Vehicle and map building and positioning method and device thereof

Abstract

The invention discloses a vehicle and a map building and positioning method and device thereof. The method comprises the following steps: acquiring a semantic map from a current parking lot to a target parking lot, a current positioning result, coordinate information of a vehicle track, course information of a vehicle and semantic information of traffic facilities; constructing a target semantic map in real time according to the coordinate information of the vehicle track, the course information of the vehicle and the semantic information of the traffic facilities; the initial position of the vehicle is determined according to the current positioning result, matching positioning is carried out on a semantic map from the current parking lot to the target parking lot and a target semantic map based on the initial position of the vehicle so as to determine the position of the vehicle; and performing positioning matching according to the target semantic map and a semantic map from the current parking lot to the target parking lot so as to update the vehicle position. According to the map building and positioning method, vehicle starting under the condition that no GNSS signal exists is supported, and the positioning problem under the condition that no high-precision map exists is solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle map construction and positioning method, a vehicle map construction and positioning device, and a vehicle. Background Art

[0002] In the related art, when a vehicle automatically drives from a current parking space to a target parking space, the current parking lot may be indoors. In the absence of a high-precision map or a GNSS signal, the vehicle cannot be located and started. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present application is to propose a method for mapping and positioning a vehicle, the method comprising: obtaining a semantic map from the current parking lot to the target parking lot, the current positioning result, the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities; constructing a target semantic map in real time according to the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities; determining the initial position of the vehicle according to the current positioning result, matching and positioning the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, positioning and matching the target semantic map with the semantic map from the current parking lot to the target parking lot to update the vehicle position. The map construction and positioning method of the present application supports starting the vehicle without a GNSS signal and solves the positioning problem without a high-precision map.

[0004] The second objective of the present application is to provide a vehicle map construction and positioning device.

[0005] The third object of the present application is to provide a vehicle.

[0006] To achieve the above-mentioned objectives, the first aspect of the present application proposes a method for mapping and positioning a vehicle, the method comprising: obtaining a semantic map from a current parking lot to a target parking lot, a current positioning result, coordinate information of a vehicle trajectory, vehicle heading information, and semantic information of traffic facilities; constructing a target semantic map in real time according to the coordinate information of the vehicle trajectory, the vehicle heading information, and the semantic information of traffic facilities; determining an initial position of the vehicle according to the current positioning result, matching and positioning the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the position of the vehicle, and when the vehicle position is not within the range of a high-precision map, matching the target semantic map with the semantic map from the current parking lot to the target parking lot to update the vehicle position.

[0007] According to one embodiment of the present application, the above method also includes: when the vehicle position is within the range of the high-precision map, positioning matching is performed based on the target semantic map and the high-precision map to update the vehicle position.

[0008] According to one embodiment of the present application, the above method also includes: when there is a GNSS signal when the coordinate information of the vehicle trajectory is generated based on dead reckoning, and / or when the vehicle position is within the range of the high-precision map during the construction of the target semantic map, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal and / or the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot.

[0009] According to one embodiment of the present application, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal to update the semantic map from the current parking lot to the target parking lot, including: obtaining a first correspondence between the coordinate information of the vehicle trajectory corresponding to the GNSS signal and the coordinate information of the vehicle trajectory generated based on dead reckoning, and an accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal; fusing the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning according to the first correspondence and the accuracy value to obtain a first fusion result, and correcting the target semantic map according to the first fusion result to update the semantic map from the current parking lot to the target parking lot.

[0010] According to one embodiment of the present application, the target semantic map is post-processed according to the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot, including: obtaining a second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map; fusing the semantic information of the traffic facilities in the high-precision map with the semantic information of the traffic facilities in the target semantic map according to the second correspondence to obtain a second fusion result, and correcting the target semantic map according to the second fusion result to update the semantic map from the current parking lot to the target parking lot.

[0011] According to an embodiment of the present application, obtaining a first correspondence between coordinate information of a vehicle trajectory corresponding to a GNSS signal and coordinate information of a vehicle trajectory generated based on dead reckoning includes: when the coordinate information of the vehicle trajectory is generated based on dead reckoning, in the presence of a GNSS signal, obtaining the coordinate information of the vehicle trajectory corresponding to the GNSS signal; and binding the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning to determine the first correspondence.

[0012] According to one embodiment of the present application, obtaining a second correspondence between the semantic information of traffic facilities in a high-precision map and the semantic information of traffic facilities in a target semantic map includes: in the process of constructing a target semantic map, when the vehicle position is within the range of the high-precision map, obtaining the semantic information of traffic facilities in the high-precision map; binding the semantic information of traffic facilities in the high-precision map with the semantic information of traffic facilities in the target semantic map to determine the second correspondence.

[0013] According to one embodiment of the present application, a target semantic map is constructed based on the coordinate information of the vehicle trajectory, the heading information of the vehicle and the semantic information of the traffic facilities, including: unifying the semantic information of the traffic facilities, the heading information of the vehicle and the coordinate information of the vehicle trajectory into the same coordinate system; determining the vehicle trajectory based on the coordinate information of the vehicle trajectory and the heading information of the vehicle; mapping the vehicle trajectory and the semantic information of the traffic facilities into a preset map framework to determine the target semantic map.

[0014] To achieve the above-mentioned purpose, the second aspect of the present application proposes a vehicle map construction and positioning device, which includes: an acquisition module, which is used to obtain a semantic map from the current parking lot to the target parking lot, a current positioning result, coordinate information of the vehicle trajectory, vehicle heading information and semantic information of traffic facilities; a mapping module, which is used to construct a target semantic map in real time according to the coordinate information of the vehicle trajectory, the vehicle heading information and the semantic information of traffic facilities; a positioning module, which is used to determine the initial position of the vehicle according to the current positioning result, match and locate the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, the target semantic map is positioned and matched with the semantic map from the current parking lot to the target parking lot to update the vehicle position.

[0015] To achieve the above-mentioned objectives, the third aspect embodiment of the present application proposes a vehicle, including a memory, a processor, and a vehicle map building and positioning program stored in the memory and executable on the processor. When the processor executes the vehicle map building and positioning program, the aforementioned vehicle map building and positioning method is implemented.

[0016] According to the vehicle and its map construction and positioning method and device of the embodiment of the present application, a semantic map from the current parking lot to the target parking lot, the current positioning result, the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities are obtained; a target semantic map is constructed in real time according to the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities; the initial position of the vehicle is determined according to the current positioning result, and the semantic map from the current parking lot to the target parking lot is matched and positioned with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, positioning matching is performed based on the target semantic map and the semantic map from the current parking lot to the target parking lot to update the vehicle position. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for mapping and positioning a vehicle according to some embodiments of the present application;

[0018] Figure 2 A block diagram of a vehicle map construction and positioning device according to some embodiments of the present application;

[0019] Figure 3 It is a block diagram of a vehicle according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0021] The vehicle and its map construction and positioning method and device according to the embodiment of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 Flowchart of a vehicle map construction and positioning method according to some embodiments of the present application. Figure 1 The vehicle map construction and positioning method of the embodiment of the present application may include the following steps:

[0023] S110, obtaining a semantic map from the current parking lot to the target parking lot, a current positioning result, coordinate information of the vehicle trajectory, vehicle heading information, and semantic information of traffic facilities.

[0024] Specifically, when the user needs to control the vehicle to automatically drive from the current parking lot to the target parking lot, the vehicle will obtain the ID of the current parking lot in the map record file after being powered on in the current parking lot. Then, after reading this ID, multiple semantic maps including the current parking lot will be determined. The user can select the semantic map from the current parking lot to the target parking lot from the multiple semantic maps including the current parking lot.

[0025] The vehicle will store the current positioning result before entering the current parking lot and powering off, and the current positioning result can be read after the vehicle is powered on.

[0026] Before the vehicle enters the current parking lot and powers off, the dead reckoning system will save the coordinate information of the current vehicle trajectory and the vehicle heading information, and load the saved coordinate information of the vehicle trajectory and the vehicle heading information when the vehicle is powered on, and continue to perform dead reckoning on this basis to obtain the coordinate information of the vehicle trajectory and the vehicle heading information.

[0027] When the vehicle is powered on, the vehicle's visual perception system will collect semantic information of traffic facilities, such as lane lines, ground arrows, parking spaces, speed bumps, etc. The semantic information includes but is not limited to coordinate information and direction information of the traffic facilities.

[0028] S120, constructing a target semantic map in real time according to the coordinate information of the vehicle trajectory, the heading information of the vehicle and the semantic information of the traffic facilities.

[0029] Specifically, after obtaining the coordinate information of the vehicle trajectory, the vehicle's heading information, and the semantic information of the traffic facilities to construct the target semantic map in real time, the vehicle's trajectory is determined based on the coordinate information of the vehicle trajectory and the vehicle's heading information, and the traffic facilities are determined based on the semantic information of the traffic facilities, such as lane lines, ground arrows, parking spaces, and speed bumps. After fusing the repeated information, the vehicle's trajectory and traffic facilities are mapped to the preset map framework to construct the target semantic map. Furthermore, during the vehicle's driving process, the coordinate information of the vehicle trajectory, the vehicle's heading information, and the semantic information of the traffic facilities are obtained in real time, and the target semantic map is constructed in real time based on the information obtained in real time.

[0030] S130, determine the initial position of the vehicle based on the current positioning result, match and locate the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, match the target semantic map with the semantic map from the current parking lot to the target parking lot to update the vehicle position.

[0031] Specifically, after the vehicle is powered on, it can read the current positioning result and use the current positioning result as the initial position of the vehicle. The real-time constructed target semantic map and the semantic map from the current parking lot to the target parking lot are used for matching positioning. The initial position of the vehicle will allow the positioning initialization to succeed directly, and determine the position of the vehicle in the semantic map from the current parking lot to the target parking lot. That is to say, when the current parking lot cannot receive GNSS signals, the positioning result saved when the vehicle was last powered off can also be used for positioning initialization to successfully start the vehicle. The initial positioning value accuracy requirement is not high and does not need to be uploaded to the server. In addition, during the driving process of the vehicle, if it is detected that the vehicle position is not within the range of the high-precision map, the semantic map from the current parking lot to the target parking lot can be used to locate the vehicle. For example, the real-time constructed target semantic map is matched with the semantic map from the current parking lot to the target parking lot to update the vehicle position to ensure that the vehicle can be planned and automatically driven to the target parking lot.

[0032] The map construction and positioning method of the present application supports starting the vehicle without a GNSS signal and solves the positioning problem without a high-precision map.

[0033] In some embodiments, the above method also includes: when the vehicle position is within the range of the high-precision map, positioning matching is performed based on the target semantic map and the high-precision map to update the vehicle position.

[0034] Specifically, during vehicle driving, if the vehicle's position is detected to be within the range of a high-precision map, the high-precision map can be used to locate the vehicle. For example, the target semantic map constructed in real time can be positioned and matched with the high-precision map to update the vehicle's position. This can improve positioning accuracy.

[0035] In some embodiments, the above method also includes: when there is a GNSS signal when the coordinate information of the vehicle trajectory is generated based on dead reckoning, and / or when the vehicle position is within the range of the high-precision map during the construction of the target semantic map, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal and / or the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot.

[0036] Specifically, when the vehicle is powered on, regardless of the scenario, a target semantic map will be constructed in real time, that is, during the process of the vehicle automatically driving from the current parking lot to the target parking lot, a target semantic map will also be constructed in real time, and after the vehicle is powered off at the target parking lot, the target semantic map will be post-processed. For example, the target semantic map is corrected according to the coordinate information of the vehicle trajectory corresponding to the GNSS (Global Navigation Satellite System) signal, the semantic information of the traffic facilities in the high-precision map, or the coordinate information of the vehicle trajectory corresponding to the GNSS signal and the semantic information of the traffic facilities in the high-precision map to generate a new semantic map from the current parking lot to the target parking lot, and the new semantic map from the current parking lot to the target parking lot is fused with the original semantic map from the current parking lot to the target parking lot, and the fused semantic map is stored as the semantic map from the current parking lot to the target parking lot in the map record file, and the map is saved in the high-precision map standard format after processing, and the start and end point information of the current map, including the start and end point coordinate information, the parking lot entrance coordinate information and the semantic map ID from the current parking lot to the target parking lot is saved in the map record file, and used the next time the user needs to control the vehicle to automatically drive from the current parking lot to the target parking lot.

[0037] In addition, post-processing the target semantic map also includes obtaining CNO (Carrier-to-Noise Ratio) according to the vehicle-mounted GPS receiver, and judging whether the target parking lot is an indoor parking lot according to CNO. For example, if CNO is less than a CNO threshold, the target parking lot is determined to be an indoor parking lot. If the target parking lot is an indoor parking lot, the indoor parking lot is divided into floors to facilitate users to determine the location of their own vehicles.

[0038] Post-processing of the target semantic map also includes retaining a certain length of map data, such as 1 km, for maps that were not actively constructed by the user before entering the current parking lot, and deleting map data that is farther away. Specifically, when the vehicle is powered on, the target semantic map will be constructed in real time regardless of the scenario. However, in order to obtain a semantic map from the current parking lot to the target parking lot, the user can actively construct and save the semantic map from the current parking lot to the target parking lot by clicking a preset button on the vehicle terminal device, and delete the map that was not actively constructed by the user before the current parking lot.

[0039] Post-processing the target semantic map also includes, if a closed loop is detected in the target semantic map, performing closed-loop fusion and deleting the track formed during the parking process to optimize the target semantic map.

[0040] Post-processing of the target semantic map also includes, if the number of maps saved by the user is greater than one, checking whether the coordinates of the tracks in multiple maps overlap. If there is overlap and the overlap ratio exceeds a certain threshold, the map fusion operation is performed to merge the two maps into one, and the same operation is continued until no overlapping maps are found. If the maps contain the same parking lot, the areas of the same parking lot in the two maps are matched. If the matching area exceeds a certain ratio, the maps are fused. After the fusion is completed, the map is saved and the two old maps are deleted.

[0041] In some embodiments, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal to update the semantic map from the current parking lot to the target parking lot, including: obtaining a first correspondence between the coordinate information of the vehicle trajectory corresponding to the GNSS signal and the coordinate information of the vehicle trajectory generated based on dead reckoning, and an accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal; fusing the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning according to the first correspondence and the accuracy value to obtain a first fusion result, and correcting the target semantic map according to the first fusion result to update the semantic map from the current parking lot to the target parking lot.

[0042] Specifically, when performing dead reckoning to generate the coordinate information of the vehicle trajectory, there may be a GNSS signal, and the coordinate information of the vehicle trajectory corresponding to the GNSS signal can also be obtained according to the GNSS signal. Therefore, in order to improve the accuracy of the semantic map from the current parking lot to the target parking lot, when the vehicle arrives at the target parking lot and powers off, the first correspondence between the coordinate information of the vehicle trajectory corresponding to the GNSS signal and the coordinate information of the vehicle trajectory generated based on the dead reckoning is obtained, and the coordinate information of the vehicle trajectory corresponding to the GNSS signal is fused with the coordinate information of the vehicle trajectory generated based on the dead reckoning according to the first correspondence. However, when the GNSS signal is relatively weak, the accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is relatively low, and when the GNSS signal is relatively strong, the accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is relatively high. Therefore, when the coordinate information of the vehicle trajectory corresponding to the GNSS signal is fused with the coordinate information of the vehicle trajectory generated based on the dead reckoning according to the first correspondence to obtain the first fusion result, the accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal needs to be considered, and then the target semantic map is corrected according to the first fusion result to update the semantic map from the current parking lot to the target parking lot.

[0043] Exemplarily, when the accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is greater than the preset accuracy threshold, the weight of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is increased during fusion, and the weight of the coordinate information of the vehicle trajectory generated based on dead reckoning is reduced; when the accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is less than or equal to the preset accuracy threshold, the weight of the coordinate information of the vehicle trajectory corresponding to the GNSS signal is reduced during fusion, and the weight of the coordinate information of the vehicle trajectory generated based on dead reckoning is increased. The preset accuracy threshold can be determined according to actual conditions, and no specific limitation is made here.

[0044] In this way, the target semantic map is corrected according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal, so as to improve the accuracy of the semantic map from the current parking lot to the target parking lot.

[0045] In some embodiments, the target semantic map is post-processed according to the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot, including: obtaining a second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map; fusing the semantic information of the traffic facilities in the high-precision map with the semantic information of the traffic facilities in the target semantic map according to the second correspondence to obtain a second fusion result; and correcting the target semantic map according to the second fusion result to update the semantic map from the current parking lot to the target parking lot.

[0046] Specifically, in the process of constructing the target semantic map in real time, the vehicle may have entered the range of the high-precision map. Based on the high-precision map, the semantic information of the traffic facilities in the high-precision map can be obtained, such as the coordinate information and direction information of the traffic facilities, and the semantic information of the traffic facilities in the high-precision map is usually better than the semantic information of the traffic facilities in the target semantic map. Therefore, in order to improve the accuracy of the semantic map from the current parking lot to the target parking lot, when the vehicle arrives at the target parking lot and powers off, the second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map is obtained, and the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map are fused according to the second correspondence to obtain a second fusion result, and the target semantic map is corrected according to the second fusion result to update the semantic map from the current parking lot to the target parking lot, and the area with the high-precision map is marked on the target semantic map constructed in real time.

[0047] In this way, the target semantic map can be corrected according to the semantic information of the traffic facilities in the high-precision map, which can improve the accuracy of the semantic map from the current parking lot to the target parking lot, and can also achieve seamless connection between the partial area covered by the high-precision map and the target semantic map.

[0048] In some embodiments, obtaining a first correspondence between coordinate information of a vehicle trajectory corresponding to a GNSS signal and coordinate information of a vehicle trajectory generated based on dead reckoning includes: when the coordinate information of the vehicle trajectory is generated based on dead reckoning, in the presence of a GNSS signal, obtaining coordinate information of the vehicle trajectory corresponding to the GNSS signal; and binding the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning to determine the first correspondence.

[0049] Specifically, the coordinate information of the vehicle trajectory obtained by the GNSS signal is relatively accurate, but there may be signal obstruction and other situations that may cause missing or inaccurate data in some time periods. Although dead reckoning can continuously calculate the trajectory to a certain extent, there are also problems such as cumulative errors. Binding the two can complement each other's advantages, using the high precision of GNSS to calibrate the results of dead reckoning, and also rely on dead reckoning to maintain the continuity of the trajectory when the GNSS signal is temporarily unavailable, thereby determining an accurate and reliable first correspondence. Exemplarily, the vehicle trajectory coordinate information obtained by GNSS signals and dead reckoning at the same time or at a similar time is associated and matched. For example, at a certain time point t1, the coordinates obtained by the GNSS signal are (Xgnss1, Ygnss1), and the coordinates obtained by dead reckoning are (Xdr1, Ydr1). The two sets of coordinate information are matched, and multiple such corresponding coordinate groups are sorted in chronological order to determine the first corresponding relationship between the two. The first corresponding relationship can intuitively reflect the correlation between the coordinate information obtained by the two different methods during the entire vehicle driving process, providing a basis for subsequent trajectory fusion, error correction and other work.

[0050] In some embodiments, obtaining a second correspondence between the semantic information of traffic facilities in the high-precision map and the semantic information of traffic facilities in the target semantic map includes: in the process of constructing the target semantic map, when the vehicle position is within the range of the high-precision map, obtaining the semantic information of traffic facilities in the high-precision map; binding the semantic information of traffic facilities in the high-precision map with the semantic information of traffic facilities in the target semantic map to determine the second correspondence.

[0051] Specifically, binding the semantic information of traffic facilities in the high-precision map with the semantic information of traffic facilities in the target semantic map can achieve effective fusion and mutual calibration of map information from different sources. The high-precision map provides relatively authoritative and accurate basic traffic facility information, while the target semantic map describes traffic facilities more from the perspective of real-time perception. By binding the information of the two, the high-precision map can be used to correct the recognition errors that may exist in the target semantic map, and at the same time, the information of the target semantic map can be enriched and improved, and the second corresponding relationship determined can help to conduct more accurate traffic environment analysis, driving decision-making and other work in the future. Exemplarily, for each identifiable traffic facility, the semantic information of the traffic facility in the high-precision map (such as the precise coordinates, type, cycle, etc. of a traffic light in the high-precision map) is matched one by one with the semantic information of the corresponding traffic facility in the target semantic map (the position and state of the traffic light in the relative coordinate system of the vehicle perceived by the sensor), and the second corresponding relationship is determined by sorting out the correlation between the two according to the categories and locations of the traffic facilities. For example, for a right-turn lane guide sign located at a specific intersection, its coordinates in the high-precision map are (X1, Y1), and its category is lane guidance. Its coordinates relative to the vehicle in the target semantic map are (X2, Y2). By matching these two sets of semantic information about the same traffic facility and summarizing the correspondence of many traffic facilities, a second correspondence can be constructed.

[0052] In some embodiments, a target semantic map is constructed based on the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities, including: unifying the semantic information of the traffic facilities, the vehicle's heading information and the coordinate information of the vehicle trajectory into the same coordinate system; determining the vehicle trajectory based on the coordinate information of the vehicle trajectory and the vehicle's heading information; mapping the vehicle trajectory and the semantic information of the traffic facilities into a preset map framework to determine the target semantic map.

[0053] Specifically, the semantic information of traffic facilities, the heading information of the vehicle and the coordinate information of the vehicle trajectory are unified into the same coordinate system to facilitate subsequent fusion processing. The coordinate information of the vehicle trajectory records the specific location of the vehicle at different times, while the heading information of the vehicle reflects the driving direction of the vehicle at each location point. Combining these two, the driving trajectory of the vehicle over a period of time can be fully depicted. For example, it is known that the coordinates of the vehicle at a certain moment are (X1, Y1), and the heading information shows that the vehicle is heading northeast. As time goes by, new position points (X2, Y2) are obtained according to the coordinate information at subsequent moments. These coordinate points in chronological order and combined with the driving direction are connected in sequence to determine how the vehicle is driving on the road, turning, changing lanes, and other actual trajectory conditions. Exemplarily, according to the time sequence, the vehicle trajectory coordinate information and the vehicle heading information corresponding to each moment are read in sequence, the position is determined by the coordinate information, and the connection direction from the position point to the next position point is determined by the heading information, and a continuous and directional vehicle trajectory line is gradually constructed. Appropriate data structures (such as linked lists, arrays, etc.) can be used to store these directional coordinate point sequences to facilitate the subsequent further processing and application of vehicle trajectories. The determined vehicle trajectories and semantic information of traffic facilities are mapped to the corresponding layers and positions in the preset map framework according to their positions in the unified coordinate system. The vehicle trajectory can be mapped to the road layer to clearly show the vehicle's driving route in the preset area; the semantic information of traffic facilities is accurately placed in the corresponding layer position according to its type (such as traffic signs corresponding to the traffic sign layer, signal lights corresponding to the signal light layer, etc.). Through this mapping integration, the preset map framework is filled with specific and meaningful content, thereby ultimately determining the target semantic map.

[0054] To sum up, the map construction and positioning method of the present application solves the mapping and positioning problems from automatic assisted driving in the current parking space to the target parking space, supports starting the vehicle without a GNSS signal, and solves the positioning problem without a high-precision map.

[0055] Corresponding to the above-mentioned embodiments, the present application also proposes a vehicle map construction and positioning device.

[0056] In some embodiments, reference Figure 2 The vehicle map construction and positioning device 200 includes: an acquisition module 210, a mapping module 220 and a positioning module 230.

[0057] Among them, the acquisition module 210 is used to obtain the semantic map from the current parking lot to the target parking lot, the current positioning result, the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities. The mapping module 220 is used to build the target semantic map in real time according to the coordinate information of the vehicle trajectory, the vehicle's heading information and the semantic information of the traffic facilities. The positioning module 230 is used to determine the initial position of the vehicle according to the current positioning result, match and locate the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, the target semantic map is matched with the semantic map from the current parking lot to the target parking lot to update the vehicle position.

[0058] According to one embodiment of the present application, the positioning module 230 is also used to, when the vehicle position is within the range of the high-precision map, perform positioning matching based on the target semantic map and the high-precision map to update the vehicle position.

[0059] According to one embodiment of the present application, the above method also includes: when there is a GNSS signal when the coordinate information of the vehicle trajectory is generated based on dead reckoning, and / or when the vehicle position is within the range of the high-precision map during the construction of the target semantic map, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal and / or the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot.

[0060] According to one embodiment of the present application, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal to update the semantic map from the current parking lot to the target parking lot, including: obtaining a first correspondence between the coordinate information of the vehicle trajectory corresponding to the GNSS signal and the coordinate information of the vehicle trajectory generated based on dead reckoning, and an accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal; fusing the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning according to the first correspondence and the accuracy value to obtain a first fusion result, and correcting the target semantic map according to the first fusion result to update the semantic map from the current parking lot to the target parking lot.

[0061] According to one embodiment of the present application, the target semantic map is post-processed according to the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot, including: obtaining a second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map; fusing the semantic information of the traffic facilities in the high-precision map with the semantic information of the traffic facilities in the target semantic map according to the second correspondence to obtain a second fusion result, and correcting the target semantic map according to the second fusion result to update the semantic map from the current parking lot to the target parking lot.

[0062] According to an embodiment of the present application, obtaining a first correspondence between coordinate information of a vehicle trajectory corresponding to a GNSS signal and coordinate information of a vehicle trajectory generated based on dead reckoning includes: when the coordinate information of the vehicle trajectory is generated based on dead reckoning, in the presence of a GNSS signal, obtaining the coordinate information of the vehicle trajectory corresponding to the GNSS signal; and binding the coordinate information of the vehicle trajectory corresponding to the GNSS signal with the coordinate information of the vehicle trajectory generated based on dead reckoning to determine the first correspondence.

[0063] According to one embodiment of the present application, obtaining a second correspondence between the semantic information of traffic facilities in a high-precision map and the semantic information of traffic facilities in a target semantic map includes: in the process of constructing a target semantic map, when the vehicle position is within the range of the high-precision map, obtaining the semantic information of traffic facilities in the high-precision map; binding the semantic information of traffic facilities in the high-precision map with the semantic information of traffic facilities in the target semantic map to determine the second correspondence.

[0064] According to one embodiment of the present application, the mapping module 220 is specifically used to unify the semantic information of traffic facilities, the heading information of the vehicle and the coordinate information of the vehicle trajectory into the same coordinate system; determine the vehicle trajectory based on the coordinate information of the vehicle trajectory and the heading information of the vehicle; map the vehicle trajectory and the semantic information of traffic facilities into a preset map framework to determine the target semantic map.

[0065] It should be pointed out that the above-mentioned explanation of the embodiments and beneficial effects of the vehicle map construction and positioning method are also applicable to the vehicle map construction and positioning device of the embodiment of the present application. In order to avoid redundancy, they will not be elaborated here.

[0066] Corresponding to the above embodiments, the present application also proposes a vehicle.

[0067] See also Figure 3As shown, the vehicle 300 of the present application includes a memory 310, a processor 320, and a vehicle map building and positioning program stored in the memory 310 and executable on the processor 320. When the processor executes the vehicle map building and positioning program, the aforementioned vehicle map building and positioning method is implemented.

[0068] It should be pointed out that the above-mentioned explanation of the embodiments and beneficial effects of the vehicle map construction and positioning method are also applicable to the vehicle of the embodiment of the present application. In order to avoid redundancy, they will not be elaborated here.

[0069] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0070] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0071] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0072] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0073] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0074] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A vehicle map construction and positioning method, characterized in that: The method comprises: Obtain the semantic map from the current parking lot to the target parking lot, the current positioning result, the coordinate information of the vehicle trajectory, the vehicle heading information and the semantic information of the traffic facilities; Constructing a target semantic map in real time according to the coordinate information of the vehicle trajectory, the heading information of the vehicle and the semantic information of the traffic facilities; The initial position of the vehicle is determined according to the current positioning result, and the semantic map from the current parking lot to the target parking lot is matched and positioned with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, positioning matching is performed based on the target semantic map and the semantic map from the current parking lot to the target parking lot to update the vehicle position.

2. The vehicle map construction and positioning method according to claim 1, characterized in that: The method further comprises: When the vehicle position is within the range of the high-precision map, positioning matching is performed based on the target semantic map and the high-precision map to update the vehicle position.

3. The vehicle map construction and positioning method according to claim 1, characterized in that: The method further comprises: When a GNSS signal is present when generating coordinate information of a vehicle trajectory based on dead reckoning, and / or when the vehicle position is within the range of a high-precision map during construction of a target semantic map, the target semantic map is post-processed according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal and / or the semantic information of traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot.

4. The vehicle map construction and positioning method according to claim 3, characterized in that: Post-processing the target semantic map according to the coordinate information of the vehicle trajectory corresponding to the GNSS signal to update the semantic map from the current parking lot to the target parking lot includes: Acquire a first correspondence between coordinate information of a vehicle trajectory corresponding to a GNSS signal and coordinate information of a vehicle trajectory generated based on dead reckoning, and an accuracy value of the coordinate information of the vehicle trajectory corresponding to the GNSS signal; The coordinate information of the vehicle trajectory corresponding to the GNSS signal is fused with the coordinate information of the vehicle trajectory generated based on dead reckoning according to the first corresponding relationship and the accuracy value to obtain a first fusion result, and the target semantic map is corrected according to the first fusion result to update the semantic map from the current parking lot to the target parking lot.

5. The vehicle map construction and positioning method according to claim 3, characterized in that: Post-processing the target semantic map according to the semantic information of the traffic facilities in the high-precision map to update the semantic map from the current parking lot to the target parking lot includes: Acquire a second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map; The semantic information of the traffic facilities in the high-precision map is fused with the semantic information of the traffic facilities in the target semantic map according to the second corresponding relationship to obtain a second fusion result, and the target semantic map is corrected according to the second fusion result to update the semantic map from the current parking lot to the target parking lot.

6. The vehicle map construction and positioning method according to claim 4, characterized in that: Acquiring a first correspondence between coordinate information of a vehicle trajectory corresponding to a GNSS signal and coordinate information of a vehicle trajectory generated based on dead reckoning, including: When generating the coordinate information of the vehicle trajectory based on dead reckoning, in the presence of a GNSS signal, obtaining the coordinate information of the vehicle trajectory corresponding to the GNSS signal; The coordinate information of the vehicle trajectory corresponding to the GNSS signal is bound to the coordinate information of the vehicle trajectory generated based on dead reckoning to determine the first corresponding relationship.

7. The vehicle map construction and positioning method according to claim 5, characterized in that: Acquiring a second correspondence between the semantic information of the traffic facilities in the high-precision map and the semantic information of the traffic facilities in the target semantic map, including: In the process of constructing the target semantic map, when the vehicle position is within the range of the high-precision map, obtaining semantic information of traffic facilities in the high-precision map; The semantic information of the traffic facilities in the high-precision map is bound with the semantic information of the traffic facilities in the target semantic map to determine the second corresponding relationship.

8. The vehicle map construction and positioning method according to any one of claims 1 to 7, characterized in that: Constructing a target semantic map according to the coordinate information of the vehicle trajectory, the heading information of the vehicle and the semantic information of the traffic facilities, including: Unifying the semantic information of the traffic facility, the heading information of the vehicle and the coordinate information of the vehicle trajectory into the same coordinate system; Determining a vehicle trajectory according to the coordinate information of the vehicle trajectory and the heading information of the vehicle; The vehicle trajectory and the semantic information of the traffic facilities are mapped into a preset map framework to determine the target semantic map.

9. A vehicle map construction and positioning device, characterized in that: The device comprises: An acquisition module is used to obtain a semantic map from the current parking lot to the target parking lot, current positioning results, coordinate information of the vehicle trajectory, vehicle heading information, and semantic information of traffic facilities; A mapping module, used to construct a target semantic map in real time according to the coordinate information of the vehicle trajectory, the heading information of the vehicle and the semantic information of the traffic facilities; A positioning module is used to determine the initial position of the vehicle according to the current positioning result, match and locate the semantic map from the current parking lot to the target parking lot with the target semantic map based on the initial position of the vehicle to determine the vehicle position, and when the vehicle position is not within the range of the high-precision map, match the target semantic map with the semantic map from the current parking lot to the target parking lot to update the vehicle position.

10. A vehicle, characterized in that: The invention comprises a memory, a processor and a vehicle map building and positioning program stored in the memory and executable on the processor. When the processor executes the vehicle map building and positioning program, the vehicle map building and positioning method according to any one of claims 1 to 8 is implemented.

Images