CN114689061A - Navigation route processing method, device and electronic device for automatic driving equipment - Google Patents

Abstract

The present disclosure provides a method, device and electronic device for processing a navigation route of an automatic driving device, and relates to the field of computer technology, in particular to the field of artificial intelligence technology such as intelligent transportation and automatic driving technology. The specific implementation scheme is: obtaining the road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device; according to the road-based navigation route in the navigation map, using a preset matching model to obtain the road-based navigation route in the high-precision map Navigation route; according to the road-based navigation route in the high-precision map and the lane data in the high-precision map, obtain the lane-based navigation route for controlling the automatic driving device to drive.

Description

Navigation route processing method, device and electronic device for automatic driving equipment

technical field

The present disclosure relates to the field of computer technology, and in particular to the field of artificial intelligence technology such as intelligent transportation and automatic driving technology.

Background technique

Due to differences in the production process of different maps, the navigation routes based on different maps cannot be directly applied.

At present, in order to meet the needs of navigation and path planning of autonomous driving devices, it is sometimes necessary to convert the navigation routes of traditional navigation maps to navigation routes of high-precision maps that can be used by autonomous driving devices.

SUMMARY OF THE INVENTION

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

According to an aspect of the present disclosure, a method for processing a navigation route of an automatic driving device is provided, including:

Obtain the road-based navigation route in the navigation map according to the driving endpoint of the autonomous driving device;

According to the road-based navigation route in the navigation map, a preset matching model is used to obtain the road-based navigation route in the high-precision map;

According to the road-based navigation route in the high-precision map and the lane data in the high-precision map, a lane-based navigation route is obtained for controlling the automatic driving device to drive.

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

an obtaining unit for obtaining the road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device;

a matching unit, configured to obtain a 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;

The control unit is 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.

According to yet another aspect of the present disclosure, an electronic device is provided, 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 aspects and any possible implementations described above way method.

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

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

According to yet another aspect of the present disclosure, there is provided an autonomous driving vehicle including the 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 then can use the preset matching model according to the road-based navigation route in the navigation map. Obtaining the road-based navigation route in the high-precision 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 The device drives, because the road-based navigation route in the high-precision map that matches the road-based navigation route in the navigation map and the lane data obtained by using the preset matching model are obtained for controlling the driving of the autonomous driving device. The lane-based navigation route can achieve a more real-time and accurate lane-based navigation route for controlling the driving of the autonomous driving device, thereby ensuring the reliability of the lane-level navigation route serving the autonomous driving device.

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

Description of drawings

The accompanying drawings are used for better understanding of the present solution, and do not constitute a limitation to the present disclosure. in:

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

2 is a schematic diagram according to a second embodiment of the present disclosure;

3 is a schematic diagram of the principle of a navigation route processing method for an automatic driving device according to a second embodiment of the present disclosure;

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

FIG. 5 is a block diagram of an electronic device used to implement a navigation route processing method for an automatic driving device according to an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. 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 from the following description for clarity and conciseness.

Obviously, the described embodiments are some, but not all, embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

It should be noted that the terminal devices involved in the embodiments of the present disclosure may include, but are not limited to, mobile phones, personal digital assistants (Personal Digital Assistant, PDA), wireless handheld devices, tablet computers (Tablet Computer) and other smart devices; display devices may include However, it is not limited to devices with display functions such as personal computers and televisions.

In addition, the term "and/or" in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, There are three cases of B alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

High-resolution maps, also known as high-resolution maps, are maps for autonomous driving. The absolute position accuracy of high-precision maps can be close to 1 meter, and the relative position accuracy can reach 10-20 cm. Different from traditional navigation maps, high-precision maps can provide lane-level navigation routes in addition to road-level navigation routes.

Due to differences in the production processes of different maps, such as inconsistent road index marks and inconsistent road topology relationships in different maps, the navigation routes of different maps cannot be directly applied.

In order to meet the navigation requirements of autonomous driving devices, it is sometimes necessary to convert the navigation routes of traditional navigation maps to the navigation routes of high-precision maps that can be used by autonomous driving devices.

At present, the solution for obtaining the navigation route of the high-precision map is mainly to establish a matching table between the navigation map and the high-precision map, and use the matching table for indexing to obtain the navigation route required by the automatic driving device.

However, the navigation routes of high-precision maps obtained through related technologies have problems such as poor accuracy and fault tolerance, which may sometimes cause the automatic driving equipment to exit the automatic driving state.

Therefore, there is an urgent need to provide a navigation route processing method for an autonomous driving device, which can achieve real-time and effective lane-level navigation route, so as to ensure the reliability of the navigation route serving the autonomous driving device.

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

101. Obtain a road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device.

102. According to the road-based navigation route in the navigation map, use a preset matching model to obtain the road-based navigation route in the high-precision map.

103. 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 the driving endpoint of the automatic driving device may include a starting location and a target location. 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 some or all of the execution bodies of 101 to 103 may be applications located in the local terminal, or may also be functions such as plug-ins or software development kits (Software Development Kit, SDK) set in the applications located in the local terminal. unit, 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. This is not particularly limited.

It can be understood that the application may be a local program (nativeApp) 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, the road-based navigation route in the navigation map can be obtained according to the driving endpoint of the automatic driving device, and then the road-based navigation route in the high-precision map can be obtained by using a preset matching model according to the road-based navigation route in the navigation map. the navigation route, 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, so as to control the automatic driving device to drive. Using the road-based navigation route in the high-precision map and the lane data obtained by the preset matching model that matches the road-based navigation route in the navigation map, the lane-based navigation route can be obtained, which can achieve more real-time and accurate usage. It is used to control the lane-based navigation route of the autonomous driving device, thereby ensuring the reliability of the lane-level navigation route serving the autonomous driving device.

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

In this implementation manner, 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 can include road topology relationship and road positioning data. Wherein, the road positioning data may include but not limited to road coordinate data and road steering angle data.

In this implementation manner, the preset matching model may include, but is not limited to, a hidden Markov model and a neural network sequence model.

For example, the trajectory data of the road-based navigation route in the navigation map and the road connectivity data in the high-precision map can be input into the hidden Markov model, and the road-based navigation route in the high-precision map can be output.

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

In this specific implementation process, the trajectory data of the road-based navigation route in the navigation map and the road connectivity data in the high-precision map can be input into a hidden Markov model, and the matching probability of the trajectory data and the road connectivity data can be calculated, If the matching probability reaches the preset probability threshold, the road-based navigation route in the high-precision map corresponding to the matching probability may be used as the road-based navigation route in the output high-precision map.

It can be understood that the preset probability threshold may be a probability threshold determined according to actual service requirements.

In another specific implementation process of this implementation manner, a preset matching model may be used to further perform data matching and fusion processing on the road-based navigation route and the high-precision map in the navigation map, so as to combine the road-based navigation in the navigation map Routes are mapped into HD Maps for road-based navigation routes in HD Maps.

Specifically, the road-based navigation route in the navigation map may also include point of interest (POI) data of the navigation map, and then the road-based navigation route in the obtained high-precision map may include the POI data corresponding to the navigation map. The POI data of the high-precision map may include POI data obtained by mapping the POI data of the navigation map to the high-precision map.

In this way, the matching probability between the trajectory data and the road connectivity data can be obtained by using a preset matching model, and then the road-based data in the high-precision map can be obtained according to the matching probability and the preset probability threshold. For the navigation route, since a preset matching model is used to perform matching analysis on the trajectory data and the road connection data, a more accurate road-based navigation route in the high-precision map can be obtained, which further improves the obtained high-precision navigation route. The accuracy of the road-based navigation route in the map can facilitate the subsequent acquisition of a more accurate and effective lane-based navigation route based on the road-based navigation route in the high-precision map.

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 according to the road line data, Obtain road connectivity data in the high-precision map.

In this specific implementation process, road line data between the driving endpoints of the automatic driving device in the high-precision map can be obtained according to the driving endpoints of the automatic driving device. The road line data may include road baseline data and road centerline data. The road baseline data may be based on the leftmost road baseline data of the road.

In one case of the specific implementation process, the road baseline data in the high-precision map can be acquired, and then the road connectivity data in the high-precision map can be acquired according to the road baseline data.

Another situation of the specific implementation process is that the road centerline data in the high-precision map can be acquired, and then the road connectivity data in the high-precision map can be acquired according to the road centerline data.

In this way, by obtaining the road connectivity data in the high-precision map according to the obtained road line data in the high-precision map, the road connectivity data in the high-precision map can be accurately obtained, so that the road connectivity data can be used later, Road-based navigation routes in more accurate high-resolution maps can be obtained.

In another specific implementation process of this implementation manner, a navigation map within a preset distance is obtained according to the position data of the automatic driving device, the road-based navigation route in the navigation map, and the road connectivity data in the high-precision map The road-based navigation route and the road connectivity data in the high-precision map within a preset distance. Then, according to the road-based navigation route within the preset distance and the road connectivity data within the preset distance, using a preset matching model, the road-based navigation route in the high-precision map within the preset distance is obtained.

In the specific implementation process, the preset distance may be a preset distance ahead of the current position of the automatic driving device.

For example, the preset distance may be a distance of 5 kilometers in front of the starting point of the autonomous driving device. The road-based navigation route in the navigation map within 5 kilometers ahead of the starting point of the automatic driving device, and the road connectivity data in the high-precision map within 5 kilometers ahead of the starting point of the automatic driving equipment can be obtained.

It can be understood that the preset distance may be less than the total length of any navigation route. That is, a navigation route with a preset distance can be extracted based on any navigation route. Any navigation route can include multiple navigation routes with preset distances. Thereby, the road-based navigation route in the high-precision map within the preset distance can be dynamically obtained according to the position data of the automatic driving device, the road-based navigation route in the navigation map, and according to the preset distance.

For example, during the driving process of controlling the automatic driving device, the road-based navigation route in the high-precision map within the preset distance to be driven in the future can be obtained according to the preset distance. That is, based on the road-based navigation route in the navigation map, the road-based navigation route in the next segment of the high-precision map can be dynamically predicted according to preset distance segments.

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

In another specific implementation process of this implementation manner, the road-based navigation route in the navigation map may include trajectory data. First, the trajectory data of the road-based navigation route in the navigation map may be further acquired. Then, data thinning processing is performed on the trajectory data to obtain processed trajectory data. Again, road connectivity data in the high-precision map can be acquired. Finally, a road-based navigation route in the high-precision map can be obtained by using a preset matching model according to the processed trajectory data and the road connectivity data.

In this way, in this implementation manner, the road-based navigation route in the high-precision map can be obtained by using a preset matching model according to the road-based navigation route in the navigation map and obtaining the road connectivity data in the high-precision map, Because the preset matching model is used to match and fuse the road-based navigation route in the navigation map and the obtained road connectivity data to obtain the road-based navigation route in the high-precision map, the obtained high-precision map can be further improved. The accuracy of the road-based navigation route, so that the lane-based navigation route in the more accurate high-precision map can be obtained in the future.

It should be noted that various specific implementation processes for obtaining the road-based navigation route in the high-precision map provided in this implementation manner can be combined with each other to realize the navigation route processing method of the automatic driving device in this embodiment. For a detailed description, please refer to the related content in this implementation manner, which will not be repeated here.

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

In this implementation, the geofence data is lane-level geofence data. The geofence data may be data of areas where the autonomous driving device is not allowed to travel. The geofence data may be static data related to the lane state pre-judged based on the road state.

For example, geofence data may include, but is not limited to, curvature overruns, slope overruns, dead roads, toll booths, and the like. Wherein, the curvature exceeding limit and the gradient exceeding the limit may refer to that the curvature and gradient of the lane exceed the thresholds of the curvature and gradient that the automatic driving device can drive.

In this implementation manner, the lane connectivity data may be the lane-level connectivity relationship, the virtual-real situation of the lane line, and the lane positioning data. The connectivity relationship at the lane level may be a topology relationship at the lane level.

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

In this specific implementation process, the at least one lane-based navigation route may be screened according to the geo-fence data and preset conditions to obtain the lane-based navigation route.

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

In one case of this specific implementation process, it can be determined whether the lane in the at least one lane-based navigation route includes the geo-fence data, and if not, the lane-based navigation route can be provided to the automatic driving device as lane-based navigation routes.

It can be understood that, the map fence data of the lane can be obtained through a high-precision map, or the map fence data of the lane can be obtained from a cloud server, which is not limited in this implementation manner.

In this way, in this implementation manner, at least one lane-based navigation route among the road-based navigation routes in the high-precision map can be obtained by using a preset search algorithm, and based on the geo-fence data, the information provided to the automatic driving device can be obtained by screening. Lane-based navigation routes. Thus, 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 automatic driving device.

It should be noted that, based on the implementation manner of obtaining the lane-based navigation route provided in this implementation manner, the various specific implementation processes for obtaining the road-based navigation route in the high-precision map provided in the foregoing implementation manner can be combined to achieve The method for processing a navigation route of an automatic driving device in this embodiment is implemented. For a detailed description, please refer to the related content in the foregoing implementation manner, which will not be repeated here.

Optionally, in a possible implementation manner of this embodiment, after 103, further lane dynamic data in the lane-based navigation route may be acquired, and then the lane dynamic data and preset filters may be obtained according to the lane dynamic data. strategy to obtain filtered lane-based navigation routes.

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

In this implementation manner, the preset screening strategy may include: if the lane dynamic data does not match a preset dynamic event, filtering out the lane dynamic data and the preset dynamic event from the lane-based navigation route The lane-based navigation route for which the event does not match is used as the filtered lane-based navigation route.

Specifically, the preset dynamic events may include preset lane-level events and road conditions.

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

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

It can be understood that the speed limit in the lane obtained based on the historical driving data may be the speed limit in the lane based on the experience value, and the speed limit in the lane based on the experience value may not be the maximum speed limit in the lane specified when the lane is designed.

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

It can be understood that since the traffic situation of the lane changes in real time, the acquired real-time dynamic data of the lane can be used to further filter the acquired lane-based navigation route.

In this way, in this implementation manner, the filtered lane-based navigation route can be obtained according to the acquired lane dynamic data in the lane-based navigation route and the preset screening strategy, and the obtained lane-based navigation route can be realized. Further screening of the navigation route results in a lane-based navigation route with higher timeliness, thereby improving the timeliness and reliability of the lane-based navigation route for controlling the driving of the autonomous vehicle.

It should be noted that, based on the implementation manner of screening the obtained lane-based navigation route provided in this implementation manner, a variety of specific implementation processes of the acquired lane-based navigation route screening provided in the foregoing implementation manner can be combined, To implement the navigation route processing method of the automatic driving device in this embodiment. For a detailed description, please refer to the related content in the foregoing implementation manner, which will not be repeated here.

In this embodiment, the road-based navigation route in the navigation map can be obtained according to the driving endpoint of the automatic driving device, and then the high-precision map can be obtained by using a preset matching model according to the road-based navigation route in the navigation map. A road-based navigation route in the high-precision map, so that a 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, so as to control the automatic driving device to drive, Since the lane-based navigation route is obtained according to the road-based navigation route in the high-precision map that matches the road-based navigation route in the navigation map and the lane data obtained by using the preset matching model, it is possible to obtain a more real-time The lane-based navigation route is accurately used to control the driving of the autonomous driving device, thereby ensuring the reliability of the lane-level navigation route serving the autonomous driving device.

In addition, using the technical solution provided by this embodiment, the road-based navigation in the high-precision map can be obtained by using a preset matching model according to the road-based navigation route in the navigation map and the road connectivity data in the high-precision map. Route, because the preset matching model is used to match and fuse the road-based navigation route in the navigation map and the acquired road connectivity data to obtain the road-based navigation route in the high-precision map, which can further improve the obtained high-precision The accuracy of the road-based navigation route in the map, so that a more accurate lane-based navigation route in the high-precision map can be obtained later.

In addition, using the technical solution provided in this embodiment, the matching probability of the trajectory data and the road connectivity data can be obtained by using a preset matching model, and then the matching probability and the preset probability threshold can be used to obtain the high-precision map based on the matching probability. The navigation route of the road, because the preset matching model is used to match and analyze the trajectory data and the road connection data, a more accurate road-based navigation route in the high-precision map can be obtained, which further improves the accuracy of the obtained high-precision map. The accuracy of the road-based navigation route can facilitate the subsequent acquisition of a more accurate and effective lane-based navigation route based on the road-based navigation route in the high-precision map.

In addition, by adopting the technical solution provided in this embodiment, the road connectivity data in the high-precision map can be obtained according to the obtained road line data in the high-precision map, so that the road connectivity data in the high-precision map can be accurately obtained, So that the road connection data can be used later, a more accurate road-based navigation route in the high-precision map can be obtained.

In addition, with the technical solution provided in this embodiment, at least one lane-based navigation route among the road-based navigation routes in the high-precision map can be obtained by using a preset search algorithm, and based on the geo-fence data, it is possible to filter and provide the Lane-based navigation routes for self-driving devices. Thus, 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 automatic driving device.

In addition, using the technical solution provided by this embodiment, the filtered lane-based navigation route can be obtained according to the acquired lane dynamic data in the lane-based navigation route and the preset screening strategy, and the obtained lane-based navigation route can be realized. The lane-based navigation route is further screened to obtain a lane-based navigation route with higher timeliness, thereby improving the timeliness and reliability of the lane-based navigation route for controlling the driving of the autonomous vehicle.

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

201. Obtain a road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device.

In this embodiment, the travel endpoints of the automatic driving device may include a start location and a target location. 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. Obtain trajectory data of the road-based navigation route in the navigation map according to the road-based navigation route in the navigation map.

In this embodiment, first, the trajectory data according to the road-based navigation route in the navigation map can 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. Acquire road connectivity data in the high-precision map.

In this embodiment, the road line data in the high-precision map can be specifically obtained, and then the road connection data in the high-precision map can be obtained according to the road line data.

Specifically, road line data between the driving endpoints of the automatic driving device in the high-precision map can be obtained according to the driving endpoints of the automatic driving device. The road line data may include road baseline data and road centerline data. The road baseline data may be based on the leftmost road baseline data of the road.

204. According to the trajectory data of the road-based navigation route in the navigation map and the road connectivity data in the high-precision map, use a hidden Markov model to obtain a road-based navigation route in the high-precision map.

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

205. Acquire lane connectivity data and geofence data in the high-precision map.

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

207. Screen at least one lane-based navigation route according to the geo-fence data sum to obtain a lane-based navigation route.

So far, the automatic driving device can be controlled to drive according to the obtained lane-based navigation route.

It can be understood that since the traffic conditions of the lanes change in real time, after the lane-based navigation route is obtained, the acquired real-time lane dynamic data and the preset screening strategy can be used to obtain the lane-based navigation route. The route is further filtered to obtain a filtered lane-based navigation route.

3 is a schematic diagram of the principle of a navigation route processing method for an automatic driving device according to a second embodiment of the present disclosure. As shown in FIG. 3 , in the implementation process of this embodiment, first, the road-based navigation route 301 in the navigation map can be obtained in real time according to the driving endpoint of the automatic driving device. Then, dynamically obtain the trajectory data 302 of the navigation route, for example, dynamically obtain 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, and extract the trajectory data 302 of the navigation route The thinning process is performed to obtain thinned trajectory data 303 . At the same time, the high-precision map 304 can be obtained, and the baseline 305 can be extracted based on the high-precision map 304 to extract the road baseline in the high-precision map 304 , and then obtain road connectivity data 306 according to the road baseline in the high-precision map 304 . Thirdly, the thinned trajectory data 303 and the road connectivity data 306 are input into the preset Hidden Markov Model M307 (HMM) to obtain the road-based navigation route 308 in the high-precision map in real time. Thirdly, the lane-based navigation route 311 is extracted according to the road-based navigation route 308 in the high-precision map, and the acquired lane connection data 309 and geofence data 310 based on the high-precision map 304 . Finally, the lane-based navigation route is output in real time for driving the autonomous driving device.

In addition, in the process of matching the road-based navigation route in the navigation map with the road connectivity data in the high-precision map, if the road-based navigation route in the navigation map is normal, but the road connectivity data of some road sections in the high-precision map has data If there is a missing problem, that is, there is a problem section, you can manually take over the automatic driving device driving to the problem section until the automatic driving device drives over the problem section, and then control the automatic driving device to continue to execute automatic driving based on the output lane-based navigation route. drive.

In this embodiment, the lane-based navigation route can be obtained according to the road-based navigation route and the lane data in the high-precision map obtained by using the preset matching model that matches the road-based navigation route in the navigation map, The lane-based navigation route for controlling the driving of the automatic driving device in a more real-time and accurate manner can be obtained, thereby ensuring the reliability of the lane-level navigation route serving the automatic driving device.

In addition, using the technical solution provided by this embodiment can effectively combine the advantages of the navigation map with many POIs and accurate lane information in the high-precision map to realize lane-level navigation route planning for the automatic driving device.

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

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the described action sequences. Because certain steps may be performed in other orders or concurrently in accordance with the present disclosure. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to the relevant 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 in this embodiment may include an obtaining unit 401 , a matching unit 402 , and a control unit 403 . Wherein, the obtaining unit 401 is used for obtaining the road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device; the matching unit 402 is used for using a preset matching model according to the road-based navigation route in the navigation map , obtain the road-based navigation route in the high-precision map; the control unit 403 is configured to obtain the 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, for the purpose of The automatic driving device is controlled to drive.

It should be noted that, part or all of the navigation route processing device of the automatic driving device in this embodiment may be an application located in the local terminal, or may also be a plug-in or software development kit (Software Development Kit) set in the application located in the local terminal. Development Kit, SDK) and other functional units, 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 distributed system in an autonomous driving processing platform on the network side system, etc., which are not particularly limited in this embodiment.

It can be understood that the application may be a local program (nativeApp) 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.

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

Optionally, in a possible implementation manner of this embodiment, the road-based navigation route in the navigation map includes trajectory data, and the matching unit 402 may also be configured to use a preset matching model to obtain the The matching probability between the trajectory data and the road connectivity data is obtained according to the matching probability and a preset probability threshold to obtain a road-based navigation route in the high-precision map.

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

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

Optionally, in a possible implementation of this embodiment, the control unit 403 may also be configured to acquire lane dynamic data in the lane-based navigation route, according to the lane dynamic data and a preset Screening strategy to obtain filtered lane-based navigation routes.

In this embodiment, the obtaining unit can obtain the road-based navigation route in the navigation map according to the driving endpoint of the automatic driving device, and then the matching unit can obtain the road-based navigation route in the navigation map by using a preset matching model. The road-based navigation route in the high-precision map enables the control unit to obtain the 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 driving of the automatic driving device. By obtaining the lane-based navigation route according to the road-based navigation route in the high-precision map that matches the road-based navigation route in the navigation map and the lane data obtained by using the preset matching model, it is possible to obtain a more real-time and accurate The lane-based navigation route used to control the driving of the autonomous driving device, thereby ensuring the reliability of the lane-level navigation route serving the autonomous driving device.

In addition, using the technical solution provided by this embodiment, the road-based navigation in the high-precision map can be obtained by using a preset matching model according to the road-based navigation route in the navigation map and the road connectivity data in the high-precision map. Route, because the preset matching model is used to match and fuse the road-based navigation route in the navigation map and the acquired road connectivity data to obtain the road-based navigation route in the high-precision map, which can further improve the obtained high-precision The accuracy of the road-based navigation route in the map, so that a more accurate lane-based navigation route in the high-precision map can be obtained later.

In addition, using the technical solution provided in this embodiment, the matching probability of the trajectory data and the road connectivity data can be obtained by using a preset matching model, and then the matching probability and the preset probability threshold can be used to obtain the high-precision map based on the matching probability. The navigation route of the road, because the preset matching model is used to match and analyze the trajectory data and the road connection data, a more accurate road-based navigation route in the high-precision map can be obtained, which further improves the accuracy of the obtained high-precision map. The accuracy of the road-based navigation route can facilitate the subsequent acquisition of a more accurate and effective lane-based navigation route based on the road-based navigation route in the high-precision map.

In addition, by adopting the technical solution provided in this embodiment, the road connectivity data in the high-precision map can be obtained according to the obtained road line data in the high-precision map, so that the road connectivity data in the high-precision map can be accurately obtained, So that the road connection data can be used later, a more accurate road-based navigation route in the high-precision map can be obtained.

In addition, with the technical solution provided in this embodiment, at least one lane-based navigation route among the road-based navigation routes in the high-precision map can be obtained by using a preset search algorithm, and based on the geo-fence data, it is possible to filter and provide the Lane-based navigation routes for self-driving devices. Thus, 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 automatic driving device.

In addition, using the technical solution provided by this embodiment, the filtered lane-based navigation route can be obtained according to the acquired lane dynamic data in the lane-based navigation route and the preset screening strategy, and the obtained lane-based navigation route can be realized. The lane-based navigation route is further screened to obtain a lane-based navigation route with higher timeliness, thereby improving the timeliness and reliability of the lane-based navigation route for controlling the driving of the autonomous vehicle.

In addition, using the technical solution provided by this embodiment can effectively combine the advantages of the navigation map with many POIs and accurate lane information in the high-precision map to realize lane-level navigation route planning for the automatic driving device.

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

In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved are all in compliance with relevant laws and regulations, and do not violate public order and good customs.

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

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

5 shows a schematic block diagram of an example electronic device 500 that may 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, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example 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 that can be programmed according to a computer program stored in a read only memory (ROM) 502 or loaded into a random access memory (RAM) 503 from a storage unit 508 . Various appropriate actions and processes are performed. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501 , the ROM 502 , and the RAM 503 are connected to each other through a bus 504 . An input/output (I/O) interface 505 is also connected to bus 504 .

Various 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, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, and the like. 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.

Computing unit 501 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 501 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as a navigation route processing method for an automatic driving device. For example, in some embodiments, a navigation route processing method for an autonomous driving device may be implemented as a computer software program tangibly embodied on 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 on 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 automatic driving device described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the navigation route processing method of the autonomous driving device by any other suitable means (eg, by means of firmware).

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), complex programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an 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 code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

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

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a distributed system server, or a server combined with blockchain.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can 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, no limitation is imposed herein.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.

Claims (16)

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

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.

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:

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 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;

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.

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

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.

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;

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

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

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;

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.

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