking a specific curve path composed of a polymorphic combination as the final planned path according to a comparison result of the target curve path and an optimal solution includes:

judging whether the target curve path is the optimal solution or not;

Taking the target curve path as the final planning path under the condition that the target curve path is the optimal solution;

Acquiring a current course angle and the turning radius of the target vehicle under the condition that the target curve path is not the optimal solution;

determining a polymorphic combination corresponding to the target vehicle, and a driving direction and a driving distance of each form according to the current position information, the current course angle and the turning radius;

obtaining the specific curve path according to the running direction and the running distance;

And taking the specific curve path as the final planning path.

7. The method of claim 6, wherein determining the corresponding polymorphic combination of the target vehicle and the driving direction and driving distance of each modality based on the current location information, the current heading angle, and the turning radius comprises:

determining the polymorphic combination and the driving direction of each form according to the current position information and the current course angle;

8. A vehicle travel path planning apparatus, characterized by comprising:

The first acquisition module is used for acquiring the speed of a target vehicle, the current position information of the target vehicle, surrounding sensing information within a preset range of the current position information and a preset space which is contained in the preset range and can be used for getting rid of the trapping of the target vehicle;

the second acquisition module is used for acquiring a target coordinate point at a target position contained in the preset space under the condition that the mode state is a target mode state;

And the setting module is used for taking the target curve path as a final planning path or taking a specific curve path formed by polymorphic combination as the final planning path according to the comparison result of the target curve path and the optimal solution, wherein the polymorphic combination is determined according to the current position information, the current course angle of the target vehicle and the turning radius.

9. A computer device, comprising:

a memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of planning a vehicle path of travel of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the vehicle travel path planning method according to any one of claims 1 to 7.