CN114689061A

CN114689061A - Navigation route processing method and device of automatic driving equipment and electronic equipment

Info

Publication number: CN114689061A
Application number: CN202210134720.1A
Authority: CN
Inventors: 闫超; 孙跃锋
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-02-14
Filing date: 2022-02-14
Publication date: 2022-07-01

Abstract

The present disclosure provides a navigation route processing method and apparatus for an automatic driving device, and an electronic device, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as intelligent transportation and automatic driving technology. The specific implementation scheme is as follows: acquiring a navigation route based on a road in a navigation map according to a driving endpoint of the automatic driving equipment; obtaining a road-based navigation route in a high-precision map by using a preset matching model according to the road-based navigation route in the navigation map; and obtaining a navigation route based on a lane according to the navigation route based on the road in the high-precision map and the lane data in the high-precision map so as to control the automatic driving equipment to drive.

Description

Navigation route processing method and device of automatic driving equipment and electronic equipment

Technical Field

The present disclosure relates to the field of computer technology, and more particularly to the field of artificial intelligence technology such as intelligent transportation and autopilot technology.

Background

Because the manufacturing processes of different maps are different, the navigation routes based on different maps cannot be directly applied.

At present, in order to meet the requirements of navigation and path planning of an automatic driving device, a navigation route of a traditional navigation map needs to be converted into a navigation route of a high-precision map which can be used by the automatic driving device.

Disclosure of Invention

The disclosure provides a navigation route processing method and device of an automatic driving device, an electronic device and a storage medium.

According to an aspect of the present disclosure, there is provided a navigation route processing method of an autonomous driving apparatus, including:

acquiring a navigation route based on a road in a navigation map according to a driving endpoint of the automatic driving equipment;

obtaining a road-based navigation route in a high-precision map by using a preset matching model according to the road-based navigation route in the navigation map;

and obtaining a navigation route based on a lane according to the navigation route based on the road in the high-precision map and the lane data in the high-precision map so as to control the automatic driving equipment to drive.

According to another aspect of the present disclosure, there is provided a navigation route processing apparatus of an autonomous driving device, including:

an obtaining unit configured to obtain a road-based navigation route in a navigation map according to a driving end point of an automatic driving apparatus;

the matching unit is used for obtaining the navigation route based on the road in the high-precision map by utilizing a preset matching model according to the navigation route based on the road in the navigation map;

and the control unit is used for obtaining a lane-based navigation route according to the road-based navigation route in the high-precision map and the lane data in the high-precision map so as to control the automatic driving equipment to drive.

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 yet another aspect of the present disclosure, there is provided an autonomous vehicle comprising an electronic device as described above.

As can be seen from the above technical solutions, the embodiment of the present disclosure obtains the road-based navigation route in the navigation map according to the driving end point of the automatic driving device, and further obtains the road-based navigation route in the high-precision map by using the preset matching model according to the road-based navigation route in the navigation map, so that the lane-based navigation route can be obtained according to the road-based navigation route in the high-precision map and the lane data in the high-precision map for controlling the automatic driving device to drive, since the lane-based navigation route for controlling the automatic driving device to drive is obtained by using the road-based navigation route in the high-precision map that is matched with the road-based navigation route in the navigation map and the lane data obtained by using the preset matching model, the lane-based navigation route for controlling the automatic driving device to drive can be obtained more accurately in real time, thereby ensuring reliability of the navigation route at the lane level serving the autonomous device.

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 diagram according to a second embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the principles of a navigation route processing method of an autonomous device according to a second embodiment of the present disclosure;

FIG. 4 is a schematic diagram according to a third embodiment of the present disclosure;

fig. 5 is a block diagram of an electronic device for implementing a navigation route processing method of an autonomous driving apparatus 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.

A high-precision map, also called a high-resolution map, is a map that serves for automatic driving. The absolute position accuracy of the high-precision map can be close to 1 m, and the relative position accuracy can reach 10-20 cm. Unlike the conventional navigation map, the high precision map may provide a lane-level navigation route in addition to a road-level navigation route.

Because the manufacturing processes of different maps have differences, such as the differences of inconsistent road index identification and inconsistent road topological relation in different maps, the navigation routes of different maps cannot be directly used.

In order to meet the navigation requirements of the automatic driving device, it is sometimes necessary to convert the navigation route of the conventional navigation map to the navigation route of a high-precision map that can be used by the automatic driving device.

At present, a scheme for obtaining a navigation route of a high-precision map mainly establishes a matching table of the navigation map and the high-precision map, and indexes the high-precision map by using the matching table to obtain the navigation route required by an automatic driving device.

However, the navigation route of the high-precision map obtained by the related art has problems of poor accuracy and fault tolerance, and the like, and may sometimes cause the autonomous driving apparatus to exit from the autonomous driving state.

Therefore, it is desirable to provide a navigation route processing method of an autonomous driving apparatus, which can achieve obtaining a real-time and effective lane-level navigation route to ensure reliability of a navigation route serving for driving of the autonomous driving apparatus.

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

101. And obtaining a navigation route based on the road in the navigation map according to the driving end point of the automatic driving equipment.

102. And obtaining the navigation route based on the road in the high-precision map by utilizing a preset matching model according to the navigation route based on the road in the navigation map.

103. And obtaining a navigation route based on a lane according to the navigation route based on the road in the high-precision map and the lane data in the high-precision map so as to control the automatic driving equipment to drive.

It should be noted that the driving end points of the automatic driving device may include a start point and a target point. The road-based navigation route in the navigation map may be a road-level navigation route between a start location and a target location in the navigation map.

It should be noted that the lane-based navigation route may be a lane-level navigation route in a high-precision map.

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 autopilot 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.

In this way, it is possible to obtain a road-based navigation route in a navigation map by obtaining a road-based navigation route in the navigation map according to a driving end point of an autopilot device, and further, it is possible to obtain a road-based navigation route in a high-precision map by using a preset matching model, so that it is possible to obtain a lane-based navigation route for controlling the autopilot device to drive according to the road-based navigation route in the high-precision map and lane data in the high-precision map, since it is possible to obtain a lane-based navigation route more accurately for controlling the autopilot device to drive in real time by obtaining a lane-based navigation route according to a road-based navigation route in a high-precision map matched with a road-based navigation route in a navigation map obtained by using a preset matching model and lane data, thereby ensuring reliability of the navigation route at the lane level serving the autopilot device.

Optionally, in a possible implementation manner of this embodiment, in 102, the road connectivity data in the high-precision map may be obtained, and then the navigation route based on the road in the high-precision map may be obtained by using a preset matching model according to the navigation route based on the road in the navigation map and the road connectivity data.

In this implementation, the road-based navigation route in the navigation map may include, but is not limited to, trajectory data.

The road connectivity data in the high-precision map may include road topology relationships and road positioning data. The road positioning data may include, but is not limited to, road coordinate data and road steering angle data, among others.

In this implementation, the preset matching models may include, but are not limited to, hidden markov models and neural network sequence models.

For example, trajectory data of a road-based navigation route in the navigation map and road connectivity data in the high-precision map may be input to the hidden markov model, and the road-based navigation route in the high-precision map may be output.

In a specific implementation process of the implementation manner, a preset matching model may be specifically used to obtain a matching probability between the trajectory data and the road connectivity data, and further, a road-based navigation route in the high-precision map may be obtained according to the matching probability and a preset probability threshold.

In the specific implementation process, the track data of the road-based navigation route in the navigation map and the road communication data in the high-precision map can be input into a hidden markov model, the matching probability of the track data and the road communication data is calculated, and if the matching probability reaches a preset probability threshold, the road-based navigation route in the high-precision map corresponding to the matching probability can be used as the road-based navigation route in the output high-precision map.

It is understood that the preset probability threshold may be a probability threshold determined according to actual traffic demands.

In another specific implementation process of this implementation, a preset matching model may be further used to perform data matching and fusion processing on the road-based navigation route in the navigation map and the high-precision map, so as to map the road-based navigation route in the navigation map into the high-precision map, so as to obtain the road-based navigation route in the high-precision map.

Specifically, the navigation route based on the road in the navigation map may further include Point of Interest (POI) data of the navigation map, and the obtained navigation route based on the road in the high-precision map may further include Point of Interest data of the high-precision map corresponding to the POI data of the navigation map, that is, may include mapping the POI data of the navigation map to the obtained POI data of the high-precision map.

Therefore, the matching probability of the track data and the road communication data can be obtained by utilizing a preset matching model, and further the navigation route based on the road in the high-precision map can be obtained according to the matching probability and a preset probability threshold value.

In another specific implementation process of this implementation manner, in the process of acquiring the road connectivity data in the high-precision map, the road line data in the high-precision map may be specifically acquired, and then the road connectivity data in the high-precision map may be acquired according to the road line data.

In this specific implementation process, the road line data between the driving end points of the automatic driving devices in the high-precision map may be acquired according to the driving end points of the automatic driving devices. The roadway line data may include roadway baseline data and roadway centerline data. The road baseline data may be based on the leftmost road baseline data of the road.

One condition of the specific implementation process is that the road baseline data in the high-precision map can be acquired, and then the road communication data in the high-precision map can be acquired according to the road baseline data.

In another specific implementation process, the road centerline data in the high-precision map may be obtained, and then the road connectivity data in the high-precision map may be obtained according to the road centerline data.

Therefore, the road communication data in the high-precision map can be acquired according to the acquired road route data in the high-precision map, so that the road communication data in the high-precision map can be accurately acquired, the road communication data can be utilized subsequently, and a more accurate navigation route based on roads in the high-precision map can be acquired.

In another specific implementation process of the implementation manner, the navigation route based on the road in the navigation map within the preset distance and the road communication data in the high-precision map within the preset distance are acquired according to the position data of the automatic driving device, the navigation route based on the road in the navigation map and the road communication data in the high-precision map within the preset distance. And then, according to the navigation route based on the road within the preset distance and the road communication data within the preset distance, obtaining the navigation route based on the road in the high-precision map within the preset distance by using a preset matching model.

In this particular implementation, the preset distance may be a preset distance forward of the current location of the autonomous driving device.

For example, the preset distance may be a distance of 5 km ahead of the start location of the autonomous device. A road-based navigation route in a navigation map within 5 kilometers ahead of the start location of the autonomous device may be obtained, and the road connectivity data in a high-precision map within 5 kilometers ahead of the start location of the autonomous device may be obtained.

It will be appreciated that the predetermined distance may be less than the total length of any one of the navigation routes. That is, the navigation route with the preset distance can be extracted based on any one of the navigation routes. Any one of the navigation routes may include a plurality of navigation routes having a preset distance. Therefore, the road-based navigation route in the high-precision map within the preset distance can be dynamically obtained according to the preset distance according to the position data of the automatic driving equipment and the road-based navigation route in the navigation map.

For example, in controlling the travel of the automatic driving apparatus, the road-based navigation route in the high-precision map within the preset distance to be traveled in the future may be obtained in accordance with the preset distance. That is, the road-based navigation route in the high-precision map within the next distance can be dynamically predicted in segments according to the preset distance based on the road-based navigation route in the navigation map.

Thus, by dynamically obtaining the road-based navigation route in the high-precision map according to the preset distance, the burden of navigation route processing and hardware requirements can be reduced, and thus the related processing efficiency can be optimized.

In yet another specific implementation of this implementation, the road-based navigation route in the navigation map may include trajectory data. First, trajectory data of a road-based navigation route in the navigation map may be further acquired. And then, performing data thinning processing on the track data to obtain processed track data. Again, road connectivity data in the high precision map may be obtained. And finally, obtaining a navigation route based on the road in the high-precision map by using a preset matching model according to the processed track data and the road communication data.

In this way, in this implementation manner, the navigation route based on the road in the high-precision map can be obtained by using the preset matching model according to the navigation route based on the road in the navigation map and the road communication data in the high-precision map, and the navigation route based on the road in the high-precision map and the obtained road communication data are subjected to matching fusion processing by using the preset matching model, so that the navigation route based on the road in the high-precision map can be obtained, the accuracy of the navigation route based on the road in the obtained high-precision map can be further improved, and the more accurate navigation route based on the lane in the high-precision map can be obtained subsequently.

It should be noted that, in the implementation manner, various specific implementation processes for obtaining a road-based navigation route in a high-precision map may be combined with each other to implement the navigation route processing method of the autopilot apparatus in this embodiment. For detailed description, reference may be made to relevant contents in the present implementation, and details are not described herein.

Optionally, in a possible implementation manner of this embodiment, the lane data may include, but is not limited to, geo-fence data and lane connectivity data, and in 103, at least one lane-based navigation route in the road-based navigation routes in the high-precision map may be obtained by using a preset search algorithm according to the lane connectivity data, and then the at least one lane-based navigation route may be filtered according to the geo-fence data to obtain the lane-based navigation route.

In this implementation, the geofence data is the lane level geofence data. The geofence data may be area data that does not allow the autonomous device to travel. The geofence data may be static data that is predicted to be relevant to lane status based on road status.

For example, geofence data may include, but is not limited to, curvature overrun, grade overrun, head break, toll booth, and the like. The curvature overrun and gradient overrun may refer to the fact that the curvature and gradient of the lane exceed the threshold values of the curvature and gradient which can be driven by the automatic driving device.

In this implementation, the lane connectivity data may be connectivity at lane level, lane line virtuality and lane positioning data. The connection relationship at the lane level may be a topological relationship at the lane level.

In a specific implementation procedure of this implementation, the preset search algorithm may include, but is not limited to, a depth search algorithm. Specifically, at least one lane-based navigation route in the road-based navigation routes in the high-precision map is obtained by using a depth search algorithm according to the lane connectivity data, and then the at least one lane-based navigation route is screened according to the geo-fence data to obtain the lane-based navigation route.

In this particular implementation, the at least one lane-based navigation route may be filtered according to the geo-fence data and a preset condition to obtain the lane-based navigation route.

In particular, the preset condition may include that lanes in the lane-based navigation route do not include geofence data.

One aspect of this particular implementation may be that it is determined whether a lane of the at least one lane-based navigation route includes the geofence data, and if not, the lane-based navigation route may be provided as the lane-based navigation route provided to the autonomous device.

It can be understood that the map fence data of the lane can be acquired through the high-precision map, and the map fence data of the lane can also be acquired from the cloud server.

Thus, in this implementation, at least one lane-based navigation route in the road-based navigation routes in the high-precision map may be obtained by using a preset search algorithm, and the lane-based navigation route provided to the autonomous driving apparatus may be obtained by screening based on the geo-fence data. Thereby, the accuracy of the obtained lane-based navigation route can be further improved, thereby further improving the reliability of the lane-level navigation route serving the autonomous driving apparatus.

It should be noted that, based on the implementation manner for obtaining the lane-based navigation route provided in the implementation manner, the navigation route processing method of the automatic driving device of the embodiment may be implemented by combining a plurality of specific implementation processes for obtaining the road-based navigation route in the high-precision map provided in the foregoing implementation manner. 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, after 103, lane dynamic data in the lane-based navigation route may be further obtained, and then the screened lane-based navigation route may be obtained according to the lane dynamic data and a preset screening policy.

In this implementation, the lane dynamics data may include, but is not limited to, lane-level events and real-time road conditions.

In this implementation, the preset screening policy may include: and if the lane dynamic data are not matched with the preset dynamic events, screening out a lane-based navigation route with the lane dynamic data not matched with the preset dynamic events from the lane-based navigation routes to serve as the screened lane-based navigation route.

Specifically, the preset dynamic event may include a preset lane-level event and a road condition.

Specifically, the lane-level events may include, but are not limited to, traffic accident events, congestion events, regulatory events, construction events, and dynamic event information such as weather events on the lanes.

Specifically, the preset road conditions may include, but are not limited to, vehicle road surface driving conditions, lane speed limit conditions obtained based on historical driving data, and other road surface conditions.

It is understood that the lane speed limit situation obtained based on the historical travel data may be a lane speed limit situation based on an empirical value, and the lane speed limit situation based on the empirical value may not be the highest lane speed limit specified when the lane is designed.

In a specific implementation process of the implementation, it may be determined whether the lane dynamic data in the lane-based navigation route matches a preset dynamic event, that is, whether the lane dynamic data in the lane-based navigation route includes the preset dynamic event may be determined, and if the lane dynamic data in the lane-based navigation route does not include the preset dynamic event, the lane-based navigation route may be used as the lane-based navigation route provided to the autopilot device.

It can be understood that, since the traffic condition of the lane is changed in real time, the obtained lane-based navigation route can be further filtered by using the obtained real-time lane dynamic data.

Thus, in the implementation manner, the screened lane-based navigation route can be obtained according to the lane dynamic data in the acquired lane-based navigation route and the preset screening strategy, so that the obtained lane-based navigation route can be further screened, and the lane-based navigation route with higher timeliness can be obtained, thereby improving timeliness and reliability of the lane-based navigation route for controlling the automatic driving vehicle to run.

It should be noted that, based on the implementation manner of screening the obtained lane-based navigation route provided in the present implementation manner, the navigation route processing method of the autopilot apparatus of the present embodiment may be implemented by combining a plurality of specific implementation procedures of screening the obtained lane-based navigation route provided in the foregoing implementation manner. 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 road-based navigation route in the navigation map can be obtained according to the driving end point of the automatic driving device, and further the road-based navigation route in the high-precision map can be obtained according to the road-based navigation route in the navigation map by using the preset matching model, so that the lane-based navigation route can be obtained according to the road-based navigation route in the high-precision map and the lane data in the high-precision map for controlling the automatic driving device to drive, and the lane-based navigation route can be obtained by obtaining the lane-based navigation route according to the road-based navigation route in the high-precision map matched with the road-based navigation route in the navigation map and the lane data obtained by using the preset matching model, thereby realizing that the lane-based navigation route for controlling the automatic driving device to drive more accurately in real time can be obtained, thereby ensuring reliability of the navigation route at the lane level serving the autopilot device.

In addition, by adopting the technical scheme provided by the embodiment, the navigation route based on the road in the high-precision map can be obtained by utilizing the preset matching model according to the navigation route based on the road in the navigation map and the road communication data in the high-precision map, and the navigation route based on the road in the navigation map and the obtained road communication data are subjected to matching fusion processing by utilizing the preset matching model, so that the navigation route based on the road in the high-precision map can be obtained, the accuracy of the navigation route based on the road in the obtained high-precision map can be further improved, and the more accurate navigation route based on the lane in the high-precision map can be obtained subsequently.

In addition, by adopting the technical scheme provided by the embodiment, the matching probability of the track data and the road communication data can be obtained by utilizing the preset matching model, and further the road-based navigation route in the high-precision map can be obtained according to the matching probability and the preset probability threshold.

In addition, by adopting the technical scheme provided by the embodiment, the road communication data in the high-precision map can be acquired according to the acquired road route data in the high-precision map, so that the road communication data in the high-precision map can be accurately acquired, the road communication data can be conveniently utilized subsequently, and a more accurate navigation route based on roads in the high-precision map can be acquired.

In addition, by adopting the technical scheme provided by the embodiment, at least one lane-based navigation route in the road-based navigation routes in the high-precision map can be obtained by utilizing a preset search algorithm, and the lane-based navigation route provided for the automatic driving equipment is obtained by screening based on the geo-fence data. Thereby, the accuracy of the obtained lane-based navigation route can be further improved, thereby further improving the reliability of the lane-level navigation route serving the autonomous driving apparatus.

In addition, by adopting the technical scheme provided by the embodiment, the screened lane-based navigation route can be obtained according to the lane dynamic data in the obtained lane-based navigation route and the preset screening strategy, the obtained lane-based navigation route can be further screened, and the lane-based navigation route with higher timeliness can be obtained, so that the timeliness and the reliability of the lane-based navigation route for controlling the automatic driving vehicle to run are improved.

Fig. 2 is a schematic diagram according to a second embodiment of the present disclosure, as shown in fig. 2.

201. And obtaining a navigation route based on the road in the navigation map according to the driving end point of the automatic driving equipment.

In the present embodiment, the travel end points of the automatic driving apparatus may include a start point and a target point. The road-based navigation route in the navigation map may be a road-level navigation route between a start location and a target location in the navigation map.

202. And obtaining the track data of the navigation route based on the road in the navigation map according to the navigation route based on the road in the navigation map.

In the present embodiment, first, trajectory data according to the road-based navigation route in the navigation map may be acquired according to the road-based navigation route in the navigation map. Then, data thinning processing is performed on the trajectory data to obtain processed trajectory data.

203. And acquiring road communication data in the high-precision map.

In this embodiment, the road line data in the high-precision map may be specifically acquired, and further, the road connectivity data in the high-precision map may be acquired according to the road line data.

Specifically, the road line data between the travel end points of the autonomous devices in the high-precision map may be acquired from the travel end points of the autonomous devices. The roadway line data may include roadway baseline data and roadway centerline data. The road baseline data may be based on the leftmost road baseline data of the road.

204. And obtaining the navigation route based on the road in the high-precision map by using a hidden Markov model according to the track data of the navigation route based on the road in the navigation map and the road communication data in the high-precision map.

Specifically, the road-based navigation route in the navigation map may further include POI data of the navigation map, and the obtained road-based navigation route in the high-precision map may further include point-of-interest data of the high-precision map corresponding to the point-of-interest data of the navigation map.

205. And acquiring lane communication data and geofence data in the high-precision map.

206. And obtaining at least one lane-based navigation route in the road-based navigation routes in the high-precision map by using a preset search algorithm according to the lane communication data.

207. At least one lane-based navigation route is filtered based on the geo-fence data sum to obtain a lane-based navigation route.

Up to this point, the autopilot device may be controlled to travel according to the obtained lane-based navigation route.

It can be understood that, since the traffic condition of the lane is changed in real time, after the lane-based navigation route is obtained, the obtained lane-based navigation route may be further subjected to a screening process using the obtained real-time lane dynamic data and a preset screening policy to obtain a screened lane-based navigation route.

Fig. 3 is a schematic diagram of the principle of a navigation route processing method of an autonomous driving apparatus according to a second embodiment of the present disclosure. As shown in fig. 3, in the implementation of the present embodiment, first, a road-based navigation route 301 in the navigation map may be obtained in real time according to the driving end point of the automatic driving device. Then, the trajectory data 302 of the navigation route is dynamically acquired, for example, the trajectory data within 5 kilometers ahead of the current driving position of the automatic driving device of the road-based navigation route in the navigation map is dynamically acquired, and the trajectory data 302 of the navigation route is subjected to rarefaction processing, so as to obtain the rarefaction trajectory data 303. Meanwhile, a high-precision map 304 can be obtained, baseline 305 extraction is performed on the basis of the high-precision map 304, a road baseline in the high-precision map 304 is extracted, and then road communication data 306 is obtained according to the road baseline in the high-precision map 304. Again, the diluted trajectory data 303 and the road connectivity data 306 are input into a preset hidden markov model M307(HMM) to obtain a road-based navigation route 308 in a high-precision map in real time. Again, a lane-based navigation route 311 is extracted from the road-based navigation route 308 in the high-precision map, and the lane connectivity data 309 and the geo-fence data 310 obtained from the high-precision map 304. And finally, outputting the navigation route based on the lane in real time so as to control the automatic driving equipment to drive.

In addition, in the process of matching the navigation route based on the road in the navigation map and the road communication data in the high-precision map, if the navigation route based on the road in the navigation map is normal, but the road communication data of a part of road sections in the high-precision map has a data loss problem, namely a problem road section exists, the automatic driving equipment driving to the problem road section can be manually taken over until the automatic driving equipment drives to the problem road section, and then the automatic driving equipment is controlled to continuously execute automatic driving based on the output navigation route based on the lane.

In this embodiment, the lane-based navigation route may be obtained according to the road-based navigation route in the high-precision map that is obtained by using the preset matching model and matches the road-based navigation route in the navigation map, and the lane data, and the lane-based navigation route that is more accurately used to control the driving of the autonomous device in real time may be obtained, thereby ensuring the reliability of the lane-level navigation route that serves the autonomous device.

In addition, by adopting the technical scheme provided by the embodiment, the lane-level navigation route planning of the automatic driving equipment can be realized by effectively combining the advantages of more POI (point of interest) of the navigation map and accurate lane information of the high-precision map.

In addition, by adopting the technical scheme provided by the embodiment, the user experience of automatic driving can be further improved by carrying out automatic driving based on a more real-time and reliable lane level navigation route.

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 will appreciate that the embodiments described in the specification are presently preferred and that acts and modules are not required for the present 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. 4 is a schematic diagram according to a third embodiment of the present disclosure, as shown in fig. 4. The navigation route processing apparatus 400 of the automatic driving device of the present embodiment may include an obtaining unit 401, a matching unit 402, and a control unit 403. The obtaining unit 401 is configured to obtain a navigation route based on a road in a navigation map according to a driving endpoint of an automatic driving device; a matching unit 402, configured to obtain a road-based navigation route in a high-precision map by using a preset matching model according to the road-based navigation route in the navigation map; a control unit 403, configured to obtain a lane-based navigation route according to the road-based navigation route in the high-precision map and the lane data in the high-precision map, so as to control the automatic driving device to drive.

It should be noted that, part or all of the navigation route processing device of the autopilot apparatus of this embodiment may be an application located at the local terminal, or may also be a functional unit such as a Software Development Kit (SDK) or a plug-in provided 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 autopilot processing platform on the network side, and this embodiment is not particularly limited to this.

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

Optionally, in a possible implementation manner of this embodiment, the matching unit 402 may be specifically configured to obtain road connectivity data in the high-precision map, and obtain a navigation route based on a road in the high-precision map by using a preset matching model according to the navigation route based on the road in the navigation map and the road connectivity data.

Optionally, in a possible implementation manner of this embodiment, the navigation route based on the road in the navigation map includes track data, and the matching unit 402 may be further configured to obtain a matching probability between the track data and the road connectivity data by using a preset matching model, and obtain the navigation route based on the road in the high-precision map according to the matching probability and a preset probability threshold.

Optionally, in a possible implementation manner of this embodiment, the matching unit 402 may be specifically configured to acquire road route data in the high-precision map, and acquire road connectivity data in the high-precision map according to the road route data.

Optionally, in a possible implementation manner of this embodiment, the lane data includes geo-fence data and lane connectivity data, and the control unit 403 is specifically configured to obtain at least one lane-based navigation route in the road-based navigation routes in the high-precision map by using a preset search algorithm according to the lane connectivity data, and filter the at least one lane-based navigation route according to the geo-fence data to obtain the lane-based navigation route.

Optionally, in a possible implementation manner of this embodiment, the control unit 403 may be further configured to obtain lane dynamic data in the lane-based navigation route, and obtain the screened lane-based navigation route according to the lane dynamic data and a preset screening policy.

In this embodiment, the road-based navigation route in the navigation map may be obtained by the obtaining unit according to the driving end point of the automatic driving device, and further the road-based navigation route in the high-precision map may be obtained by the matching unit according to the road-based navigation route in the navigation map using a preset matching model, so that the control unit may obtain the lane-based navigation route for controlling the automatic driving device to drive according to the road-based navigation route in the high-precision map and the lane data in the high-precision map obtained by using the preset matching model, and since the lane-based navigation route is obtained by obtaining the lane-based navigation route according to the road-based navigation route in the high-precision map matched with the road-based navigation route in the navigation map and the lane data using the preset matching model, it is possible to obtain the lane-based navigation route more accurately used for controlling the automatic driving device to drive in real time, thereby ensuring reliability of the navigation route at the lane level serving the autonomous device.

In addition, by adopting the technical scheme provided by the embodiment, the navigation route based on the road in the high-precision map can be obtained by utilizing the preset matching model according to the navigation route based on the road in the navigation map and the road communication data in the high-precision map, and the navigation route based on the road in the high-precision map and the obtained road communication data are subjected to matching fusion processing by utilizing the preset matching model, so that the navigation route based on the road in the high-precision map can be obtained, the accuracy of the navigation route based on the road in the obtained high-precision map can be further improved, and the more accurate navigation route based on the lane in the high-precision map can be obtained subsequently.

In addition, by adopting the technical scheme provided by the embodiment, the matching probability of the track data and the road communication data can be obtained by utilizing the preset matching model, and then the navigation route based on the road in the high-precision map can be obtained according to the matching probability and the preset probability threshold.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

Further, the present disclosure also provides an autonomous vehicle including the provided electronic device. The autonomous vehicle may be an L3/L4 class autonomous vehicle.

FIG. 5 illustrates a schematic block diagram of an example electronic device 500 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 intended to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 5, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in the electronic device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 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 so forth. The calculation unit 501 executes the respective methods and processes described above, such as the navigation route processing method of the automatic driving apparatus. For example, in some embodiments, the navigation routing processing method of the autonomous device may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the navigation route processing method of the autopilot device described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other suitable way (e.g., by means of firmware) to perform the navigation route processing method of the autonomous device.

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 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/acts 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 combining a blockchain.

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

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 navigation route processing method of an autonomous driving apparatus, comprising:

2. The method according to claim 1, wherein the obtaining of the road-based navigation route in the high-precision map by using a preset matching model according to the road-based navigation route in the navigation map comprises:

acquiring road communication data in the high-precision map;

and obtaining the navigation route based on the road in the high-precision map by utilizing a preset matching model according to the navigation route based on the road in the navigation map and the road communication data.

3. The method of claim 2, wherein the road-based navigation route in the navigation map comprises track data, and the obtaining of the road-based navigation route in the high-precision map by using a preset matching model according to the road-based navigation route in the navigation map and the road connectivity data comprises:

obtaining the matching probability of the track data and the road communication data by using a preset matching model;

and acquiring a navigation route based on the road in the high-precision map according to the matching probability and a preset probability threshold.

4. The method of claim 2 or 3, wherein the obtaining of the road connectivity data in the high-precision map comprises:

acquiring road route data in the high-precision map;

and acquiring the road communication data in the high-precision map according to the road route data.

5. The method of any of claims 1-4, wherein the lane data includes geofence data and lane connectivity data, the obtaining a lane-based navigation route from the road-based navigation route in the high-precision map and the lane data in the high-precision map comprising:

obtaining at least one lane-based navigation route in the high-precision map based on the road navigation routes by using a preset search algorithm according to the lane communication data;

filtering the at least one lane-based navigation route according to the geo-fence data to obtain the lane-based navigation route.

6. The method according to any one of claims 1-5, wherein after obtaining the lane-based navigation route from the road-based navigation route in the high-precision map and the lane data in the high-precision map, further comprising:

acquiring lane dynamic data in the lane-based navigation route;

and obtaining a screened navigation route based on the lane according to the lane dynamic data and a preset screening strategy.

7. A navigation route processing apparatus of an autonomous driving device, comprising:

and the control unit is used for obtaining a navigation route based on a lane according to the navigation route based on the road in the high-precision map and the lane data in the high-precision map so as to control the automatic driving equipment to drive.

8. The apparatus of claim 7, wherein the matching unit is specifically configured to

Acquiring road communication data in the high-precision map;

9. The apparatus of claim 8, wherein the road-based navigation route in the navigation map comprises trajectory data, the matching unit further configured to

10. Device according to claim 8 or 9, wherein the matching unit is in particular for

Acquiring road route data in the high-precision map;

11. The apparatus according to any of claims 7-10, wherein the lane data comprises geofence data and lane connectivity data, the control unit, in particular for

screening the at least one lane-based navigation route according to the geo-fence data to obtain the lane-based navigation route.

12. The apparatus according to any one of claims 7-11, wherein the control unit is further configured to

Acquiring lane dynamic data in the lane-based navigation route;

and obtaining a screened lane-based navigation route according to the lane dynamic data and a preset screening strategy.

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 autonomous vehicle comprising the electronic device of claim 13.