CN114355874A

CN114355874A - Path planning method and device, electronic equipment and automatic driving equipment

Info

Publication number: CN114355874A
Application number: CN202111334754.7A
Authority: CN
Inventors: 梁兴亚; 刘阳; 耿涛; 彭亮
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-11-11
Filing date: 2021-11-11
Publication date: 2022-04-15
Anticipated expiration: 2041-11-11
Also published as: CN114355874B

Abstract

The disclosure provides a path planning method and device, electronic equipment and automatic driving equipment, relates to the technical field of computers, and particularly relates to the technical fields of artificial intelligence, such as the field of intelligent transportation, the technical field of automatic driving and the like. The specific implementation scheme is as follows: acquiring a query endpoint of automatic driving equipment, wherein the query endpoint comprises a starting place and a destination; determining a matching path corresponding to the query endpoint according to a reference automatic driving path corresponding to a preset endpoint; and controlling the automatic driving equipment to automatically drive according to the matching path.

Description

Path planning method and device, electronic equipment and automatic driving equipment

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of intelligent transportation and the field of automated driving technologies, and in particular, to a method and an apparatus for path planning, an electronic device, and an automatic driving device.

Background

Path planning techniques have wide application in many fields, such as intelligent travel, automatic driving, and autonomous collision-free action of robots. For automatic driving equipment, such as an automatic driving vehicle, an intelligent robot and the like, reasonable path planning can obviously improve the success rate of automatic driving.

At present, most of path planning of automatic traveling equipment adopts algorithms for finding a shortest path and optimizing a path, such as an a star (a star) algorithm, a Contract Hierarchy (CH) algorithm, and an ant colony algorithm.

Disclosure of Invention

The disclosure provides a path planning method and device, electronic equipment and automatic driving equipment.

According to an aspect of the present disclosure, there is provided a path planning method, including:

acquiring a query endpoint of automatic driving equipment, wherein the query endpoint comprises a starting place and a destination;

determining a matching path corresponding to the query endpoint according to a reference automatic driving path corresponding to a preset endpoint;

and controlling the automatic driving equipment to automatically drive according to the matching path.

According to another aspect of the present disclosure, there is provided a path planning apparatus including:

an acquisition unit configured to acquire an inquiry endpoint of an automatic travel device, the inquiry endpoint including a departure place and a destination;

the determining unit is used for determining a matching path corresponding to the query endpoint according to a reference automatic driving path corresponding to a preset endpoint;

and the control unit is used for controlling the automatic running equipment to automatically run according to the matching path.

According to still another aspect of the present disclosure, there is provided an electronic device including:

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 cause the at least one processor to perform the method of the aspects and any possible implementation described above.

According to yet another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the above-described aspect and any possible implementation.

According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of the aspect and any possible implementation as described above.

According to still another aspect of the present disclosure, there is provided an automatic traveling apparatus including the electronic apparatus as described above.

According to the technical scheme, the query end points including the departure place and the destination of the automatic driving equipment are obtained, the matching paths corresponding to the query end points are determined according to the reference automatic driving paths corresponding to the preset end points, the automatic driving equipment can be controlled to automatically drive according to the matching paths, and the reference automatic driving paths corresponding to the preset end points are used as planning basis of the driving paths, so that the driving paths of the automatic driving equipment can be reasonably planned, and the driving effectiveness and reliability of the automatic driving equipment are improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;

FIG. 2 is a schematic view of a reference automated travel path according to a first embodiment of the present disclosure;

fig. 3 is a schematic view of an automatic driving state map according to a first embodiment of the present disclosure;

fig. 4 is a schematic diagram of an application scenario according to a second embodiment of the present disclosure;

fig. 5 is a schematic diagram of a process of offline path planning according to a second embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a process of online path planning according to a second embodiment of the present disclosure;

FIG. 7 is a schematic diagram according to a third embodiment of the present disclosure;

fig. 8 is a block diagram of an electronic device for implementing a path planning method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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 and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It is to be understood that the described embodiments are only a few, and not all, of the disclosed embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It should be noted that the terminal device involved in the embodiments of the present disclosure may include, but is not limited to, a mobile phone, a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), and other intelligent devices; the display device may include, but is not limited to, a personal computer, a television, and the like having a display function.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

With the rapid development of scientific technology, path planning technology has wide application in many fields, such as intelligent travel, automatic driving and robot autonomous collision-free action. In general, path planning refers to finding an optimal path that satisfies the condition that the automatic traveling equipment does not intersect any obstacle from the starting point to the ending point in a given environment. For automatic driving equipment such as automatic driving vehicles and intelligent robots, reasonable path planning can obviously improve the success rate of automatic driving.

At present, most of path planning of automatic driving equipment adopts algorithms for finding a shortest path and optimizing a path, such as an a star algorithm, a CH algorithm, an ant colony algorithm, and the like. However, the current path planning algorithm for the automatic driving equipment only considers the factors such as the shortest path, and the like, and the effectiveness and reliability of the driving of the automatic driving equipment cannot be better guaranteed based on the planned path obtained by the existing path planning algorithm.

Therefore, it is desirable to provide a path planning method, which can plan the driving path of the automatic driving device more reasonably to ensure the effectiveness and reliability of the driving of the automatic driving device.

Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure, as shown in fig. 1.

101. And acquiring a query endpoint of the automatic driving equipment, wherein the query endpoint comprises a starting place and a destination.

102. And determining a matching path corresponding to the query endpoint according to the reference automatic driving path corresponding to the preset endpoint.

103. And controlling the automatic driving equipment to automatically drive according to the matching path.

It should be noted that part or all of the execution subjects of 101 to 103 may be an application located at the local terminal, or may also be a functional unit such as a plug-in or Software Development Kit (SDK) set in the application located at the local terminal, or may also be a processing engine located in a server on the network side, or may also be a distributed system located on the network side, for example, a processing engine or a distributed system in an automatic driving processing platform on the network side, which is not particularly limited in this embodiment.

It is to be understood that the application may be a native application (native app) installed on the local terminal, or may also be a web page program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

Therefore, by acquiring the query end point of the automatic driving equipment, including the departure place and the destination, and further determining the matching path corresponding to the query end point according to the reference automatic driving path corresponding to the preset end point, the automatic driving equipment can be controlled to automatically drive according to the matching path, and the reference automatic driving path corresponding to the preset end point is adopted as the planning basis of the driving path, so that the driving path of the automatic driving equipment can be reasonably planned, and the driving effectiveness and reliability of the automatic driving equipment are improved.

Optionally, in one possible implementation manner of the embodiment, the preset endpoint may be a place where a stop position of the automatic traveling device may be provided. The preset endpoints may be locations that are set in pairs, for example, the preset endpoints may include a start location and an end location. The inquiry endpoint of the automatic travel device acquired by executing 101 may also be a place where the stop position of the automatic travel device can be provided.

In this implementation, the query endpoints may be two endpoints of the preset endpoints; or, the query endpoint may be any two endpoints on the reference automatic travel path corresponding to the preset endpoint, except for the preset endpoint; or one of the query end points is on the reference automatic driving path corresponding to the preset end point, and the other end point is not on the reference automatic driving path corresponding to the preset end point; or, both the two end points of the query end point may not be on the reference automatic travel path corresponding to the preset end point. The present embodiment does not particularly limit the relationship between the query endpoint and the preset endpoint.

Optionally, in a possible implementation manner of this embodiment, before executing 102, automatic driving state data of a historical automatic driving path may be further obtained, where the automatic driving state data of the historical automatic driving path includes path data of the historical automatic driving path and state data that can be automatically driven on the historical automatic driving path, and further, a historical automatic driving path corresponding to the preset endpoint may be determined according to the path data of the historical automatic driving path. Then, at least one historical automatic travel path may be selected from the historical automatic travel paths corresponding to the preset end points according to the state data of automatic travel possible on the historical automatic travel paths corresponding to the preset end points, so as to serve as a reference automatic travel path corresponding to the preset end points, as shown in fig. 2.

In fig. 2, the number of the reference automatic travel paths corresponding to the preset end points may be one, two, or more.

In this implementation, the automatic travel state data of the historical automatic travel path may be represented as an automatic travel state map of the historical automatic travel path, as shown in fig. 3.

Specifically, the automatic driving state map can be simplified into a topological map, so that the automatic driving state map can be well applied to the existing efficient searching and reasoning algorithm, and further can be beneficial to further path planning. In addition, the storage space and the search space of the automatic driving state map are both smaller, and the calculation processing efficiency of the path planning can be improved.

In a specific implementation process, before the automatic driving state data of the historical automatic driving path is acquired, the driving data of the preset area can be further acquired by using the test automatic driving equipment, and then the path data of the historical automatic driving path is acquired according to the driving data of the preset area acquired by the test automatic driving equipment.

In another specific implementation process, before the automatic driving state data of the historical automatic driving path is acquired, the state data of automatic driving on the historical automatic driving path can be further acquired according to the taken-over condition of the automatic driving equipment on the historical automatic driving path.

The taken over condition of the automatic traveling equipment on the historical automatic traveling path may be a condition that any automatic traveling equipment traveling on the historical automatic traveling path is taken over by an operator to perform manual control traveling.

In this specific implementation process, the taken over condition of the automatic traveling apparatus on the historical automatic traveling path may be the number of times of the taken over of the automatic traveling apparatus on the historical automatic traveling path.

For example, with respect to the historical automatic travel route a, if a situation occurs in which any one of the automatic travel apparatuses traveling on the historical automatic travel route a is taken over by the operator for manual control travel 1 time, the number of times of taking over of the automatic travel apparatus on the historical automatic travel route a may be counted as 1 time.

In this concrete implementation process, the state data that can be automatically traveled on the history automatic travel path may be obtained based on the number of times of takeover of the automatic travel apparatus on the history automatic travel path and the number of times of travel of the automatic travel apparatus on the history automatic travel path. The automatically travelable state data on the historical automatic travel path may represent the historical travel success rate on the historical automatic travel path, such as the data shown in fig. 3.

In fig. 3, for example, the number of times of takeover of the automatic traveling apparatus on a history automatic traveling path is 16 times, the number of times of traveling of the automatic traveling apparatus on the history automatic traveling path is 100 times, and the automatically travelable state data on the history automatic traveling path may be 0.16.

In this way, the state data of automatic driving on the historical automatic driving path, namely the historical driving success rate on the historical automatic driving path, is obtained according to the taken-over condition of the automatic driving equipment on the historical automatic driving path, so as to determine the reference automatic driving path corresponding to the preset endpoint. Therefore, in the process of determining the reference automatic driving path corresponding to the preset endpoint, the factors such as the historical driving success rate on the historical automatic driving path are fully considered, and the reliability of the reference automatic driving path corresponding to the determined preset endpoint can be effectively improved.

In another specific implementation process, the historical automatic travel path corresponding to the preset endpoint is determined according to the path data of the historical automatic travel path, specifically, the preset endpoint may be obtained first, and then, a preset path planning algorithm is used to perform path planning according to the preset endpoint and the path data of the historical automatic travel path, so as to obtain the shortest historical automatic travel path. After the shortest historical automatic travel path is obtained, a preset search algorithm can be used for searching the historical automatic travel path meeting the preset path length condition according to the length of the shortest historical automatic travel path and the preset path length condition, and then the searched historical automatic travel path is used as the historical automatic travel path corresponding to the preset endpoint.

Specifically, the preset path length condition may be that the length by which the path length of the historical automatic travel path exceeds the path length of the shortest historical automatic travel path is less than a predetermined length threshold, for example, the length by which the path length of the historical automatic travel path exceeds the path length of the shortest historical automatic travel path is less than 20% of the path length of the shortest historical automatic travel path.

In another specific implementation process, the historical automatic travel path corresponding to the preset endpoint may refer to all historical automatic travel paths between a pair of start points and end points, and the preset endpoint may specifically correspond to one, two, or more historical automatic travel paths. The historical automatic driving paths corresponding to the preset end points can form one, two or more optional paths between the preset end points.

Specifically, all selectable paths between the preset end points may be determined according to each historical automatic travel path corresponding to the preset end points. Then, the automatically travelable state data on each of the alternative routes may be determined based on the automatically travelable state data on each of the historical automatic travel routes in each of the alternative routes.

For example, for the history automatic travel path B1, the history automatic travel path B2, and the history automatic travel path B3 to which preset end points correspond, the history automatic travel path B1 and the history automatic travel path B2 may constitute the alternative path B, and the history automatic travel path B3 may constitute the alternative path C.

For another example, the automatic traveling available state data on the history automatic traveling route B1 may be 0.28, the automatic traveling available state data on the history automatic traveling route B2 may be 0.48, and the automatic traveling available state data on the alternative route B may be a result of calculation in which 0.28 and 0.48 are respectively weighted and multiplied.

Further, after the status data that can be automatically traveled on each selectable path is determined, a reference automatic travel path corresponding to the preset endpoint may be determined according to a preset status data threshold and the status data that can be automatically traveled on each selectable path.

Specifically, if the state data of the automatic traveling on any optional path reaches the preset state data threshold, the optional path may be used as a reference automatic traveling path corresponding to the preset endpoint.

Here, the preset state data threshold may be an average value of the automatically travelable state data on the historical automatic travel path.

In another specific implementation process, further, the reference automatic driving paths corresponding to the preset end points may be sorted according to the state data of automatic driving on the reference automatic driving paths corresponding to the preset end points, and the sorted reference automatic driving paths corresponding to the preset end points are stored.

In this way, the reference automatic travel path corresponding to the preset end point is configured in advance through the acquired automatic travel state data of the historical automatic travel path and stored. Therefore, when the query endpoint is obtained, the reference automatic driving path corresponding to the preset endpoint which is configured and stored in advance can be used for being directly matched with the matching path corresponding to the query endpoint, namely the automatic driving path corresponding to the query endpoint, so that the complex path planning algorithm processing process is reduced, the path planning request time delay can be effectively reduced, and the processing efficiency of path planning is improved.

Optionally, in a possible implementation manner of this embodiment, in 102, at least one reference automatic travel path corresponding to the query endpoint may be specifically obtained according to a reference automatic travel path corresponding to a preset endpoint, and further, road condition data of the at least one reference automatic travel path corresponding to the query endpoint is obtained. Then, the matching path may be determined from at least one reference automatic travel path corresponding to the query endpoint according to the road condition data.

In this implementation manner, the road condition data of at least one reference automatic driving path corresponding to the query endpoint can be obtained according to the real-time road condition map. The road condition data may include, but is not limited to, congestion conditions, road infrastructure conditions, traffic control conditions. This embodiment is not particularly limited to this.

According to the implementation mode, at least one reference automatic driving path corresponding to the query endpoint can be screened according to the acquired road condition data, the reference automatic driving path corresponding to the query endpoint with good road condition is obtained and used as a matching path, the reliability of the planned path can be further improved, and the effectiveness and the reliability of the driving of the automatic driving equipment are further improved.

It is understood that after the reference automatic traveling path corresponding to the preset endpoint is determined according to the foregoing implementation, the matching path may be determined in combination with the various specific implementation processes of 102 provided in the foregoing implementation. For a detailed description, reference may be made to the related contents in the foregoing implementation manners, and details are not described herein.

Optionally, in a possible implementation manner of this embodiment, if a matching path corresponding to the query endpoint is not determined in 102, the automatic driving state data of the historical automatic driving path may be obtained, where the automatic driving state data of the historical automatic driving path includes path data of the historical automatic driving path and state data that can be automatically driven on the historical automatic driving path. And then, according to the path data of the historical automatic driving paths, determining the historical automatic driving paths corresponding to the inquiry end points, and according to the state data capable of automatically driving on the historical automatic driving paths corresponding to the inquiry end points, selecting at least one historical automatic driving path from the historical automatic driving paths corresponding to the inquiry end points to serve as a matching path corresponding to the inquiry end points.

In a specific implementation process, the condition that the matching path corresponding to the query endpoint is not determined may include: and in the reference automatic driving path corresponding to the preset end point, a matching path corresponding to the query end point is not determined.

For example, a matching path corresponding to the query endpoint does not exist in the reference automatic travel path corresponding to the preset endpoint.

In another specific implementation process, the condition that the matching path corresponding to the query endpoint is not determined may further include: and according to the acquired road condition data, determining a matched path from at least one reference automatic driving path corresponding to the query end point.

For example, at least one reference automatic driving path corresponding to the query endpoint is screened according to the acquired road condition data, and the screening result indicates that none of the reference automatic driving paths corresponding to the query endpoint can normally drive, that is, a matching path corresponding to the query endpoint is not determined.

In this way, under the condition that the matching path corresponding to the query endpoint is not determined, the historical automatic driving path corresponding to the query endpoint is determined through the acquired automatic driving state data of the historical automatic driving path, and then at least one historical automatic driving path can be directly selected from the determined historical automatic driving paths corresponding to the query endpoint to serve as the matching path corresponding to the query endpoint, so that the reliability of path planning is further guaranteed.

In another specific implementation process, if the query endpoint may be a location that can provide a stop position of the automatic travel device, the query endpoint may be further updated to the preset endpoint, and at the same time, at least one historical automatic travel path corresponding to the selected query endpoint may be updated to a reference automatic travel path corresponding to the preset endpoint.

Therefore, the preset end points and the reference automatic driving paths corresponding to the preset end points can be updated, the preset path resources are enriched, the updated reference automatic driving paths corresponding to the preset end points can be conveniently and directly utilized in subsequent path planning, and the processing efficiency of path planning is further improved.

Optionally, in a possible implementation manner of this embodiment, if the matching path corresponding to the query endpoint is not determined in 102, in addition to determining the matching path corresponding to the query endpoint by using the obtained automatic driving state data of the historical automatic driving path, an existing path planning algorithm, for example, an a star algorithm, may be used to perform path planning according to the obtained query endpoint and a preset map, so as to determine a shortest path corresponding to the query endpoint.

In this implementation, if the query endpoint may be a location that can provide a stop position of the automatic travel device, after the shortest path corresponding to the query endpoint is determined by using an existing path planning algorithm, the query endpoint may also be updated to a preset endpoint, and at the same time, the determined shortest path corresponding to the query endpoint may be updated to a reference automatic travel path corresponding to the preset endpoint.

It should be noted that, if the matching path corresponding to the query endpoint is not determined in 102, the matching path corresponding to the query endpoint may be determined according to any of the foregoing implementation manners by combining with the technical solution provided in any of the foregoing implementation manners. For a detailed description, reference may be made to the related contents in the foregoing implementation manners, and details are not described herein.

In the embodiment, the inquiry end point including the departure place and the destination of the automatic driving equipment is obtained, and then the matching path corresponding to the inquiry end point is determined according to the reference automatic driving path corresponding to the preset end point, so that the automatic driving equipment can be controlled to automatically drive according to the matching path.

In addition, by adopting the technical scheme provided by the embodiment, the reference automatic travel path corresponding to the preset endpoint can be configured in advance and stored according to the acquired automatic travel state data of the historical automatic travel path. Therefore, when the query endpoint is obtained, the reference automatic driving path corresponding to the preset endpoint which is configured and stored in advance can be used for being directly matched with the matching path corresponding to the query endpoint, namely the automatic driving path corresponding to the query endpoint, so that the complex path planning algorithm processing process is reduced, the path planning request time delay can be effectively reduced, and the processing efficiency of path planning is improved.

In addition, by adopting the technical scheme provided by the embodiment, the state data of automatic traveling on the historical automatic traveling path, namely the historical traveling success rate on the historical automatic traveling path, can be obtained according to the taken over condition of the automatic traveling equipment on the historical automatic traveling path, so as to determine the reference automatic traveling path corresponding to the preset endpoint. Therefore, in the process of determining the reference automatic driving path corresponding to the preset endpoint, the factors such as the historical driving success rate on the historical automatic driving path are fully considered, and the reliability of the reference automatic driving path corresponding to the determined preset endpoint can be effectively improved.

In addition, by adopting the technical scheme provided by this embodiment, at least one reference automatic traveling path corresponding to the query endpoint can be screened according to the acquired road condition data, and the reference automatic traveling path corresponding to the query endpoint with good road condition is acquired to be used as a matching path, so that the reliability of the planned path can be further improved, and the effectiveness and reliability of the traveling of the automatic traveling equipment can be further improved.

In addition, by adopting the technical scheme provided by the embodiment, under the condition that the matching path corresponding to the query endpoint is not determined, the historical automatic driving path corresponding to the query endpoint is determined through the acquired automatic driving state data of the historical automatic driving path, and then at least one historical automatic driving path can be directly selected from the determined historical automatic driving paths corresponding to the query endpoint to serve as the matching path corresponding to the query endpoint, so that the reliability of path planning is further ensured.

In addition, by adopting the technical scheme provided by the embodiment, the preset end point and the reference automatic driving path corresponding to the preset end point can be updated, the preset path resources are enriched, and the updated reference automatic driving path corresponding to the preset end point can be conveniently and directly utilized in subsequent path planning, so that the processing efficiency of path planning is improved.

In addition, by adopting the technical scheme provided by the embodiment, the time delay of the path planning request can be effectively reduced, and the user experience of the automatic driving equipment can be effectively improved.

Fig. 4 is a schematic view of an application scenario according to a second embodiment of the present disclosure, and as shown in fig. 4, in one possible implementation manner of this embodiment, a path planning method of the present disclosure is described in detail by taking an autonomous vehicle as an example, and the application scenario of the path planning method of the autonomous vehicle may include offline path planning and online path planning.

Fig. 5 is a schematic diagram of a process of offline path planning according to a second embodiment of the present disclosure, as shown in fig. 5.

501. Automatic travel state data of a history automatic travel path is acquired.

Specifically, the automatic travel state data of the history automatic travel path includes path data of the history automatic travel path of the autonomous vehicle and state data that is automatically travelable on the history automatic travel path.

In this implementation, the travel data of the predetermined area may be collected using the test autonomous vehicle, and then the path data of the historical autonomous travel path may be obtained from the travel data of the predetermined area collected by the test autonomous vehicle.

In this implementation, the state data that can be automatically traveled on the historical automatic travel path may be obtained specifically according to the taken over condition of the automatically driven vehicle on the historical automatic travel path. The automatically travelable state data on the historical automatic travel path may characterize the historical travel success rate on the historical automatic travel path.

502. And obtaining an automatic driving state map according to the automatic driving state data of the historical automatic driving path.

Specifically, the automatic travel state data of the history automatic travel path may be represented as an automatic travel state map of the history automatic travel path. The automatic travel state map may include route data of a historical automatic travel route and state data of automatic travel possible on the historical automatic travel route, that is, data representing a historical travel success rate on the historical automatic travel route, as shown in fig. 3.

503. And acquiring a preset endpoint.

In this implementation, the preset endpoint may be a location that may provide a stop location for the autonomous vehicle. The preset endpoints include a start location and an end location.

504. And determining a reference automatic driving path corresponding to the preset end point according to the automatic driving state map and the preset end point.

In the implementation mode, firstly, according to the automatic driving state map and the preset end point, a preset path planning algorithm is utilized to determine the shortest historical automatic driving path corresponding to the preset end point. And then, according to the length of the shortest historical automatic driving path and a preset path length condition, searching the historical automatic driving path meeting the preset path length condition by using a preset search algorithm, and taking the searched historical automatic driving path as the historical automatic driving path corresponding to the preset endpoint.

Then, at least one historical automatic travel path may be selected from the historical automatic travel paths corresponding to the preset end points according to the state data of automatic travel on the historical automatic travel paths corresponding to the preset end points, so as to serve as a reference automatic travel path corresponding to the preset end points.

Specifically, the preset end point may correspond to one, two, or more historical automatic travel paths. The historical automatic driving paths corresponding to the preset end points can form one, two or more optional paths between the preset end points.

Then, after the state data capable of automatically driving on each selectable path is determined, a reference automatic driving path corresponding to the preset endpoint may be determined according to the preset state data threshold and the state data capable of automatically driving on each selectable path.

505. And storing the reference automatic driving path corresponding to the preset end point.

In a specific implementation manner, the obtained reference automatic travel path corresponding to the preset endpoint may be stored as a database file, and the database file is uploaded to the cloud.

In the specific implementation process, the reference automatic driving paths corresponding to the preset end points are sorted according to the state data capable of automatically driving on the reference automatic driving paths corresponding to the preset end points, and then the sorted reference automatic driving paths corresponding to the preset end points are stored.

In the implementation manner of the offline path planning in this embodiment, a reference automatic travel path corresponding to a preset endpoint may be configured in advance through the acquired automatic travel state data of the historical automatic travel path, and stored. Therefore, when the query endpoint is obtained, the reference automatic driving path corresponding to the preset endpoint which is configured and stored in advance can be used for being directly matched with the matching path corresponding to the query endpoint, namely the automatic driving path corresponding to the query endpoint, so that the complex path planning algorithm processing process is reduced, the path planning request time delay can be effectively reduced, and the processing efficiency of path planning is improved.

According to the implementation mode, the state data capable of automatically driving on the historical automatic driving path, namely the historical driving success rate on the historical automatic driving path, can be obtained according to the taken-over condition of the automatic driving vehicle on the historical automatic driving path, so that the reference automatic driving path corresponding to the preset endpoint can be determined. Therefore, in the process of determining the reference automatic driving path corresponding to the preset endpoint, the factors such as the historical driving success rate on the historical automatic driving path are fully considered, and the reliability of the reference automatic driving path corresponding to the determined preset endpoint can be effectively improved.

Fig. 6 is a schematic diagram of a process of online path planning according to a second embodiment of the present disclosure, as shown in fig. 6.

601. Query endpoints for an autonomous vehicle are obtained.

In particular, the query endpoint may include a departure location and a destination. For example, the query endpoints may be a starting point and a destination in a navigation order request from a user for an autonomous vehicle.

602. And judging whether a matching path corresponding to the query endpoint can be determined or not according to the reference automatic driving path corresponding to the preset endpoint.

If so, execution continues 603, and if not, execution 606.

603. And obtaining at least one reference automatic driving path corresponding to the query endpoint according to the reference automatic driving path corresponding to the preset endpoint.

604. And acquiring road condition data of at least one reference automatic driving path corresponding to the query end point.

605. And judging whether a matching path corresponding to the query endpoint can be determined or not according to the road condition data and the reference automatic driving path corresponding to the preset endpoint.

If so, execution continues at 607, and if not, execution proceeds at 606.

Specifically, according to the road condition data, a matching path is determined from at least one reference automatic driving path corresponding to the query end point.

606. And if the matching path corresponding to the query endpoint is not determined, determining the shortest path corresponding to the query endpoint by using an A s tar algorithm.

607. The matching path or shortest path is sent to the autonomous vehicle.

Specifically, after the autonomous vehicle receives the fed back matching path or shortest path, the autonomous vehicle may automatically travel according to the matching path or shortest path.

In the implementation manner of the online path planning in this embodiment, a matching path corresponding to the query endpoint of the autonomous vehicle is obtained according to the reference autonomous driving path corresponding to the preset endpoint, so that the autonomous vehicle is controlled to travel according to the path, and the traveling path of the autonomous vehicle can be reasonably planned, thereby improving the effectiveness and reliability of the autonomous vehicle.

According to the implementation mode, at least one reference automatic driving path corresponding to the query endpoint can be screened according to the acquired road condition data, the reference automatic driving path corresponding to the query endpoint with good road condition is obtained and used as a matching path, the reliability of the planned path can be further improved, and the effectiveness and the reliability of the driving of the automatic driving vehicle are further improved.

According to the implementation mode, under the condition that the matching path corresponding to the query endpoint is not determined, path planning is performed according to the acquired query endpoint by using an A star algorithm so as to determine the shortest path corresponding to the query endpoint, and the reliability of path planning is further guaranteed.

It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required for the disclosure.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

Fig. 7 is a schematic diagram according to a third embodiment of the present disclosure, as shown in fig. 7. The path planning apparatus 700 of the present embodiment may include an obtaining unit 701, a determining unit 702, and a control unit 703. The obtaining unit 701 is configured to obtain an inquiry endpoint of the automatic traveling device, where the inquiry endpoint includes a departure place and a destination; a determining unit 702, configured to determine, according to a reference automatic traveling path corresponding to a preset endpoint, a matching path corresponding to the query endpoint; and a control unit 703, configured to control the automatic traveling device to perform automatic traveling according to the matching path.

It should be noted that, part or all of the path planning apparatus in this embodiment may be an application located at the local terminal, or may also be a functional unit such as a plug-in or Software Development Kit (SDK) set in the application located at the local terminal, or may also be a processing engine located in a server on the network side, or may also be a distributed system located on the network side, for example, a processing engine or a distributed system in an automatic driving processing platform on the network side, and the like, which is not particularly limited in this embodiment.

Optionally, in a possible implementation manner of this embodiment, the determining unit 702 is further configured to obtain automatic driving state data of a historical automatic driving path, where the automatic driving state data of the historical automatic driving path includes path data of the historical automatic driving path and state data that can be automatically driven on the historical automatic driving path; determining a historical automatic driving path corresponding to the preset end point according to the path data of the historical automatic driving path; and selecting at least one historical automatic driving path from the historical automatic driving paths corresponding to the preset end points according to the state data capable of automatically driving on the historical automatic driving paths corresponding to the preset end points, wherein the at least one historical automatic driving path is used as a reference automatic driving path corresponding to the preset end points.

Optionally, in a possible implementation manner of this embodiment, the determining unit 702 is specifically configured to obtain status data of automatic traveling on the historical automatic traveling path according to a taken-over situation of the automatic traveling device on the historical automatic traveling path.

Optionally, in a possible implementation manner of this embodiment, the determining unit 702 may be further configured to obtain at least one reference automatic traveling path corresponding to the query endpoint according to a reference automatic traveling path corresponding to a preset endpoint; acquiring road condition data of at least one reference automatic driving path corresponding to the query endpoint; and determining the matching path from at least one reference automatic driving path corresponding to the query endpoint according to the road condition data.

Optionally, in a possible implementation manner of this embodiment, the determining unit 702 may be specifically configured to, if a matching path corresponding to the query endpoint is not determined, obtain automatic driving state data of a historical automatic driving path, where the automatic driving state data of the historical automatic driving path includes path data of the historical automatic driving path and state data that can be automatically driven on the historical automatic driving path; determining a historical automatic driving path corresponding to the query endpoint according to the path data of the historical automatic driving path; and selecting at least one historical automatic driving path from the historical automatic driving paths corresponding to the inquiry end points according to the state data which can be automatically driven on the historical automatic driving paths corresponding to the inquiry end points to serve as the matching path corresponding to the inquiry end points.

Optionally, in a possible implementation manner of this embodiment, the determining unit 702 may be further configured to update the query endpoint to the preset endpoint; and updating at least one historical automatic driving path corresponding to the selected inquiry end point into a reference automatic driving path corresponding to the preset end point.

In this embodiment, an obtaining unit obtains an inquiry end point of an automatic traveling device, where the inquiry end point includes a departure place and a destination, and a determining unit determines a matching path corresponding to the inquiry end point according to a reference automatic traveling path corresponding to a preset end point, so that a control unit can control the automatic traveling device to automatically travel according to the matching path. Because the reference automatic driving path corresponding to the preset end point is used as the planning basis of the driving path, the driving path of the automatic driving equipment can be reasonably planned, and the driving effectiveness and reliability of the automatic driving equipment are improved.

By adopting the technical scheme provided by the embodiment, the reference automatic travel path corresponding to the preset endpoint can be configured in advance according to the acquired automatic travel state data of the historical automatic travel path and stored. Therefore, when the query endpoint is obtained, the reference automatic driving path corresponding to the preset endpoint which is configured and stored in advance can be used for being directly matched with the matching path corresponding to the query endpoint, namely the automatic driving path corresponding to the query endpoint, so that the complex path planning algorithm processing process is reduced, the path planning request time delay can be effectively reduced, and the processing efficiency of path planning is improved.

By adopting the technical scheme provided by the embodiment, the state data capable of automatically driving on the historical automatic driving path, namely the historical driving success rate on the historical automatic driving path, can be obtained according to the takeover condition of the automatic driving equipment on the historical automatic driving path, so as to determine the reference automatic driving path corresponding to the preset endpoint. Therefore, in the process of determining the reference automatic driving path corresponding to the preset endpoint, the factors such as the historical driving success rate on the historical automatic driving path are fully considered, and the reliability of the reference automatic driving path corresponding to the determined preset endpoint can be effectively improved.

By adopting the technical scheme provided by the embodiment, at least one reference automatic driving path corresponding to the query endpoint can be screened according to the acquired road condition data, and the reference automatic driving path corresponding to the query endpoint with good road condition is acquired and used as a matching path, so that the reliability of the planned path can be further improved, and the driving effectiveness and reliability of the automatic driving equipment can be further improved.

By adopting the technical scheme provided by the embodiment, under the condition that the matching path corresponding to the query endpoint is not determined, the historical automatic driving path corresponding to the query endpoint is determined through the acquired automatic driving state data of the historical automatic driving path, and then at least one historical automatic driving path can be directly selected from the determined historical automatic driving paths corresponding to the query endpoint to serve as the matching path corresponding to the query endpoint, so that the reliability of path planning is further ensured.

By adopting the technical scheme provided by the embodiment, the preset end point and the reference automatic driving path corresponding to the preset end point configured in advance can be updated, the path resources configured in advance are enriched, and the updated reference automatic driving path corresponding to the preset end point can be conveniently and directly utilized in subsequent path planning, so that the processing efficiency of path planning is improved.

According to the technical scheme, the acquisition of the personal information of the user in the driving process of the automatic driving equipment, such as travel requirement information, passenger navigation order information and the like, storage and application and the like, all accord with the regulations of relevant laws and regulations, and the customs of the public order is not violated.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to an embodiment of the present disclosure, and further provides an automatic traveling device including the provided electronic device.

FIG. 8 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples 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 apparatus 800 can also be stored. The calculation 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 bus 804.

A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 805 such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 performs the respective methods and processes described above, such as the path planning method. For example, in some embodiments, the path planning method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto the electronic device 800 via the ROM 802 and/or the communication unit 809. When loaded into RAM 803 and executed by the computing unit 801, a computer program may perform one or more steps of the path planning method described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the path planning 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 circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), and the Internet.

The computer system may include clients and servers. A client and server are generally 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 with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A path planning method, comprising:

2. The method according to claim 1, wherein before determining the matching path corresponding to the query endpoint according to the reference automatic traveling path corresponding to the preset endpoint, the method further comprises:

acquiring automatic driving state data of a historical automatic driving path, wherein the automatic driving state data of the historical automatic driving path comprises path data of the historical automatic driving path and state data capable of automatically driving on the historical automatic driving path;

determining a historical automatic driving path corresponding to the preset end point according to the path data of the historical automatic driving path;

and selecting at least one historical automatic driving path from the historical automatic driving paths corresponding to the preset end points according to the state data capable of automatically driving on the historical automatic driving paths corresponding to the preset end points, so as to be used as a reference automatic driving path corresponding to the preset end points.

3. The method of claim 2, wherein prior to the obtaining the automatic travel state data for the historical automatic travel path, further comprising:

and acquiring state data of automatic driving on the historical automatic driving path according to the taken-over condition of the automatic driving equipment on the historical automatic driving path.

4. The method according to any one of claims 1 to 3, wherein the determining a matching path corresponding to the query endpoint according to the reference automatic travel path corresponding to the preset endpoint comprises:

obtaining at least one reference automatic driving path corresponding to the query endpoint according to the reference automatic driving path corresponding to the preset endpoint;

acquiring road condition data of at least one reference automatic driving path corresponding to the query endpoint;

and determining the matching path from at least one reference automatic driving path corresponding to the query endpoint according to the road condition data.

5. The method according to any one of claims 1-4, wherein the method further comprises:

if the matched path corresponding to the query endpoint is not determined, acquiring automatic driving state data of a historical automatic driving path, wherein the automatic driving state data of the historical automatic driving path comprises the path data of the historical automatic driving path and state data capable of automatically driving on the historical automatic driving path;

determining a historical automatic driving path corresponding to the query endpoint according to the path data of the historical automatic driving path;

and selecting at least one historical automatic driving path from the historical automatic driving paths corresponding to the query end points according to the state data capable of automatically driving on the historical automatic driving paths corresponding to the query end points to serve as a matching path corresponding to the query end points.

6. The method of claim 5, wherein the method further comprises:

updating the query endpoint into the preset endpoint;

and updating at least one historical automatic driving path corresponding to the selected inquiry end point into a reference automatic driving path corresponding to the preset end point.

7. A path planner, comprising:

8. The apparatus according to claim 7, wherein the determination unit is further configured to acquire automatic travel state data of a historical automatic travel path, the automatic travel state data of the historical automatic travel path including path data of the historical automatic travel path and state data that is automatically travelable on the historical automatic travel path;

determining a historical automatic driving path corresponding to the preset end point according to the path data of the historical automatic driving path; and

9. The apparatus of claim 8, wherein the determining unit is further configured to

10. The apparatus according to any one of claims 7-9, wherein the determining unit is further configured to

acquiring road condition data of at least one reference automatic driving path corresponding to the query endpoint; and

11. The apparatus according to any of claims 7-10, wherein the determining unit is, in particular, for

determining a historical automatic driving path corresponding to the query endpoint according to the path data of the historical automatic driving path; and

12. The apparatus of claim 11, wherein the determining unit is further configured to

Updating the query endpoint into the preset endpoint;

13. 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 method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

16. An automatic traveling apparatus comprising the electronic apparatus according to claim 13.