CN116929399A

CN116929399A - Driving path searching method, device, equipment and automatic driving vehicle

Info

Publication number: CN116929399A
Application number: CN202311015338.XA
Authority: CN
Inventors: 王丕阁
Original assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Current assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Priority date: 2023-08-11
Filing date: 2023-08-11
Publication date: 2023-10-24

Abstract

The disclosure provides a driving path searching method, device and equipment and an automatic driving vehicle, relates to the field of automatic driving, and particularly relates to the technical field of driving path planning. The implementation scheme is as follows: determining an initial target node position based on the current position of the vehicle and an initial search step, wherein the initial target node position is a node position with the minimum search cost in a plurality of candidate node positions; determining whether an obstacle collides with the vehicle when the vehicle is at an initial target node position; in response to the vehicle being at the initial target node position, there is an obstacle colliding with the vehicle: shortening the initial searching step length to obtain a target searching step length, so that no obstacle collides with the vehicle when the vehicle is at a first position reached after the target searching step length is moved from the current position; and determining the first location as a target node location of the vehicle.

Description

Driving path searching method, device, equipment and automatic driving vehicle

Technical Field

The present disclosure relates to the field of autopilot, and in particular to the technical field of driving path planning, and more particularly to a driving path searching method, apparatus, electronic device, computer-readable storage medium, computer program product, and autopilot vehicle.

Background

Today in the field of autopilot technology, more and more people are focusing on the problem of open scene path planning with global optimality. With the development of automatic driving technology, users have put higher demands on how to perform route expansion faster in the course of driving route search, which has also been a hot spot of research in the art.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.

Disclosure of Invention

The present disclosure provides a driving path search method, apparatus, electronic device, computer-readable storage medium, computer program product, and autonomous vehicle.

According to an aspect of the present disclosure, there is provided a driving path search method including: determining an initial target node position based on the current position of the vehicle and an initial search step, wherein the initial target node position is a node position with the minimum search cost in a plurality of candidate node positions; determining whether an obstacle collides with the vehicle when the vehicle is at an initial target node position; in response to the vehicle being at the initial target node position, there is an obstacle colliding with the vehicle: shortening the initial searching step length to obtain a target searching step length, so that no obstacle collides with the vehicle when the vehicle is at a first position reached after the target searching step length is moved from the current position; and determining the first location as a target node location of the vehicle.

According to another aspect of the present disclosure, there is provided a driving path search apparatus including: an initial target node position determining module configured to determine an initial target node position based on a current position of the vehicle and an initial search step, wherein the initial target node position is a node position with a minimum search cost among the plurality of candidate node positions; a collision determination module configured to determine whether an obstacle collides with the vehicle when the vehicle is at an initial target node position; the target searching step length determining module is configured to shorten the initial searching step length to obtain a target searching step length in response to the collision of the obstacle with the vehicle when the vehicle is at the initial target node position, so that the collision of the obstacle with the vehicle is not generated when the vehicle is at the first position reached after the target searching step length is moved from the current position; and a first target node position determination module configured to determine the first position as a target node position of the vehicle in response to the presence of an obstacle colliding with the vehicle while the vehicle is at the initial target node position.

According to another aspect of the present disclosure, there is provided an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods of the present disclosure as provided above.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of the present disclosure as provided above.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method as provided above.

According to another aspect of the present disclosure, there is provided an autonomous vehicle comprising a controller, wherein the controller is configured to perform the method of the present disclosure as provided above.

According to one or more embodiments of the present disclosure, path expansion can be performed faster in the course of driving path search.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a flow chart of a driving path search method according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of a step size determination process according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of a step size determination process according to another embodiment of the present disclosure;

FIG. 5 shows a flowchart of a step size determination process according to another embodiment of the present disclosure;

fig. 6 shows a block diagram of a driving path search apparatus according to an embodiment of the present disclosure;

fig. 7 shows a block diagram of a driving path search apparatus according to another embodiment of the present disclosure;

fig. 8 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another element. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.

The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.

Today in the field of autopilot technology, more and more people are focusing on the problem of open scene path planning with global optimality. With the development of automatic driving technology, users have put higher demands on how to perform route expansion faster in the course of driving route search.

In the related art, in order to perform driving route search, it is often necessary to perform route expansion in units of smaller steps. In this manner, each path segment has a particular direction and step size. If each section of path is connected end to end in sequence, the driving path determined by searching can be obtained. In one conventional driving path search approach, the search step used to determine each path segment has a fixed length. The traditional fixed step search strategy has low space utilization rate, so that the search success rate and speed of the strategy cannot reach expectations in narrow spaces with more obstacles. In another conventional driving path search method, the search step is discretely preset to some specific length, and then a non-collision search step is determined in a discrete space by means of enumeration or binary search. However, in this way, due to the discrete error, the final search step cannot be practically guaranteed to be the maximum non-collision step, so that the space utilization is still not maximized and the speed of path expansion is still affected.

Therefore, a method capable of performing route expansion faster in the course of driving route search is demanded.

In view of the above technical problems, according to one aspect of the present disclosure, a driving path search method is provided.

Before describing in detail the driving path searching method according to an embodiment of the present disclosure, a schematic diagram of an exemplary system in which the various methods and apparatuses described herein may be implemented is first described in connection with fig. 1.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.

In an embodiment of the present disclosure, motor vehicle 110 may include a computing device in accordance with an embodiment of the present disclosure and/or be configured to perform a method in accordance with an embodiment of the present disclosure.

The server 120 may run one or more services or software applications that enable the method of driving the path search. In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user of motor vehicle 110 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of motor vehicle 110.

Network 130 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, the one or more networks 130 may be a satellite communications network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a blockchain network, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, for example, bluetooth, wiFi), and/or any combination of these with other networks.

The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 150 may be used to store information such as audio files and video files. The data store 150 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 150 may be of different types. In some embodiments, the data store used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve the databases and data from the databases in response to the commands.

In some embodiments, one or more of databases 150 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.

Motor vehicle 110 may include a sensor 111 for sensing the surrounding environment. The sensors 111 may include one or more of the following: visual cameras, infrared cameras, ultrasonic sensors, millimeter wave radar, and laser radar (LiDAR). Different sensors may provide different detection accuracy and range. The camera may be mounted in front of, behind or other locations on the vehicle. The vision cameras can capture the conditions inside and outside the vehicle in real time and present them to the driver and/or passengers. In addition, by analyzing the captured images of the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, etc. can be acquired. The infrared camera can capture objects under night vision. The ultrasonic sensor can be arranged around the vehicle and is used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directivity and the like. The millimeter wave radar may be installed in front of, behind, or other locations of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. Lidar may be mounted in front of, behind, or other locations on the vehicle for detecting object edges, shape information for object identification and tracking. The radar apparatus may also measure a change in the speed of the vehicle and the moving object due to the doppler effect.

Motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with the mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as the current or evolving wireless communication technology (e.g., 5G technology) such as GSM/GPRS, CDMA, LTE. The communication device 112 may also have a Vehicle-to-Everything (V2X) module configured to enable, for example, vehicle-to-Vehicle (V2V) communication with other vehicles 143 and Vehicle-to-Infrastructure (V2I) communication with Infrastructure 144. In addition, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smart phone, tablet computer, or wearable device such as a watch), for example, by using a wireless local area network or bluetooth of the IEEE802.11 standard. With the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

Motor vehicle 110 may also include a control device 113. The control device 113 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer readable storage devices or mediums. The control device 113 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain, steering system, braking system, etc. of a motor vehicle 110 (not shown) via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Part of the processing functions of the control device 113 may be implemented by cloud computing. For example, some of the processing may be performed using an onboard processor while other processing may be performed using cloud computing resources. The control device 113 may be configured to perform a method according to the present disclosure. Furthermore, the control means 113 may be implemented as one example of a computing device on the motor vehicle side (client) according to the present disclosure.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

The driving path search method according to the embodiment of the present disclosure is described in detail below.

Fig. 2 shows a flowchart of a driving path search method 200 according to an embodiment of the present disclosure. As shown in fig. 2, the method 200 includes steps S210, S220, S230, and S240.

In step S210, an initial target node position, which is a node position having the smallest search cost among the plurality of candidate node positions, is determined based on the current position of the vehicle and the initial search step.

In step S220, it is determined whether there is an obstacle colliding with the vehicle while the vehicle is at the initial target node position.

In response to the obstacle colliding with the vehicle while the vehicle is at the initial target node position, the initial search step is shortened to obtain a target search step such that the obstacle does not collide with the vehicle while the vehicle is at the first position reached after the target search step is moved from the current position at step S240. In step S250, the first location is determined as a target node location of the vehicle.

In an example, driving path search may be implemented by building a search tree. The search may be started in a search tree from the start of the driving path. When the end point of the driving path is searched, the search may be ended. At this time, the search tree may be traced back, so that the shortest path from the start point to the end point is obtained as the driving path.

In an example, a path of the vehicle moving the target search step toward the current search direction at each current location may be inserted into the search tree as one edge of the search tree. Each edge in the search tree may represent a segment of a sub-path that satisfies the kinematic constraints of the vehicle and is non-collision, which may correspond to a primitive of motion of the vehicle between the respective start and stop nodes. The motion primitive may be obtained by discretizing a control space according to a vehicle kinematic model, and may represent a motion direction of the vehicle (for example, may include six motion directions of forward straight, backward straight, leftward front turning, leftward rear turning, rightward front turning, rightward rear turning), a steering wheel angle, and a motion step.

In an example, the position of one feature point of the vehicle, which may be, for example, the midpoint of the front bumper of the vehicle, or may be, for example, the midpoint between the rear wheels of the vehicle, may be taken as the position of the vehicle.

In an example, a vehicle coordinate system may be established with the above-described feature points of the vehicle as an origin, and the coordinate system may have a longitudinal direction of the vehicle as a horizontal axis and a width direction as a vertical axis. Since the vehicle itself has a certain volume, the actual position occupied by the vehicle in space can be represented in the coordinate system as a polygon having a certain area, for example, as a rectangle corresponding to the length and width of the vehicle.

In an example, if there is a point or shape representing an obstacle among rectangles used in the vehicle coordinate system to represent the actual position occupied by the vehicle in space when the vehicle is at a certain position, the obstacle may be considered to collide with the vehicle when the vehicle is at this position.

In an example, the vehicle may default to no obstacle colliding with the vehicle when in the current position. In some embodiments, the initial step size may be set to be smaller than the length of the vehicle itself or even smaller (e.g., may be set to 0.4 meters). Under such an arrangement, no obstacle collides with the vehicle when the vehicle is at both the current position and the target node position, and then it can be considered that the vehicle collides with the vehicle in moving from the current position to the target node position in the search direction. This approach may be well suited for scenes of high environmental complexity, such as scenes where parking in a parking lot or scenes where driving in a narrow roadway or cell.

In some embodiments, there may not be a first location where the vehicle moves from the current location along the search direction without colliding with the obstacle. In this case, it can be considered that the vehicle does not have a search step in the search direction at the current position. In this case, other node positions may be selected.

According to the driving path searching method, through judging the collision situation of the vehicle with the obstacle when the vehicle is at the initial target node position, the other position which is not collided with the obstacle and the non-collision searching step corresponding to the other position can be determined in a self-adaptive mode under the condition that the initial searching step cannot be directly used as the target searching step, and therefore the efficiency and the success rate of driving path searching can be improved.

Various aspects of the driving path search method according to the embodiments of the present disclosure are described further below.

According to some embodiments, the driving path search method may further include determining the initial target node position as the target node position of the vehicle in response to the vehicle not having an obstacle colliding with the vehicle while at the initial target node position.

According to the embodiment of the disclosure, in the case that the vehicle is in the initial target node position and has no collision with the obstacle, the initial search step is directly used as the target search step, so that the efficiency of driving path search can be improved.

According to some embodiments, in step S240 shown in fig. 2, the initial target node position may be adjusted toward the current position to obtain the first position, until there is no obstacle colliding with the vehicle when the vehicle is at the first position, and then the moving distance between the first position and the current position of the vehicle is determined as the target search step of the vehicle.

According to the method and the device for determining the target search step length, the first position where the obstacle collides with the vehicle is determined, and the corresponding target search step length is determined based on the first position, so that the rapid determination of the target search step length can be facilitated.

According to some embodiments, the number of times the initial target node position is adjusted may be less than or equal to a predetermined number of times threshold.

In an example, the predetermined number of times threshold may be set to three times, for example. When the initial target node position is adjusted three times, the vehicle still collides with the obstacle when the vehicle is at the position, which means that the obstacle is too many in the current searching direction and is not suitable for expanding the driving path in the searching direction, so that the target searching step length of the vehicle along the searching direction at the current position can be considered to be not successfully determined.

According to the embodiment of the disclosure, by presetting the maximum number of times of adjusting the target node position, the iterative computation which consumes excessive time in a certain search direction can be avoided, and the acceleration of the driving path search is facilitated.

According to some embodiments, in the case where the initial target node position is adjusted, there are a plurality of obstacles colliding with the vehicle when the vehicle is at the initial target node position before adjustment and none of the plurality of obstacles when the vehicle is at the initial target node position after adjustment when the initial target node position is adjusted at least once.

According to the embodiment of the disclosure, by avoiding all obstacles in the original initial target node position every time the initial target node position is adjusted, the efficiency of initial target node position adjustment can be improved, and the adjustment times can be reduced.

According to some embodiments, whether there is an obstacle colliding with the vehicle may be determined by a grid map having euclidean distance field properties.

In some conventional approaches, a common grid map is often used for obstacle screening. However, this approach is more complex in algorithm, often resulting in less efficient screening of obstacles. In the driving path search method of the embodiment of the present disclosure, obstacle screening and collision detection may be performed using, for example, an ESDF (Euclidean Signed Distance Field, euclidean symbol distance field) map.

In an example, multiple inscribed circle sampling points may be provided at the actual location occupied by the vehicle in space (otherwise referred to as the body coverage area) so that these inscribed circles can cover just the entire body coverage area. If an obstacle is detected to exist in any inscribed circle, then it can be assumed that an obstacle collides with the vehicle.

In an example, a grid corresponding to the center of each inscribed circle may be queried in the ESDF map and a nearest obstacle to the grid may be obtained, if the distance of the obstacle to the grid is less than the radius of the inscribed circle corresponding to the grid, then the obstacle may be considered to collide with the vehicle, which may be referred to as the nearest obstacle.

In some embodiments, the detection result can be obtained and the detection can be stopped when the first nearest obstacle in the inscribed circle is detected, so that the time for collision detection can be saved, and the detection efficiency can be improved. The collision detection may be performed on a plurality of inscribed circles simultaneously in a parallel manner. In this case, a plurality of nearest obstacles may be obtained.

According to the embodiments of the present disclosure, by means of the ESDF map, the distance between the obstacle and the center of the inscribed circle can be obtained without adding much computation, so that it is possible to facilitate quick determination of whether there is a collision between the vehicle and the obstacle.

According to some embodiments, the target search step size may be greater than or equal to a predetermined step size threshold. The size of the step size threshold may be based on the resolution of the grid map.

In an example, each location may be represented as a grid in a grid map. The grid may have two different states, symbolizing an obstacle and no obstacle, respectively. The grids may have the same size, and adjacent grids may have the same separation distance therebetween, which may be positively correlated with the resolution of the grid map.

In an example, the separation distance between adjacent grids may be determined as a step threshold. When the moving distance of the vehicle between the target node position and the current position is smaller than the spacing distance between adjacent grids, the target node position and the current position are actually represented as the same grid on the grid map, and such target node position is meaningless. In this case, it can be considered that the target search step of the vehicle in the search direction at the current position is not successfully determined.

According to the embodiment of the disclosure, by setting the step threshold, the failure of searching the driving path caused by the fact that the searching path is stuck in place can be avoided.

Fig. 3 shows a schematic diagram of a step size determination process 300 according to an embodiment of the present disclosure, the process 300 being applicable to a scenario where the search direction is straight forward. Fig. 3 shows a current location 301 of the vehicle and an initial target node location 302 reached after an initial search step 350 is moved from the current location 301 in the search direction, as well as obstacles 304 and 305.

In an example, when the vehicle is at the current position 301, the actual position occupied by the vehicle in space may be represented as a rectangular position 310, and the distance from the current position 301 to the forefront of the vehicle may be represented as L. Similarly, when the vehicle is at the initial target node position 302, the actual position occupied by the vehicle in space may be represented as a rectangular position 320. The point representing the position of the vehicle may be a midpoint between the rear wheels of the vehicle. A vehicle coordinate system may be established with the midpoint between the rear wheels as the origin, and the coordinate system may have the longitudinal direction of the vehicle as the transverse axis and the width direction as the longitudinal axis.

In an example, referring to fig. 3, both obstacles 304 and 305 collide with the vehicle when the vehicle is at the initial target node position 302. Can use x ₁ To represent the projection of the distance between the obstacle 304 and the current position 301 on the lateral axis of the vehicle coordinate system, using x ₂ Representing a projection of the distance between the obstacle 305 and the current position 301 on the lateral axis of the vehicle coordinate system. By comparing x ₁ And x ₂ Is known to be closer to the vehicle than the obstacle 304.

In order to avoid the obstacles 304 and 305, it is necessary to shorten the search step. When the vehicle avoids a closer obstacle 304, it is inevitable to avoid a further obstacle 305, and therefore a rectangular position 330 just not colliding with the obstacle 304 can be acquired, and a position 303 corresponding to the rectangular position 330 can be obtained. The distance 340 between the position 303 and the current position 301, i.e., the target search step size, may be expressed as (x ₁ -L)。

It will be appreciated that fig. 3 shows only one example of straight ahead. In an actual application scenario, the method may also be similarly performed straight backward.

It will also be appreciated that fig. 3 shows only one example where two obstacles are present. In the case where there are more (e.g., n) obstacles, the target search step size may be expressed as min { x } ₁ -L,x ₂ -L,…,x _n -L }, wherein x _n Represents the nthProjection of the distance between the obstacle and the current position on the lateral axis of the vehicle coordinate system.

Fig. 4 shows a schematic diagram of a step size determination process 400 according to another embodiment of the present disclosure, the process 400 may be applied to a scenario where driving is right ahead. Fig. 4 shows the current position 401 of the vehicle and the radius R, P from the current position 401 along the search direction _r As the center of a circle, turn right by theta ₁ The initial target node position 402 that arrives later, at which time the initial step size may be expressed as (θ ₁ * R). Fig. 4 also shows an obstacle 404.

In an example, when the vehicle is at the current position 401, the actual position occupied by the vehicle in space may be represented as a rectangular position 410, the distance from the current position 401 to the forefront of the vehicle may be represented as L, and the width of the vehicle may be represented as W. The actual position occupied by the vehicle in space when the vehicle is at the initial target node position 402 may be represented as a rectangular position 420. The point representing the position of the vehicle may be a midpoint between the rear wheels of the vehicle. The vehicle coordinate system may be established with the midpoint between the rear wheels as the origin, and the coordinate system may be established with the longitudinal direction of the vehicle as the X-axis and the width direction as the Y-axis.

In an example, referring to fig. 4, an obstacle 404 collides with the vehicle while the vehicle is at an initial target node location 402. At this time, the coordinates of the obstacle 404 can be obtained, and the position of the obstacle 404 in the coordinate system can be expressed as P ₁ 。

In order to avoid the obstacle 404, the search step needs to be shortened. For this reason, a rectangular position 430 that does not collide with the obstacle 404 exactly can be acquired, and a position 403 corresponding to the rectangular position 430 can be obtained.

Can pass through firstCalculating the center P of a vehicle turn _r Distance D to the right side of the front end of the vehicle (i.e., upper right corner), and passes d= |p ₁ -P _r Computing obstacle 404 to center P _r Is a distance d of (a). In the example shown in FIG. 4, due to d<D, it can be determined that the obstacle 404 is in position 403 with the vehicleRight side of (c) just do not collide. It will be appreciated that at some d>In the embodiment of D, it may be determined that the obstacle does not collide with the front end of the vehicle exactly when the vehicle is at the target node position. Hereinafter referred to as d<D is described as an example.

Position 403 may be a right turn θ of the vehicle along the search direction with R as radius from current position 401 ₂ And then to the location. In calculating theta ₂ When the vehicle is stationary, the obstacle 404 is rotated in the opposite direction to the positionSo that the rotated obstacle does not collide with the right side of the vehicle exactly. Position->Can be expressed as (-0.5W),). Based on the coordinates and position of obstacle 404 +.>Can be represented by the formula:

Calculating θ ₂ Is of a size of (a) and (b). The moving distance of the vehicle from the current position 401 to the position 403 may be expressed as (θ ₂ * R) can then be used to convert (θ ₂ * R) is determined as the target search step.

In some embodiments, the location of the vehicle just without collision with it and the curve angle corresponding to that location may be calculated in the same way for other obstacles. By comparing the magnitudes of the turning angles corresponding to the plurality of obstacles, the obstacle corresponding to the smallest turning angle can be determined as the nearest obstacle, thereby obtaining the target search step.

It is to be understood that fig. 4 shows only one example of traveling in the front right direction, and that the nearest obstacle does not collide with the right side of the vehicle. In an actual application scenario, the method may be similarly performed when traveling to the right, left, and may be similarly performed when the obstacle is located in other orientations of the vehicle.

It will also be appreciated that fig. 4 shows only one example of the presence of one obstacle. In the case where there are more (e.g., n) obstacles, the curve angle θ corresponding to the target search step may be expressed as:

wherein P is _n Indicating the position of the nth obstacle,indicating the position where the nth obstacle rotates in the reverse direction to be just out of collision with the vehicle.

Fig. 5 shows a flowchart of a step size determination process 500 according to another embodiment of the present disclosure. The process 500 may be applied to the determination of the step size of the vehicle in the motion primitive for each node location.

First, in step S501, an initial target node position may be obtained based on the current position and an initial search step. Step S501 may, for example, incorporate S210 as shown in fig. 2.

Step S502 may then be performed to determine whether there is a collision of the vehicle at the initial target node location. Step S502 may be combined with S220 as shown in fig. 2, for example. Step S502 may be performed using an ESDF map. If it is determined in step S502 that there is no collision of the vehicle at the initial target node position, the process may jump to step S508 to indicate that the step size determination is successful, or may be understood as that the motion primitive expansion is successful. The step size determined at this time is the initial search step size.

If it is determined in step S502 that there is a collision of the vehicle at the initial target node position, step S503 may be performed first to determine whether the number of adjustment steps is overrun. For example, the maximum number of adjustment steps may be set to three. If the number of steps is adjusted beyond the preset limit, the process may jump to step S507 to indicate step failure. This means that the current search direction is not feasible and the path expansion of the current location can be performed by adjusting the search direction and re-executing the process 500.

If the number of times the step is adjusted has not exceeded the preset limit, step S504 may be performed to calculate a new step based on the most recent obstacle position. By means of the ESDF map, a grid can be obtained where one or more nearest obstacles collide with the vehicle. Based on the positions of these nearest obstacles, keeping the direction of motion and steering wheel angle constant in the primitive of motion, a new step size can be calculated that leaves the vehicle just clear of these nearest obstacles.

After determining the new step size, step S505 may be performed to determine whether the new step size is smaller than a step size threshold. If the new step size is smaller than the step size threshold, the process may also jump to step S507 to indicate that the step size fails, and then the search direction may be adjusted and the process 500 may be re-executed to perform path expansion for the current location.

If the new step size is greater than or equal to the step size threshold, step S506 may be performed to determine the target node location based on the current location and the resulting step size.

After determining the target node position, the method may jump to step S502 to repeatedly perform the above steps until the step size is successfully determined in step S508, or a result of failure in step S507 is obtained, so as to adjust the search direction and re-perform the method 500 to perform path expansion of the current position.

According to some embodiments, the search cost for the node location may be based on a fusion of a path cost for the vehicle from a start point of the driving path to the node location and a heuristic cost for the vehicle from the node location to an end point of the driving path.

According to the embodiment of the disclosure, the node position with the minimum searching cost can be selected to perform the driving path searching preferentially by performing the searching cost calculation on the node position, so that the efficiency of the driving path searching can be improved.

According to another aspect of the present disclosure, there is also provided a driving path search apparatus.

Fig. 6 shows a block diagram of a driving path search apparatus 600 according to an embodiment of the present disclosure.

As shown in fig. 6, the driving route search device 600 includes: an initial target node position determination module 610 configured to determine an initial target node position based on a current position of the vehicle and an initial search step, wherein the initial target node position is a node position with a minimum search cost among the plurality of candidate node positions; a collision determination module 620 configured to determine whether an obstacle collides with the vehicle when the vehicle is at the initial target node position; a target search step determination module 630 configured to shorten an initial search step to obtain a target search step in response to the vehicle being in collision with the vehicle with an obstacle present at an initial target node position, such that the vehicle is not in collision with the vehicle at a first position reached after moving the target search step from the current position; and a first target node position determination module 640 configured to determine the first position as a target node position of the vehicle in response to the presence of an obstacle colliding with the vehicle while the vehicle is at the initial target node position.

Since the initial target node position determination module 610, the collision judgment module 620, the target search step size determination module 630, and the first target node position determination module 640 in the driving path search apparatus 600 may correspond to steps S210 to S240 as in fig. 2, respectively, details of various aspects thereof will not be described here.

In addition, the driving path search device 600 and the modules included therein may also include further sub-modules, which will be described in detail below in connection with fig. 7.

According to the method and the device for searching the driving path, through judging the collision condition of the vehicle with the obstacle when the vehicle is at the initial target node position, the other position which is not collided with the obstacle and the non-collision searching step corresponding to the other position can be determined in a self-adaptive mode under the condition that the initial searching step cannot be directly used as the target searching step, and therefore the efficiency and the success rate of searching the driving path can be improved.

Fig. 7 shows a block diagram of a driving path search device 700 according to another embodiment of the present disclosure.

As shown in fig. 7, the driving path search apparatus 700 may include an initial target node position determination module 710, a collision judgment module 720, a target search step determination module 730, and a first target node position determination module 740. The initial target node position determining module 710, the collision judging module 720, the target search step size determining module 730, and the first target node position determining module 740 may correspond to the initial target node position determining module 610, the collision judging module 620, the target search step size determining module 630, and the first target node position determining module 640 shown in fig. 6, and thus the details thereof will not be repeated here.

In an example, the driving path search apparatus 700 may further include: the second target node position determination module 750 is configured to determine the initial target node position as a target node position of the vehicle in response to the vehicle being in the initial target node position without an obstacle colliding with the vehicle.

In an example, the target search step determination module 730 may include: a position determination module 731 configured to adjust the target node position towards the current position to obtain a first position until there is no obstacle to collide with the vehicle when the vehicle is at the first position; and a step determination module 732 configured to determine a distance traveled by the vehicle between the first location and the current location as a target search step for the vehicle.

In an example, the number of times the target node position is adjusted may be less than or equal to a predetermined number of times threshold.

In an example, whether there is an obstacle colliding with the vehicle may be determined by a grid map having euclidean distance field properties.

In an example, the target search step may be greater than or equal to a predetermined step threshold, which may be sized based on the resolution of the grid map.

In an example, the search cost for the node location may be based on a fusion of a path cost for the vehicle from a start point of the driving path to the node location and a heuristic cost for the vehicle from the node location to an end point of the driving path.

According to embodiments of the present disclosure, there is also provided an electronic device, a readable storage medium, a computer program product and an autonomous vehicle.

Referring to fig. 8, a block diagram of an electronic device 800 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

Various components in electronic device 800 are connected to I/O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the electronic device 800, the input unit 806 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and/or a remote control. The output unit 807 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. The storage unit 808 may include, but is not limited to, magnetic disks, optical disks. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices over computer networks, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 801 performs the respective methods and processes described above, such as a driving path search method. For example, in some embodiments, the driving path search method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 800 via the ROM 802 and/or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the driving path search method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the driving path search method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.

Claims

1. A driving path search method, comprising:

determining an initial target node position based on a current position of the vehicle and an initial search step, wherein the initial target node position is a node position with the minimum search cost in a plurality of candidate node positions;

Determining whether an obstacle collides with the vehicle when the vehicle is at the initial target node position;

in response to a vehicle collision with the vehicle when the vehicle is at the initial target node position:

shortening an initial search step to obtain a target search step, so that no obstacle collides with the vehicle when the vehicle is at a first position reached after the target search step is moved from the current position; and

the first location is determined as a target node location of the vehicle.

2. The method of claim 1, further comprising:

in response to a vehicle collision with the vehicle when there is no obstacle at the initial target node position, the initial target node position is determined as a target node position of the vehicle.

3. The method of claim 1 or 2, wherein the shortening the initial search step to a target search step such that no obstacle collides with the vehicle when the vehicle is in a first position reached after moving the target search step from the current position, comprises:

adjusting the initial target node position towards the current position to obtain the first position until no obstacle collides with the vehicle when the vehicle is at the first position; and

A distance traveled by the vehicle between the first location and the current location is determined as the target search step of the vehicle.

4. A method according to claim 3, wherein the number of times the initial target node position is adjusted is less than or equal to a predetermined number of times threshold.

5. The method according to any one of claims 1 to 4, wherein whether there is an obstacle colliding with the vehicle is determined by a grid map having euclidean distance field properties.

6. The method of claim 5, wherein the target search step size is greater than or equal to a predetermined step size threshold, wherein the step size threshold is based on a resolution of the grid map.

7. The method of any of claims 1-6, wherein the search cost for the node location is based on a fusion of a path cost for the vehicle from a start of the driving path to the node location and a heuristic cost for the vehicle from the node location to an end of the driving path.

8. A driving route search device, comprising:

an initial target node position determining module configured to determine an initial target node position based on a current position of the vehicle and an initial search step, wherein the initial target node position is a node position with a minimum search cost among a plurality of candidate node positions;

A collision determination module configured to determine whether an obstacle collides with a vehicle when the vehicle is at the initial target node position;

a target search step determination module configured to shorten an initial search step to obtain a target search step in response to a collision of an obstacle with a vehicle when the vehicle is at the initial target node position, such that the vehicle is not in collision with the vehicle when the vehicle is at a first position reached after moving the target search step from the current position; and

a first target node position determination module configured to determine a first position as a target node position of a vehicle in response to the vehicle being in collision with the vehicle with an obstacle at the initial target node position.

9. The apparatus of claim 8, further comprising:

a second target node position determination module configured to determine the initial target node position as a target node position of the vehicle in response to a vehicle being at the initial target node position without an obstacle colliding with the vehicle.

10. The apparatus of claim 8 or 9, wherein the target search step determination module comprises:

A position determination module configured to adjust the initial target node position towards the current position to obtain the first position until there is no obstacle colliding with the vehicle when the vehicle is in the first position; and

a step size determination module configured to determine a distance traveled by the vehicle between the first location and the current location as the target search step size of the vehicle.

11. The apparatus of claim 10, wherein the number of times the target node location is adjusted is less than or equal to a predetermined number of times threshold.

12. The apparatus according to any one of claims 8 to 11, wherein whether there is an obstacle colliding with the vehicle is determined by a grid map having euclidean distance field properties.

13. The apparatus of claim 12, wherein the target search step size is greater than or equal to a predetermined step size threshold, wherein the step size threshold is based on a resolution of the grid map.

14. The apparatus of any of claims 8-13, wherein the search cost for the node location is based on a fusion of a path cost for the vehicle from a start of the driving path to the node location and a heuristic cost for the vehicle from the node location to an end of the driving path.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the method comprises the steps of

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.

17. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to any of claims 1-7.

18. An autonomous vehicle comprising a controller, wherein the controller is configured to perform the method of any of claims 1-7.