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

Abstract

The present disclosure provides a navigation route processing method and device of an autonomous driving device and an electronic device, and relates to the technical field of computers, specifically to the technical field of artificial intelligence such as intelligent transportation and autonomous driving. The specific implementation scheme is: obtaining a road-based navigation route in a navigation map according to a driving endpoint of an autonomous driving device; 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 lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, so as to control the autonomous driving device to drive.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computers, in particular to the technical field of artificial intelligence such as intelligent transportation and automatic driving. BACKGROUND

[0002] Due to the differences in the manufacturing processes of different maps, the navigation routes based on different maps cannot be directly applied.

[0003] At present, in order to meet the navigation and path planning requirements of automatic driving equipment, sometimes it is necessary to convert the navigation route of a traditional navigation map to a navigation route of a high-precision map that can be used by automatic driving equipment. SUMMARY

[0004] The present disclosure provides a navigation route processing method and device for an automatic driving equipment, an electronic device, and a storage medium.

[0005] According to an aspect of the present disclosure, a navigation route processing method for an automatic driving equipment is provided, comprising:

[0006] obtaining a road-based navigation route in a navigation map according to an endpoint of the automatic driving equipment;

[0007] 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;

[0008] obtaining a lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, so as to control the automatic driving equipment to travel.

[0009] According to another aspect of the present disclosure, a navigation route processing device for an automatic driving equipment is provided, comprising:

[0010] an obtaining unit configured to obtain a road-based navigation route in a navigation map according to an endpoint of the automatic driving equipment;

[0011] a matching unit 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;

[0012] a control unit configured to obtain a lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, so as to control the automatic driving equipment to travel.

[0013] According to still another aspect of the present disclosure, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the aspects and any possible implementation thereof as described above.

[0017] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of the aspects and any possible implementation thereof as described above is provided.

[0018] According to yet another aspect of the present disclosure, a computer program product comprising a computer program which, when executed by a processor, implements the method of the aspects and any possible implementation thereof as described above is provided.

[0019] According to yet another aspect of the present disclosure, an autonomous vehicle comprising the electronic device as described above is provided.

[0020] According to the above technical solution, the present disclosure can obtain a road-based navigation route in a high-precision map according to the driving endpoint of the autonomous driving device, and then 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 autonomous driving device to drive. Since the lane-based navigation route for controlling the autonomous driving device to drive is obtained 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, a more real-time and accurate lane-based navigation route for controlling the autonomous driving device to drive can be obtained, thereby ensuring the reliability of the lane-level navigation route serving the autonomous driving device.

[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings are used to better understand the present solution and do not limit the present disclosure. Among them:

[0023] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram of the principle of a navigation route processing method of an automatic driving device according to a second embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram according to a third embodiment of the present disclosure;

[0027] Figure 5 is a block diagram of an electronic device for implementing a navigation route processing method of an automatic driving device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are set forth to assist in the understanding of the present disclosure. It will be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of brevity and clarity, descriptions of well-known functions and constructions are omitted herein.

[0029] It is apparent that the described embodiments are merely a part of the embodiments of the present disclosure, and not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present disclosure.

[0030] It should be noted that the terminal device involved in the embodiments of the present disclosure can include, but is not limited to, a mobile phone, a personal digital assistant (PDA), a wireless handheld device, a tablet computer, and the like. The display device can include, but is not limited to, a personal computer, a television, and the like.

[0031] In addition, the term "and / or" herein is merely a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it.

[0032] The high-precision map is also called a high-resolution map, which is a map for automatic driving services. The absolute position accuracy of the high-precision map can be close to 1 meter, and the relative position accuracy can reach 10-20 centimeters. Unlike traditional navigation maps, the high-precision map can provide not only road-level navigation routes, but also lane-level navigation routes.

[0033] Due to the differences in the manufacturing processes of different maps, such as the inconsistency of road index identification and the inconsistency of road topological relations in different maps, the navigation routes of different maps cannot be directly applied.

[0034] In order to meet the navigation needs of the automatic driving device, sometimes it is necessary to convert the navigation route of the traditional navigation map to the navigation route of the high-precision map that can be used by the automatic driving device.

[0035] At present, the scheme for obtaining the navigation route of the high-precision map mainly establishes a matching table of the navigation map and the high-precision map, and uses the matching table for indexing to obtain the navigation route required by the automatic driving device.

[0036] However, the navigation route of the high-precision map obtained by the related technology has the problems of poor accuracy and fault tolerance, which may sometimes cause the automatic driving device to exit the automatic driving state.

[0037] Therefore, it is urgent to provide a navigation route processing method for an automatic driving device, which can obtain a real-time and effective lane-level navigation route to ensure the reliability of the navigation route for the automatic driving device.

[0038] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure, as Figure 1 shown.

[0039] 101. Obtain a road-based navigation route in a navigation map according to a driving endpoint of an automatic driving device.

[0040] 102. 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.

[0041] 103. Obtain a lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, to control the automatic driving device to drive.

[0042] It should be noted that the driving endpoint of the automatic driving device can include a starting point and a target point. The road-based navigation route in the navigation map can be a road-level navigation route between the starting point and the target point in the navigation map.

[0043] It should be noted that the lane-based navigation route can be a lane-level navigation route in the high-precision map.

[0044] It should be noted that part or all of the execution subject of 101-103 can be an application located at the local terminal, or can also be a functional unit such as a plug-in or a software development kit (Software Development Kit, SDK) arranged in the application located at the local terminal, or can also be a processing engine located in a network side server, or can also be a distributed system located at the network side, for example, a processing engine or a distributed system in an automatic driving processing platform at the network side, and the embodiment is not particularly limited.

[0045] It can be understood that the application can be a native program (nativeApp) installed on the local terminal, or can also be a web program (webApp) of a browser on the local terminal, and the embodiment is not limited.

[0046] 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 according to the road-based navigation route in the navigation map by using a 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, 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 matched with the road-based navigation route in the navigation map by using the preset matching model and the lane data, a more real-time and accurate lane-based navigation route for controlling the automatic driving device to drive can be obtained, thereby ensuring the reliability of the lane-level navigation route serving the automatic driving device.

[0047] Optionally, in one possible implementation of the embodiment, in 102, the road connectivity data in the high-precision map can be obtained, and then the road-based navigation route in the high-precision map can be obtained according to the road-based navigation route in the navigation map and the road connectivity data by using a preset matching model.

[0048] In this implementation, the road-based navigation route in the navigation map can include but is not limited to trajectory data.

[0049] The road connectivity data in the high-precision map can include road topological relationship and road positioning data. The road positioning data can include but is not limited to road coordinate data and road turning angle data.

[0050] In this implementation, the preset matching model can include but is not limited to a hidden Markov model and a neural network sequence model.

[0051] 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 a hidden Markov model, and the road-based navigation route in the high-precision map can be output.

[0052] In one specific implementation process of the implementation mode, a preset matching model can be used to obtain a matching probability of the trajectory data and the road connectivity data, and then the road-based navigation route in the high-precision map can be obtained according to the matching probability and a preset probability threshold.

[0053] In the 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, the matching probability of the trajectory data and the road connectivity data can be 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 taken as the output road-based navigation route in the high-precision map.

[0054] It can be understood that the preset probability threshold can be a probability threshold determined according to actual business requirements.

[0055] In another specific implementation process of the implementation mode, a preset matching model can 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 to the high-precision map, and obtain the road-based navigation route in the high-precision map.

[0056] Specifically, the road-based navigation route in the navigation map can further include point of interest (POI) data of the navigation map, and then the obtained road-based navigation route in the high-precision map can include point of interest data of the high-precision map corresponding to the point of interest data of the navigation map, that is, can include the point of interest data obtained by mapping the point of interest data of the navigation map to the high-precision map.

[0057] In this way, the matching probability of the trajectory data and the road connectivity data can be obtained by using a preset matching model, and then the road-based navigation route in the high-precision map can be obtained according to the matching probability and a preset probability threshold. Since the preset matching model is used to perform matching analysis on the trajectory data and the road connectivity data, a more accurate road-based navigation route in the high-precision map can be obtained, and the accuracy of the obtained road-based navigation route in the high-precision map is further improved, so that a more accurate and effective lane-based navigation route can be obtained based on the road-based navigation route in the high-precision map in subsequent processes.

[0058] In another specific implementation process of the implementation, in the process of acquiring the road connectivity data in the high-precision map, the road line 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 line data.

[0059] In this specific implementation process, the road line data between the driving endpoints of the autonomous driving device in the high-precision map can be acquired according to the driving endpoints of the autonomous driving device. The road line data can include road baseline data and road center line data. The road baseline data can be based on the leftmost road reference line data of the road.

[0060] In this 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.

[0061] In another specific implementation process of the implementation, the road center line 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 center line data.

[0062] In this way, by acquiring the road connectivity data in the high-precision map according to the acquired 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-based navigation route in the high-precision map can be more accurately obtained based on the road connectivity data in the subsequent process.

[0063] In another specific implementation process of the implementation, the road-based navigation route in the navigation map within a preset distance and the road connectivity data in the high-precision map within the preset distance are acquired according to the position data of the autonomous driving device, the road-based navigation route in the navigation map, and the road connectivity data in the high-precision map. Then, the road-based navigation route in the high-precision map within the preset distance is obtained by using a preset matching model according to the road-based navigation route within the preset distance and the road connectivity data within the preset distance.

[0064] In this specific implementation process, the preset distance can be a preset distance in front of the current position of the autonomous driving device.

[0065] For example, the preset distance can 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 in front of the starting point of the autonomous driving device can be acquired, and the road connectivity data in the high-precision map within 5 kilometers in front of the starting point of the autonomous driving device can be acquired.

[0066] It can be understood that the preset distance can be less than the total length of any navigation route. That is, based on any navigation route, a navigation route of a preset distance can be extracted. Any navigation route can include multiple navigation routes of a preset distance. Thus, based on the position data of the autonomous driving device, the road-based navigation route in the navigation map, and the preset distance, the road-based navigation route in the high-precision map within the preset distance can be dynamically obtained.

[0067] For example, during the control of the driving of the autonomous 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 high-precision map within the next distance can be dynamically predicted according to the preset distance.

[0068] In this way, 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 the hardware requirement can be reduced, thereby the processing efficiency can be optimized.

[0069] In another specific implementation process of the implementation mode, the road-based navigation route in the navigation map can include trajectory data. First, the trajectory data of the road-based navigation route in the navigation map can be further obtained. Then, the trajectory data is subjected to data thinning processing to obtain processed trajectory data. Again, the road connectivity data in the high-precision map can be obtained. Finally, based on the processed trajectory data and the road connectivity data, the road-based navigation route in the high-precision map can be obtained by using a preset matching model.

[0070] In this way, in the implementation mode, the road-based navigation route in the high-precision map can be obtained by using a preset matching model based on the road-based navigation route in the navigation map and the obtained road connectivity data in the high-precision map. Since the road-based navigation route in the high-precision map is obtained by using the preset matching model to match and fuse the road-based navigation route in the navigation map and the obtained road connectivity data, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate road-based navigation route in the high-precision map can be obtained subsequently.

[0071] It should be noted that the various specific implementation processes for obtaining the road-based navigation route in the high-precision map provided in the implementation mode can be combined with each other to implement the navigation route processing method of the autonomous driving device of the embodiment. For details, please refer to the related content in the implementation mode, which will not be described here.

[0072] Optionally, in one possible implementation of the embodiment, the lane data can 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 route in the high-precision map can be obtained by using a preset search algorithm according to the lane connectivity data, and then the at least one lane-based navigation route can be filtered according to the geo-fence data to obtain the lane-based navigation route.

[0073] In this implementation, the geo-fence data is lane-level geo-fence data. The geo-fence data can be area data in which the automatic driving device is not allowed to travel. The geo-fence data can be static data related to the lane state based on road state prediction.

[0074] For example, the geo-fence data can include, but is not limited to, curvature overrun, slope overrun, broken road, toll station, etc. Among them, the curvature overrun and the slope overrun can mean that the curvature and the slope of the lane exceed the threshold values of the curvature and the slope that the automatic driving device can travel.

[0075] In this implementation, the lane connectivity data can be lane-level connectivity relationship, lane line solid-void situation, and lane positioning data. Among them, the lane-level connectivity relationship can be a lane-level topological relationship.

[0076] In one specific implementation process of this implementation, the preset search algorithm can include, but is not limited to, a depth search algorithm. Specifically, at least one lane-based navigation route in the road-based navigation route in the high-precision map can be obtained by using a depth search algorithm according to the lane connectivity data, and then the at least one lane-based navigation route can be filtered according to the geo-fence data to obtain the lane-based navigation route.

[0077] In this specific implementation process, the at least one lane-based navigation route can be filtered according to the geo-fence data and a preset condition to obtain the lane-based navigation route.

[0078] Specifically, the preset condition can include that the lane in the lane-based navigation route does not include the geo-fence data.

[0079] In one case of this specific implementation process, it can be judged 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 taken as the lane-based navigation route provided to the automatic driving device.

[0080] It can be understood that the lane map fence data can be obtained through a high-precision map or obtained from a cloud server. In this implementation manner, no specific limitation is made.

[0081] In this implementation manner, at least one lane-based navigation route in the road-based navigation route in the high-precision map can be obtained by using a preset search algorithm, and the lane-based navigation route provided to the automatic driving device can be filtered based on the geographic fence data. Therefore, 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.

[0082] It should be noted that, based on the implementation manner of obtaining the lane-based navigation route provided in this implementation manner, the navigation route processing method of the automatic driving device of this embodiment can be implemented in combination with the various specific implementation processes of obtaining the road-based navigation route in the high-precision map provided in the foregoing implementation manners. For details, refer to the related content in the foregoing implementation manners, which will not be described here again.

[0083] Optionally, in one possible implementation manner of this embodiment, after 103, the lane dynamic data in the lane-based navigation route can be further obtained, and then the filtered lane-based navigation route can be obtained according to the lane dynamic data and a preset filtering strategy.

[0084] In this implementation manner, the lane dynamic data can include but is not limited to lane-level events and real-time road conditions.

[0085] In this implementation manner, the preset filtering strategy can include: if the lane dynamic data does not match the preset dynamic event, filtering the lane-based navigation route in which the lane dynamic data does not match the preset dynamic event from the lane-based navigation route as the filtered lane-based navigation route.

[0086] Specifically, the preset dynamic event can include a preset lane-level event and a road condition.

[0087] Specifically, the lane-level event can include but is not limited to dynamic event information such as a traffic accident event, a congestion event, a regulation event, a construction event, and a weather event on the lane.

[0088] Specifically, the preset road condition can include but is not limited to a driving condition on the road surface, a lane speed limit condition obtained based on historical driving data, and other road surface conditions.

[0089] It can be understood that the lane speed limit condition obtained based on the historical driving data, i.e., the lane speed limit condition based on experience, can not be the highest lane speed limit specified when the lane is designed.

[0090] In one specific implementation process of the implementation mode, it can be judged whether the lane dynamic data in the lane-based navigation route matches the preset dynamic event, i.e., 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 taken as the lane-based navigation route provided to the automatic driving device.

[0091] It can be understood that, since the traffic condition of the lane is real-time changing, the obtained lane-based navigation route can be further filtered by using the obtained real-time lane dynamic data.

[0092] In this way, in the implementation mode, the filtered lane-based navigation route can be obtained according to the lane dynamic data in the obtained lane-based navigation route and the preset filtering strategy, the further filtering of the obtained lane-based navigation route can be realized, the lane-based navigation route with higher timeliness can be obtained, and thus the timeliness and reliability of the lane-based navigation route used for controlling the driving of the automatic driving vehicle are improved.

[0093] It should be noted that, based on the implementation mode of filtering the obtained lane-based navigation route provided in the implementation mode, the navigation route processing method of the automatic driving device of the present embodiment can be realized in combination with the various specific implementation processes of filtering the obtained lane-based navigation route provided in the foregoing implementation modes. For details, please refer to the related content in the foregoing implementation modes, which will not be described here.

[0094] In the present 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 road-based navigation route in the high-precision map can be obtained 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, to control the driving of the automatic driving device. Since the lane-based navigation route is obtained by using 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, a more real-time and accurate lane-based navigation route used for controlling the driving of the automatic driving device can be obtained, and thus the reliability of the lane-level navigation route serving the automatic driving device is ensured.

[0095] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by matching the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model. Since the road-based navigation route in the high-precision map is obtained by matching and fusing the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0096] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by matching the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model. Since the road-based navigation route in the high-precision map is obtained by matching and fusing the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0097] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by matching the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model. Since the road-based navigation route in the high-precision map is obtained by matching and fusing the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0098] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by matching the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model. Since the road-based navigation route in the high-precision map is obtained by matching and fusing the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0099] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by matching the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model. Since the road-based navigation route in the high-precision map is obtained by matching and fusing the road-based navigation route in the navigation map and the obtained road connectivity data based on the preset matching model, the accuracy of the obtained road-based navigation route in the high-precision map can be further improved, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0100] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure, as Figure 2 shown.

[0101] 201. Obtain a road-based navigation route in a navigation map according to a driving endpoint of an autonomous driving device.

[0102] In this embodiment, the driving endpoint of the autonomous driving device can include a starting point and a target point. The road-based navigation route in the navigation map can be a road-level navigation route between the starting point and the target point in the navigation map.

[0103] 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.

[0104] In this embodiment, first, trajectory data according to the road-based navigation route in the navigation map can be obtained according to the road-based navigation route in the navigation map. Then, the trajectory data is subjected to data thinning processing to obtain processed trajectory data.

[0105] 203. Obtain road connectivity data in a high-precision map.

[0106] In this embodiment, road line data in the high-precision map can be obtained, and then road connectivity data in the high-precision map can be obtained according to the road line data.

[0107] Specifically, road line data between the driving endpoints of the autonomous driving device in the high-precision map can be obtained according to the driving endpoints of the autonomous driving device. The road line data can include road baseline data and road center line data. The road baseline data can be based on the leftmost road reference line data.

[0108] 204. Obtain a road-based navigation route in a high-precision map using a hidden Markov model according to trajectory data of the road-based navigation route in the navigation map and road connectivity data in the high-precision map.

[0109] Specifically, the road-based navigation route in the navigation map can also include POI data of the navigation map, and then the obtained road-based navigation route in the high-precision map can include interest point data of the high-precision map corresponding to the interest point data of the navigation map.

[0110] 205. Obtain lane connectivity data and geofencing data in a high-precision map.

[0111] 206. Obtain at least one lane-based navigation route in the road-based navigation route in the high-precision map using a preset search algorithm according to the lane connectivity data.

[0112] 207、screen the at least one lane-based navigation route according to the geo-fence data and the lane connectivity data to obtain a lane-based navigation route.

[0113] At this point, the obtained lane-based navigation route can be used to control the autonomous driving device to travel.

[0114] It can be understood that, since the traffic situation of the lane is real-time changing, after the lane-based navigation route is obtained, the obtained lane-based navigation route can be further screened by using the obtained real-time lane dynamic data and the preset screening strategy, to obtain a screened lane-based navigation route.

[0115] Figure 3 is a schematic diagram of the principle of the navigation route processing method of the autonomous driving device according to the second embodiment of the present disclosure. As shown in Figure 3 in the implementation process of the present embodiment, first, the lane-based navigation route 301 in the navigation map can be obtained in real time according to the travel endpoint of the autonomous driving device. Then, the trajectory data 302 of the navigation route is dynamically obtained, for example, the trajectory data within 5 kilometers in front of the current travel position of the autonomous driving device on the lane-based navigation route in the navigation map is dynamically obtained, and the trajectory data 302 of the navigation route is thinned to obtain the thinned trajectory data 303. At the same time, the high-precision map 304 can be obtained, the baseline 305 is extracted based on the high-precision map 304, the road baseline in the high-precision map 304 is extracted, and then the road connectivity data 306 is obtained according to the road baseline in the high-precision map 304. Again, the thinned trajectory data 303 and the road connectivity data 306 are input into the preset hidden Markov model M 307 (HMM), to obtain the lane-based navigation route 308 in the high-precision map in real time. Again, the lane-based navigation route 311 is extracted according to the lane-based navigation route 308 in the high-precision map, and the lane connectivity data 309 and the geo-fence data 310 obtained based on the high-precision map 304. Finally, the lane-based navigation route is output in real time for controlling the autonomous driving device to travel.

[0116] In addition, in the process of matching the lane-based navigation route in the navigation map and the road connectivity data in the high-precision map, if the lane-based navigation route in the navigation map is normal, but the road connectivity data of part of the road section in the high-precision map has a data missing problem, that is, there is a problem road section, then the autonomous driving device traveling to the problem road section can be manually taken over until the autonomous driving device travels through the problem road section, and then the autonomous driving device is controlled to continue autonomous driving based on the output lane-based navigation route.

[0117] In this embodiment, the lane-based navigation route can be obtained based on 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, and a lane-based navigation route used for more real-time and accurate control of the driving of the automatic driving device can be obtained, thereby ensuring the reliability of the lane-level navigation route for the automatic driving device.

[0118] In addition, the technical solution provided in this embodiment can effectively combine the advantages of the navigation map POI and the lane information of the high-precision map to realize lane-level navigation route planning for the automatic driving device.

[0119] In addition, the technical solution provided in this embodiment can further improve the user experience of automatic driving by performing automatic driving based on the more real-time and reliable lane-level navigation route.

[0120] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited to the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for the present disclosure.

[0121] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0122] Figure 4 is a schematic diagram according to the third embodiment of the present disclosure, as Figure 4 shown. The navigation route processing device 400 of the automatic driving device in this embodiment can include an obtaining unit 401, a matching unit 402, and a control unit 403. The obtaining unit 401 is configured to obtain a road-based navigation route in a navigation map according to a driving endpoint of an automatic driving device. The matching unit 402 is 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. The control unit 403 is configured to obtain a lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, so as to control the automatic driving device to drive.

[0123] It should be noted that part or all of the navigation route processing apparatus of the automatic driving device in this embodiment can be an application located at the local terminal, or can also be a plug-in or a software development kit (SDK) and the like functional unit arranged in the application located at the local terminal, or can also be a processing engine located in a network side server, or can also be a distributed system located at the network side, for example, a processing engine or a distributed system in an automatic driving processing platform at the network side, and the like, which are not particularly limited in this embodiment.

[0124] It can be understood that the application can be a native application (nativeApp) installed on the local terminal, or can also be a web application (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0125] Optionally, in a possible implementation manner of this embodiment, the matching unit 402 can be specifically configured to obtain road connectivity data in the high-precision map, and obtain the road-based navigation route in the high-precision map according to the road-based navigation route in the navigation map and the road connectivity data by using a preset matching model.

[0126] 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 can also be configured to obtain a matching probability of the trajectory data and the road connectivity data by using a preset matching model, and obtain the road-based navigation route in the high-precision map according to the matching probability and a preset probability threshold.

[0127] Optionally, in a possible implementation manner of this embodiment, the matching unit 402 can be specifically configured to obtain road line data in the high-precision map, and obtain the road connectivity data in the high-precision map according to the road line data.

[0128] Optionally, in a possible implementation manner of this embodiment, the lane data includes geographic fence data and lane connectivity data, and the control unit 403 can be specifically configured to obtain at least one lane-based navigation route in the road-based navigation route in the high-precision map according to the lane connectivity data by using a preset search algorithm, and screen the at least one lane-based navigation route according to the geographic fence data to obtain the lane-based navigation route.

[0129] Optionally, in a possible implementation manner of the embodiment, the control unit 403 can further be configured to acquire lane dynamic data in the lane-based navigation route, and obtain a filtered lane-based navigation route according to the lane dynamic data and a preset filtering strategy.

[0130] In the 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 high-precision map by using a preset matching model according to the road-based navigation route in the navigation map, so that the control unit can 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, 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 matched with the road-based navigation route in the navigation map by using the preset matching model, and the lane data, a lane-based navigation route that is more real-time and accurate for controlling the automatic driving device to drive can be obtained, thereby ensuring the reliability of the lane-level navigation route serving the automatic driving device.

[0131] In addition, by using the technical solution provided in the embodiment, the road-based navigation route in the high-precision map can be obtained by using a preset matching model to match and fuse the road-based navigation route in the navigation map and the obtained road connectivity data, which can further improve the accuracy of the obtained road-based navigation route in the high-precision map, so that a more accurate lane-based navigation route in the high-precision map can be obtained subsequently.

[0132] In addition, by using the technical solution provided in the 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 road-based navigation route in the high-precision map can be obtained according to the matching probability and a preset probability threshold. Since the trajectory data and the road connectivity data are matched and analyzed by using the preset matching model, a more accurate road-based navigation route in the high-precision map can be obtained, which further improves the accuracy of the obtained road-based navigation route in the high-precision map, so that a more accurate and effective lane-based navigation route can be obtained based on the road-based navigation route in the high-precision map subsequently.

[0133] In addition, by using the technical solution provided in the embodiment, the road connectivity data in the high-precision map can be obtained according to the road line data in the acquired high-precision map, and the road connectivity data in the high-precision map can be accurately obtained, so that the road-based navigation route in the high-precision map can be obtained, and a more accurate road-based navigation route in the high-precision map can be obtained.

[0134] In addition, by using the technical solution provided in the embodiment, at least one lane-based navigation route in the road-based navigation route in the high-precision map can be obtained by using the preset search algorithm, and the lane-based navigation route provided to the automatic driving device can be obtained based on the geographic fence data. Therefore, the accuracy of the obtained lane-based navigation route can be further improved, and the reliability of the lane-level navigation route serving the automatic driving device can be further improved.

[0135] In addition, by using the technical solution provided in the embodiment, the lane-based navigation route after screening can be obtained according to the lane dynamic data in the acquired lane-based navigation route and the preset screening strategy, the obtained lane-based navigation route can be further screened, the lane-based navigation route with higher timeliness can be obtained, and the timeliness and reliability of the lane-based navigation route used to control the driving of the automatic driving vehicle can be improved.

[0136] In addition, by using the technical solution provided in the embodiment, the lane-level navigation route planning for the automatic driving device can be effectively combined with the advantages of the navigation map POI and the lane information in the high-precision map.

[0137] In addition, by using the technical solution provided in the embodiment, the user experience of automatic driving can be further improved by automatic driving based on the more real-time and reliable lane-level navigation route.

[0138] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0139] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0140] Further, the present disclosure further provides an automatic driving vehicle comprising the provided electronic device. The automatic driving vehicle can be an L3 / L4 level automatic driving vehicle.

[0141] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0142] like Figure 5 As shown, 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. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0143] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] The computing unit 501 can be various general and / or special purpose processing components with 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 specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the navigation route processing method of the autonomous driving device. For example, in some embodiments, the navigation route processing method of the autonomous driving device can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can 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 onto the RAM 503 and executed by the computing unit 501, one or more steps of the navigation route processing method of the autonomous driving device described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the navigation route processing method of the autonomous driving device by any other appropriate means, such as by means of firmware.

[0145] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0146] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0147] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0148] 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 the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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 can be received in any form, including acoustic, speech, or tactile input.

[0149] 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 a local area network (LAN), a wide area network (WAN), and the Internet.

[0150] The computer system can 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 can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0151] It should be understood that the various forms of flow shown above can be re-ordered, added to, or have steps deleted, using the steps. For example, the steps described in the present disclosure can be executed in parallel, in series, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which are not limited herein.

[0152] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A navigation route processing method of an autonomous driving device, comprising: obtaining a road-based navigation route in a navigation map according to a driving endpoint of the autonomous driving device; 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; obtaining a lane-based navigation route according to the road-based navigation route in the high-precision map and lane data in the high-precision map, wherein the lane data comprises geofence data and lane connectivity data, so as to control the autonomous driving device to drive from a starting location to a target location according to the lane-based navigation route; wherein the obtaining of 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 comprises: obtaining at least one lane-based navigation route in the road-based navigation route in the high-precision map by using a preset search algorithm according to the lane connectivity data; screening the at least one lane-based navigation route according to the geofence data and a preset condition to obtain the lane-based navigation route; after the obtaining of 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, further comprising: obtaining lane dynamic data in the lane-based navigation route; obtaining a screened lane-based navigation route according to the lane dynamic data and a preset screening strategy.

2. The method of claim 1, wherein, the obtaining of 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 comprises: obtaining road connectivity data in the high-precision map; obtaining 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 and the road connectivity data.

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

5. 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 endpoint of the autonomous driving device; a matching unit 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; The control unit is configured to obtain a lane-based navigation route based on road-based navigation route in the high-precision map and lane data in the high-precision map, so as to control the automatic driving device to travel from a starting point to a target point according to the lane-based navigation route. The control unit is configured to obtain at least one lane-based navigation route based on the road-based navigation route in the high-precision map by using a preset search algorithm according to the lane connection data; screen the at least one lane-based navigation route based on the geographic fence data and a preset condition to obtain the lane-based navigation route. The control unit is further configured to obtain lane dynamic data in the lane-based navigation route; obtain a screened lane-based navigation route based on the lane dynamic data and a preset screening strategy.

6. The apparatus of claim 5, wherein, The matching unit is configured to obtain road connection data in the high-precision map; obtain the road-based navigation route in the high-precision map by using a preset matching model based on the road-based navigation route in the navigation map and the road connection data.

7. The apparatus of claim 6, wherein, The road-based navigation route in the navigation map includes trajectory data, and the matching unit is further configured to obtain a matching probability of the trajectory data and the road connection data by using a preset matching model; obtain the road-based navigation route in the high-precision map based on the matching probability and a preset probability threshold.

8. The apparatus of claim 6 or 7, wherein, The matching unit is configured to obtain road line data in the high-precision map; obtain the road connection data in the high-precision map based on the road line data.

9. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-4.

10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method according to any one of claims 1-4.

11. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-4.

12. An autonomous vehicle comprising the electronic device of claim 9.

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