A light-weight memory route mapping method and device, vehicle controller and vehicle

Abstract

The application relates to the technical field of electronic maps, and discloses a light-weight memory route mapping method and device, a vehicle controller and a vehicle. The method comprises the following steps: acquiring road vector information and driving track information collected in real time during driving of a user vehicle on a preset route, and acquiring navigation information provided by a vehicle navigation map application; performing mapping optimization processing on the driving track information to generate high-precision track information; performing aggregation processing on a plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of light-weight road information on the preset route; and creating a memory map of the preset route according to the N pieces of light-weight road information and the navigation information. The real-time mapping data collected and reported by mass-produced user vehicles can save sampling costs, and the sampling data is convenient and flexible and covers the whole road in the mapping content. In addition, the driving track information is subjected to mapping optimization processing, so that the accuracy and reliability of the memory map are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic maps, in particular to a lightweight memory route mapping method and device, a vehicle controller and a vehicle. BACKGROUND

[0002] The AI (Artificial Intelligence) driving function is supported by offline maps of memory routes, and needs to support a large number of users without large-scale testing. At the same time, it is necessary to ensure the lightweight of the mapping process under the premise of ensuring the mapping quality, so as to reduce the cost and improve the efficiency.

[0003] At present, in order to improve the sampling accuracy, the traditional memory route mapping relies on expensive professional surveying and mapping equipment such as laser radar and surveying and mapping grade GNSS (Global Navigation Satellite System), which has high cost and poor practicability. In addition, based on the laser radar and surveying and mapping grade GNSS data, a large amount of computing resources are consumed in the post-processing process to generate a usable high-precision map, and due to the cost limitation, the coverage range of such high-precision map is limited, and the driving road cannot be fully covered. SUMMARY

[0004] Therefore, the present application provides a lightweight memory route mapping method, device, vehicle controller and vehicle to solve the problems of high cost, large consumption of computing resources and inability to fully cover the driving road caused by the traditional memory map.

[0005] In a first aspect, the present application provides a lightweight memory route mapping method, which can be applied to a vehicle controller, and the method comprises:

[0006] acquiring real-time collected road vector information and driving track information of a user vehicle during driving on a preset route, and acquiring navigation information provided by a vehicle navigation map application; wherein the road vector information comprises a plurality of road vector elements;

[0007] performing graph optimization processing on the driving track information to generate high-precision track information;

[0008] performing aggregation processing on the plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of lightweight road information on the preset route, N≥1;

[0009] creating a memory map of the preset route according to the N pieces of lightweight road information and the navigation information.

[0010] In combination with the first aspect, in a possible implementation, the driving track information includes: DR information and GPS information on the driving track; the graph optimization processing of the driving track information generates high-precision track information, including:

[0011] The DR information and the GPS information are fused, and the high-precision track information is generated after the graph optimization processing.

[0012] In combination with the first aspect, in another possible implementation, the aggregation processing of the plurality of road vector elements according to the high-precision track information and the road vector information generates N pieces of lightweight road information on the preset route, including:

[0013] At least two pieces of sampling data recorded at different sampling moments are obtained, and the at least two pieces of sampling data are aggregated by a spatial distance algorithm to generate N pieces of lightweight road information.

[0014] The spatial distance algorithm is that points of continuous elements in the at least two pieces of sampling data are aggregated into a piece in space, and points of non-continuous elements are represented by discrete end points in space.

[0015] In combination with the first aspect, in yet another possible implementation, the navigation information is composed of at least one navigation segment information; before the memory map of the preset route is created according to the N pieces of lightweight road information and the navigation information, the method further includes:

[0016] The N pieces of lightweight road information corresponding to the current navigation segment are spliced, the at least one navigation segment information is checked and repaired, and navigation information consistent with the driving track is generated.

[0017] The creation of the memory map of the preset route according to the N pieces of lightweight road information and the navigation information includes:

[0018] The memory map is created according to the N pieces of lightweight road information and the navigation information consistent with the driving track.

[0019] In combination with the first aspect, in yet another possible implementation, the splicing of the N pieces of lightweight road information corresponding to the current navigation segment and the checking and repairing of the at least one navigation segment information include:

[0020] The navigation route splicing and navigation information repairing are performed on each navigation segment information in the at least one navigation segment information, wherein the navigation route splicing is to splice multiple navigation routes generated in the navigation resetting process into one complete navigation route, and the navigation information repairing is to mount one or more of slope information, curvature information and speed limit information to the correct position of the spliced navigation route, and / or to complete the navigation information by means of the semantic information extracted from the track.

[0021] In combination with the first aspect, in yet another possible implementation manner, after the memory map of the preset route is created, the method further includes:

[0022] loading at least one electronic map near the real-time position of the user vehicle, the at least one electronic map including the created memory map;

[0023] finding a target map in the at least one electronic map according to the route currently traveled by the user vehicle, the target map containing a route matching the route currently traveled by the user vehicle;

[0024] loading the target map and providing the target map to the vehicle terminal display and supporting the subsequent positioning planning module.

[0025] The second aspect of the present application provides a light-weight memory route mapping device, the device including:

[0026] an acquisition module configured to acquire road vector information and driving track information collected in real time during the driving of a user vehicle on a preset route, and navigation information provided by a navigation map application of a vehicle terminal, wherein the road vector information includes a plurality of road vector elements;

[0027] an optimization module configured to perform graph optimization processing on the driving track information to generate high-precision track information;

[0028] a fusion module configured to perform aggregation processing on the plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of light-weight road information on the preset route, N≥1;

[0029] a creation module configured to create a memory map of the preset route according to the N pieces of light-weight road information and the navigation information.

[0030] The third aspect of the present application provides a vehicle machine controller, including a memory and a processor, the memory and the processor being connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the light-weight memory route mapping method of the first aspect or any of the corresponding implementation manners thereof.

[0031] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to execute the light-weighted memory route mapping method of the first aspect or any of the corresponding embodiments thereof.

[0032] In addition, the present application provides a computer program product comprising computer instructions for causing a computer to execute the light-weighted memory route mapping method of the first aspect or any of the corresponding embodiments thereof.

[0033] In a fifth aspect, the present application further provides a vehicle comprising a car machine controller and a collection device, wherein the collection device is configured to collect road vector information and driving track information of a user's car in real time during driving on a preset route, and report the road vector information and the driving track information to the car machine controller.

[0034] The car machine controller is configured to acquire the road vector information and the driving track information reported by the collection device, acquire navigation information provided by a car-end navigation map application, perform mapping optimization on the driving track information to generate high-precision track information, perform aggregation processing on a plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of light-weighted road information on the preset route (N≥1), and create a memory map of the preset route according to the N pieces of light-weighted road information and the navigation information.

[0035] In addition, the car machine controller is further configured to execute the light-weighted memory route mapping method of the other embodiments corresponding to the first aspect.

[0036] The light-weighted memory route mapping method, device, car machine controller and vehicle provided by the present embodiment utilize real-time mapping data collected and reported by mass-produced user cars, such as road vector elements including road vector information and driving track information, which saves sampling costs compared to relying on expensive professional surveying equipment such as laser radar and surveying-grade GNSS, and as long as the user car drives on a route, sampling data for mapping can be obtained, which is convenient and flexible, and the mapping content can achieve full coverage of the road within the driving range.

[0037] In addition, the sampled driving track information is subjected to mapping optimization processing, and the processed high-precision track information is fused with N pieces of light-weighted road information on the preset route to generate a memory map, which further improves the accuracy and reliability of the mapping data.

[0038] And the method provided by the embodiment can realize route matching and loading of correct memory map data at the vehicle end, so that a large amount of network resources and computing resources consumed in the server side are avoided. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0040] Figure 1 is a flowchart of a lightweight memory route mapping method according to an embodiment of the present application;

[0041] Figure 2 is a flowchart of another lightweight memory route mapping method according to an embodiment of the present application;

[0042] Figure 3 is a flowchart of another lightweight memory route mapping method according to an embodiment of the present application;

[0043] Figure 4 is a flowchart of another lightweight memory route mapping method according to an embodiment of the present application;

[0044] Figure 5 is a structural block diagram of a lightweight memory route mapping device according to an embodiment of the present application;

[0045] Figure 6 is a hardware structural diagram of a vehicle machine controller according to an embodiment of the present application;

[0046] Figure 7 is a structural diagram of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0048] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0049] An electronic map, which combines advanced Geographic Information System (GIS), remote sensing technology, Global Positioning System (GPS) and Internet technology, converts a traditional paper map into a digital platform with strong interaction, rich information and real-time update.

[0050] In the process of creating an electronic map, there are problems such as low real-time mapping accuracy at the vehicle end, discontinuous, incomplete navigation information caused by navigation reset, and loop matching, etc. Among them, the loop matching problem is caused by factors such as environmental changes and geometric error accumulation, and the success rate of loop matching based on low-precision real-time mapping data is difficult to guarantee. Therefore, the present application needs to solve the above problems by algorithm optimization, data-driven intelligent matching and other methods to realize high-precision memory route mapping of lightweight real-time mapping data, thereby meeting the demand for high-quality and lightweight maps in AI driving scenarios.

[0051] It should be noted that the lightweight memory route mapping method provided by the embodiments of the present application can be a mapping device or a mapping equipment, which can be realized by software, hardware or a combination of software and hardware to become part or all of an electronic device. The electronic device can be a control device at the vehicle end, such as a car machine controller, or it can also be a terminal device or a cloud device, such as a cloud server and other network devices. The electronic device is not limited in the embodiments of the present application. In the following method embodiments, the execution subject is taken as an example to be explained.

[0052] According to the embodiments of the present application, a lightweight memory route mapping method embodiment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from here.

[0053] In the present embodiment, a lightweight memory route mapping method is provided, which can be used for the car machine controller described above, Figure 1 is a flowchart of the lightweight memory route mapping method according to the embodiments of the present application, which comprises:

[0054] Step S101: Obtain the road vector information, driving trajectory information collected in real time during the driving of the user vehicle on the preset route, and the navigation information provided by the map application at the vehicle end.

[0055] Among them, at least one sensor is arranged on the user vehicle, such as a camera, a millimeter wave radar, a speed sensor, a GPS sensor, a gyroscope sensor, etc., which can be collectively referred to as a collection device, for measuring road vector information and driving trajectory information during driving.

[0056] The road vector information includes a plurality of road vector elements, which are a kind of road mapping elements for describing road and road peripheral route, identification and other information. The road vector elements include but are not limited to lane lines, dashed lines, lane boundary lines, road boundary lines, road arrows, stop lines, sidewalk lines or pedestrian crossings, etc., and these road vector elements can be represented by coordinate points.

[0057] The driving trajectory information refers to the trajectory route passed by the user vehicle during driving on the preset route. Among them, the driving trajectory information can include DR (Dead Reckoning, Track Recursion) information and GPS information.

[0058] Among them, the DR information mainly includes: initial position information, heading information, speed information, and sailing time of the user vehicle. According to the above initial position, heading information, speed information and sailing time, the DR system can calculate the current position of the user vehicle in real time through a mathematical model. This calculated position is a dynamically updated value that changes with the passage of sailing time. DR information is not affected by external environment, but the error will accumulate with time.

[0059] The GPS information includes three-dimensional position, speed information and time information. Specifically, the three-dimensional position refers to: the GPS system can determine the three-dimensional position (longitude, latitude and height) of the vehicle in real time by receiving signals from global positioning satellites, which is used to accurately locate the geographical position where the vehicle is currently located. The speed information refers to: in addition to the position information, the three-dimensional speed information of the vehicle, including the speed and direction. Time information: GPS system can provide accurate time information, which can be used for time synchronization. The GPS signal may be affected by multipath effect, building obstruction, tunnel and other environmental factors, resulting in decreased positioning accuracy.

[0060] The navigation information provided by the navigation map application at the vehicle end refers to the navigation information provided by the navigation map application APP on the vehicle controller for the preset route of the user vehicle, which can be displayed on the display screen of the vehicle in the form of a navigation map.

[0061] Optionally, the navigation information includes ADASIS V2 navigation information. ADASIS V2 (Advanced Driver Assistance Systems Interface Specifications Version 2) is a widely used communication protocol and data format specification in the automotive industry, providing various information about the road ahead, including lane geometry, slope, curvature, speed limits, traffic signs, etc. This data can be used by ADAS applications such as adaptive cruise control, lane keeping assist, and traffic sign recognition. This protocol enables the transmission of key data such as map data, real-time traffic information, and location information between vehicles through standardized data formats and interfaces.

[0062] Step S102: Perform image optimization processing on the driving trajectory information to generate high-precision trajectory information.

[0063] One implementation method, such as Figure 2 As shown, when the vehicle trajectory information includes DR information and GPS information, step S102 above performs image optimization processing on the vehicle trajectory information to generate high-precision trajectory information, specifically including:

[0064] Step S102-1: The recursive DR information and GPS information are fused together, and after graph optimization processing, the high-precision trajectory information is generated.

[0065] The main purpose of Pose Graph processing is to globally optimize the pose (position and orientation) of a robot or camera at different time points through graph optimization methods, so as to improve the accuracy and consistency of the entire trajectory.

[0066] This step mainly involves data fusion, trajectory construction, and graph optimization of DR and GPS information. Specifically, GPS and DR data are filtered and denoised to improve accuracy and reliability, and the timestamps of the two data sources are synchronized to ensure temporal alignment. Then, the data is processed using a data fusion algorithm, such as Kalman filtering or its variants (e.g., Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), etc.), to optimally fuse the GPS and DR data. Next, updated GPS and DR data are received in real time, and the latest vehicle position and attitude are calculated using the data fusion algorithm. The newly calculated position points are added to the trajectory to form a continuous trajectory line.

[0067] Finally, each position point (pose) in the trajectory is considered as a node in the graph, and the relative positional relationship between nodes is considered as an edge. According to the estimation results of the DR system and the observation results of the GPS, edges are established between nodes and corresponding weights (representing measurement errors) are assigned. The pose graph is optimized using a pose graph optimization algorithm (such as Gauss-Newton method, Levenberg-Marquardt algorithm, LM optimization, etc.). The algorithm adjusts the position of the node through iteration to minimize the weight of the edge (i.e. measurement error), so as to obtain a globally consistent trajectory. Finally, the high-precision trajectory is output, and the high-precision trajectory information obtained after the graph optimization PoseGraph processing is used to realize high-precision vehicle positioning and navigation.

[0068] Step S103: According to the high-precision trajectory information and the road vector information, a plurality of road vector elements are aggregated to generate N pieces of lightweight road information on the preset route, N≥1.

[0069] In this step, according to the lightweight road information, the preset route is divided into a plurality of road segments (such as N segments, N is a positive integer). Each road segment can contain a relatively independent road and have similar geographical features or navigation points.

[0070] Specifically, according to the real-time data of the vehicle's latitude and longitude coordinates, speed, direction, etc. in the high-precision trajectory information, and the road's geometric shape (such as lines, curves), attributes (such as the number of lanes, speed limit, direction, etc.) and relationship with other roads (such as intersections, ramps, etc.) contained in the road vector information, the road vector elements are aggregated. The aggregation process includes road element aggregation, such as segmenting the preset route: the preset route is divided into N segments according to certain rules (such as distance, time, road type change, etc.). The length and number of each segment can be adjusted according to actual needs, and the key attribute information of each road segment is extracted, such as road segment length, average speed limit, number of lanes, road type, etc.

[0071] The road vector elements are simplified to remove unnecessary details and retain information essential for navigation and path planning, generating N pieces of lightweight road information. These information maintains high precision while reducing data redundancy and storage requirements, improving system response speed and efficiency.

[0072] Step S104: According to the N pieces of lightweight road information and the navigation information, a memory map of the preset route is created.

[0073] Specifically, the N pieces of lightweight road information generated according to the step S103 and the navigation information, such as ADASIS V2 navigation information, acquired in the step S101 are combined, and a memory map of the preset route composed of the N pieces of lightweight road is generated through visualization display.

[0074] The method for creating the memory map includes, but is not limited to, a SLAM (Simultaneous Localization and Mapping) method, a map visualization method, such as a geographic information system (GIS) software, a segmented memory method, an associative memory method, a digital memory method, and a plurality of review and practice methods.

[0075] The method provided in the embodiment saves sampling costs compared to relying on expensive professional surveying equipment, such as a laser radar and a surveying-grade GNSS, and sampling data for mapping can be obtained for any route on which a user vehicle travels, which is convenient and flexible, and the mapping content can achieve full coverage of roads within a driving range.

[0076] In addition, the sampled driving trajectory information is optimized for mapping, and the processed high-precision trajectory information is fused with the N pieces of lightweight road information on the preset route to generate the memory map, which further improves the accuracy and reliability of the mapping data.

[0077] The method provided in the embodiment can achieve route matching and loading of correct memory map data at the vehicle end, avoiding consumption of a large amount of network resources and computing resources at the back end, such as a server side, and the memory map generated by the method provides reliable map support for AI driving assistance.

[0078] Referring to Figure 3 In a possible implementation of the embodiment, the step S103 includes the following steps.

[0079] The step S103-1 includes the following steps.

[0080] The step S103-2 includes the following steps.

[0081] The spatial distance algorithm is that points of continuous elements in at least two sampling data are aggregated into a segment in space, and points of non-continuous elements can be represented by discrete end points, such as M end points, for example, a straight arrow on the ground can be represented by end points A and straight arrow vertex B, and two points are used for representation. M is greater than or equal to 2 and is a positive integer.

[0082] According to the fusion method of the continuous element (such as a long solid line, a road edge), the continuous element is represented by a series of points, such as a continuous element including a lane boundary and a road boundary. For the continuous element, first, all the continuous elements are overlapped in space, and if the transverse distance is less than or equal to a preset distance, the two lines are regarded as two observations of the same continuous element. If the distance is greater than the preset distance, the two lines are regarded as two observations of two different continuous elements. Then, the geometric position average and attribute voting are performed on the two or more observations of the same continuous element after clustering, to obtain a continuous element with better spatial position accuracy and more accurate attributes.

[0083] For the non-continuous element, a possible processing method includes: first, the end points of the element are split, and geometric constraints (position, direction), attribute constraints and prior constraints between point elements are constructed. For example, the attribute constraint of the ground arrow can be divided into the arrow tail point and the arrow vertex. The arrow vertex can be further divided into a straight arrow vertex, a left-turn arrow vertex and the like.

[0084] The prior constraint takes the road arrow as an example. The arrows are necessarily in the same lane, and the lanes are separated by lane lines, so the connection line between the arrows is not allowed to pass through the lane line.

[0085] In this step, the point elements are associated and fused based on the above various constraints, and the new point elements are generated. In the fusion process, the data freshness is weighted, and the new point elements are assembled to restore the new road sign elements, and the lightweight road information of each segment is generated.

[0086] The navigation information is composed of at least one navigation segment information; the navigation information is visualized and displayed as a segment of navigation information, and each segment is a distance. Before step S104, the method of the embodiment further includes:

[0087] Step S104-1: Splicing the section corresponding to the current navigation segment in the N lightweight road information, verifying and repairing the at least one navigation segment information, and generating navigation information consistent with the driving track.

[0088] Specifically, due to the possibility of incorrect navigation information or missing navigation information in individual segment navigation information, and occasionally deviated route information, it is necessary to first check and repair at least one navigation segment information before splicing the N segment lightweight road information with individual segment navigation information to correct the incorrect information.

[0089] A specific implementation of navigation route splicing and navigation information repair for each navigation segment information in at least one navigation segment information includes: splicing multiple navigation route segments generated during a navigation reset process into one complete navigation route, which is a navigation route splicing process.

[0090] Specifically, an implementation process of navigation route splicing includes: first, collecting all navigation route segments that need to be spliced, including the start point, end point, passing point, direction, and other information of each route segment. Then, according to the coordinate information of the start point and the end point, the route segments are matched and sorted to ensure that they can be connected together in the correct order. Spatial position algorithms such as nearest neighbor search and geometric matching are involved. Finally, after confirming that the order of the route segments is correct, they are connected in turn to form a complete navigation route. During the splicing process, the connection points between the route segments need to be handled to ensure smoothness and no discontinuity at the connection points. Optionally, the spliced route is also optimized to remove redundant points, adjust the path to reduce travel distance or time, etc.

[0091] The navigation information repair is to mount one or more of slope, curvature, speed limit information to the correct position of the spliced navigation route, and / or to complete the navigation information through trajectory extraction semantic information. A specific implementation process includes: using map data or real-time sensor data (such as GPS, gyroscope, etc.) to add slope, curvature, speed limit, etc. information point by point or segment by segment on the spliced navigation route. Then, through trajectory extraction technology (such as machine learning, deep learning, etc.), semantic information such as road type (such as expressway, urban road, rural road, etc.), traffic signs (such as speed limit signs, no-turn signs, etc.) is extracted from the existing driving trajectory, and is mounted to the corresponding navigation route segment.

[0092] For missing or incorrect information, it is completed or corrected by comparing other reliable data sources (such as other users' navigation records, government-issued traffic information, etc.). Finally, the repaired navigation information is verified to ensure its accuracy and reliability.

[0093] In addition, optionally, in this step, it also includes the processing process of trajectory pose optimization, reverse processing, and trajectory semantic information extraction (such as identifying turns, ramps, etc.), and after these processing processes, the final output is the navigation information consistent with the driving trajectory.

[0094] The step S104 creates a memory map of the preset route according to the N pieces of lightweight road information and the navigation information, and specifically includes:

[0095] The step S104-2 creates the memory map according to the N pieces of lightweight road information and the navigation information consistent with the driving trajectory.

[0096] The navigation information consistent with the driving trajectory generated in the step S104-1 is fused with the N pieces of lightweight road information to create the memory map.

[0097] In the embodiment, the multi-segment navigation route is automatically spliced by associating the trajectory and the navigation route information, and the erroneous and missing navigation information is repaired, so that the problem of discontinuity and incompleteness of the navigation information caused by the navigation reset is effectively solved, and more accurate, complete and real-time navigation guidance is provided for the user.

[0098] In another specific implementation of the embodiment, as shown in Figure 4 The step S104 further includes, after the memory map of the preset route is created:

[0099] The step S105 loads at least one electronic map near the real-time position of the user's vehicle according to the real-time position of the user's vehicle.

[0100] The at least one electronic map includes the memory map created. The memory map of the user's vehicle driving on each road is created according to the steps S101 to S104 as the position of the user's vehicle changes in real time during driving. The method of creating the memory map is specifically described in the foregoing embodiments, which will not be described here.

[0101] The step S106 finds a target map in the at least one electronic map according to the route currently driven by the user's vehicle, and the route included in the target map matches the route currently driven by the user's vehicle.

[0102] Specifically, the matching method of finding the target map includes searching for adjacent possible routes in the electronic map around the current position of the vehicle. These routes should be consistent with the current driving direction of the user. Then, the shape of the current driving route (for example, through a series of coordinate points or path identifiers) is compared with the shape of the route stored in the electronic map. Specifically, a similarity algorithm (such as Hausdorff distance, dynamic time warping DTW, etc.) can be used to evaluate the matching degree between them. Optionally, other properties of the route, such as road type (expressway, urban road, etc.), traffic flow, speed limit, etc., can also be considered in the process of finding the matching route to ensure that the most suitable matching is found.

[0103] The step S107 loads the target map and provides the target map to the vehicle-side display and supports the subsequent positioning planning module.

[0104] Specifically, after loading the target map, it is displayed on the screen and provides support for the subsequent positioning and planning module. This planning module can be another module or device connected to the vehicle's infotainment system controller.

[0105] In this embodiment, map elements are associated with trajectory mileage information, using mileage data to solve the loop matching problem. Furthermore, map elements within a specific range can be quickly located and extracted based on mileage information, improving search and utilization efficiency. In addition, this method also enables the search for matching target maps in the electronic map based on the user's current driving route, providing the user with accurate navigation maps in real time on the vehicle.

[0106] This embodiment also provides a lightweight memory route mapping device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0107] This embodiment provides a lightweight memory route mapping device, such as... Figure 5 As shown, the device includes: an acquisition module 510, an optimization module 520, a fusion module 530, and a creation module 540. In addition, the device may include other more or fewer units or modules, such as a storage unit, etc. This embodiment does not limit this.

[0108] The acquisition module 510 is used to acquire road vector information and driving trajectory information collected in real time during the user's vehicle's journey on a preset route, as well as navigation information provided by the vehicle-side navigation map application; the road vector information includes multiple road vector elements.

[0109] The optimization module 520 is used to perform graphical optimization processing on the driving trajectory information to generate high-precision trajectory information.

[0110] The fusion module 530 is used to aggregate the multiple road vector elements according to the high-precision trajectory information and the road vector information to generate N lightweight road information segments on the preset route, where N≥1 and N is a positive integer.

[0111] The creation module 540 is used to create a memory map of the preset route based on the N segments of lightweight road information and the navigation information.

[0112] In some alternative implementations, the driving trajectory information includes GPS information along the driving trajectory.

[0113] The optimization module 520 is specifically configured to fuse the track recursive DR information and the GPS information, and generate the high-precision track information after graph optimization processing.

[0114] In some optional embodiments, the fusion module 530 is specifically configured to acquire at least two sampling data recorded at different sampling moments of the plurality of road vector elements; and aggregate the at least two sampling data through a spatial distance algorithm to generate the N pieces of lightweight road information.

[0115] The spatial distance algorithm is that points of continuous elements in the at least two sampling data are aggregated into a piece in space, and points of non-continuous elements are represented by discrete end points in space.

[0116] In some optional embodiments, the navigation information is composed of at least one navigation segment information.

[0117] The device provided in the embodiment further includes a splicing and repairing module, which is configured to splice a section corresponding to a current navigation segment in the N pieces of lightweight road information before the creation module 540 creates the memory map of the preset route, check and repair the at least one navigation segment information, and generate navigation information consistent with the driving track.

[0118] The creation module 540 is specifically further configured to create the memory map according to the N pieces of lightweight road information and the navigation information consistent with the driving track.

[0119] In some optional embodiments, the splicing and repairing module is specifically configured to perform navigation route splicing and navigation information repairing on each navigation segment information in the at least one navigation segment information.

[0120] The navigation route splicing is to splice a plurality of navigation routes generated in a navigation resetting process into one complete navigation route; and the navigation information repairing is to mount one or more of slope, curvature and speed limit information to a correct position of the spliced navigation route, and / or complete the navigation information through semantic information extracted from the track.

[0121] Optionally, in some embodiments, the above lightweight memory route mapping device further includes a loading module, a searching module and a display module.

[0122] Specifically, the loading module is configured to load at least one electronic map near a real-time position of the user's vehicle, and the at least one electronic map includes the created memory map.

[0123] The searching module is configured to search a target map from at least one electronic map according to a route currently traveled by the user vehicle, the target map containing a route matching the route currently traveled by the user vehicle.

[0124] The loading module is further configured to load the target map and provide the target map to a vehicle terminal display through the display module, and provide support for a subsequent positioning planning module.

[0125] Optionally, the display module is a display screen or a display.

[0126] Further function descriptions of the above-mentioned modules and units are the same as those of the corresponding embodiments, and thus are not described herein.

[0127] The lightweight memory route mapping device in the embodiment is in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices capable of providing the above functions.

[0128] The embodiment of the present application also provides a vehicle machine controller having the above-mentioned lightweight memory route mapping device. Figure 5

[0129] Please refer to Figure 6 , which is a structural schematic diagram of a vehicle machine controller provided by an optional embodiment of the present application, as shown in Figure 6 , the vehicle machine controller comprises one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are communicatively connected to each other by different buses, and can be installed on a common mainboard or in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface).

[0130] In some optional embodiments, multiple processors and / or multiple buses can be used together with multiple memories and multiple storage devices, if necessary. Similarly, multiple computer devices can be connected, each providing part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 In the above-mentioned vehicle machine controller, the processor 10 is taken as an example.

[0131] ​The processor 10 can be a central processing unit, a network processing unit, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0132] The memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods illustrated in the above embodiments.

[0133] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely with respect to the processor 10, and these remote memories can be connected to the in-vehicle infotainment controller through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0134] The memory 20 can include a volatile memory, such as a random access memory, and can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 20 can further include a combination of the above-mentioned types of memories.

[0135] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 can be connected by a bus or other means, Figure 6 For example, by way of a bus connection.

[0136] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, and the like. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), and the like. The display device includes, but is not limited to, a liquid crystal display, a light emitting diode, a display, and a plasma display. In some alternative embodiments, the display device can be a touch screen.

[0137] In addition, the vehicle machine controller further comprises at least one communication interface for the vehicle machine controller to communicate with other devices or communication networks, such as connecting at least one sensor and display screen, etc.

[0138] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories.

[0139] It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor or hardware, the lightweight memory route mapping method shown in the above embodiments is implemented.

[0140] The embodiments of the present application provide a computer program product, which comprises computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the lightweight memory route mapping method of any embodiment of the present application.

[0141] In addition, referring to Figure 7 , the embodiments of the present application further provide a vehicle, which comprises a vehicle machine controller and a collection device.

[0142] The collection device is used to collect road vector information and driving track information of the user vehicle in real time during driving on a preset route, and report the road vector information and the driving track information to the vehicle machine controller. In addition, the collection device includes but is not limited to various sensors, such as camera, millimeter wave radar and other devices or apparatuses, and the number of collection devices can be set according to actual needs.

[0143] The vehicle machine controller is configured to acquire the road vector information and driving track information reported by the collection device, acquire navigation information provided by a navigation map application at the vehicle end, perform graph optimization processing on the driving track information, and generate high-precision track information; perform aggregation processing on a plurality of road vector elements according to the high-precision track information and the road vector information, and generate N pieces of lightweight road information on a preset route; and create a memory map of the preset route according to the N pieces of lightweight road information and the navigation information.

[0144] In addition, the vehicle machine controller is further configured to perform all or part of the method steps shown in the foregoing Figure 2 , Figure 3 and Figure 4 , and the specific process can be referred to the foregoing embodiment description, which will not be described here again.

[0145] The above embodiments are only used to illustrate the technical solutions of the embodiments of the present application, rather than limit them; although the embodiments of the present application have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A light-weighted memory route mapping method, characterized in that, The method comprises: acquiring road vector information, driving track information, and navigation information provided by a navigation map application during driving of a user vehicle on a preset route in real time; the road vector information comprises a plurality of road vector elements, and the driving track information refers to a track route passed by the user vehicle during driving on the preset route; performing graph optimization processing on the driving track information to generate high-precision track information; performing aggregation processing on the plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of lightweight road information on the preset route, wherein N is greater than or equal to 1; creating a memory map of the preset route according to the N pieces of lightweight road information and the navigation information; wherein the aggregation processing on the plurality of road vector elements according to the high-precision track information and the road vector information to generate N pieces of lightweight road information on the preset route comprises: acquiring at least two sampling data recorded at different sampling moments for the plurality of road vector elements; performing aggregation on the at least two sampling data by a spatial distance algorithm to generate the N pieces of lightweight road information; wherein the spatial distance algorithm aggregates points of continuous elements in at least two sampling data into a segment in space, and points of non-continuous elements are represented by discrete end points in space.

2. The method of claim 1, wherein, The driving track information comprises DR information and GPS information on the driving track; the graph optimization processing on the driving track information to generate high-precision track information comprises: fusing the DR information and the GPS information, and generating the high-precision track information after graph optimization processing.

3. The method according to claim 1 or 2, characterized in that, The navigation information is composed of at least one navigation segment information; before creating the memory map of the preset route according to the N pieces of lightweight road information and the navigation information, the method further comprises: splicing a section corresponding to a current navigation segment in the N pieces of lightweight road information, checking and repairing the at least one navigation segment information to generate navigation information consistent with the driving track; the creation of the memory map of the preset route according to the N pieces of lightweight road information and the navigation information comprises: creating the memory map according to the N pieces of lightweight road information and the navigation information consistent with the driving track.

4. The method of claim 3, wherein, the splicing of the section corresponding to the current navigation segment in the N pieces of lightweight road information, and the checking and repairing of the at least one navigation segment information comprises: performing navigation route splicing and navigation information repairing on each navigation segment information in the at least one navigation segment information; wherein the navigation route splicing is to splice a plurality of navigation routes generated during navigation resetting into a complete navigation route; and the navigation information repairing is to mount one or more of slope, curvature, and speed limit information to a correct position of the spliced navigation route, and / or to complete the navigation information by using semantic information extracted from the track.

5. The method of claim 1, wherein, after creating the memory map of the preset route, the method further comprises: According to the real-time position of the user vehicle, at least one electronic map near the user vehicle is loaded, and the at least one electronic map includes the memory map created; According to the route currently traveled by the user vehicle, a target map is searched for in the at least one electronic map, and the target map contains a route matching the route currently traveled by the user vehicle; The target map is loaded and provided to a vehicle terminal display and supports a subsequent positioning planning module.

6. A light-weighted memory route mapping device, characterized by, The device comprises: An acquisition module is configured to acquire road vector information and driving track information collected in real time during travel of a user vehicle on a preset route, and acquire navigation information provided by a navigation map application of a vehicle terminal; the road vector information includes a plurality of road vector elements, and the driving track information refers to a track route traveled by the user vehicle on the preset route; An optimization module is configured to perform graph optimization on the driving track information to generate high-precision track information; A fusion module is configured to aggregate the plurality of road vector elements based on the high-precision track information and the road vector information to generate N pieces of lightweight road information on the preset route, where N is greater than or equal to 1; A creation module is configured to create a memory map of the preset route based on the N pieces of lightweight road information and the navigation information. The fusion module is specifically configured to acquire at least two sampling data recorded at different sampling moments for the plurality of road vector elements, aggregate the at least two sampling data by a spatial distance algorithm to generate the N pieces of lightweight road information, and wherein the spatial distance algorithm aggregates points of continuous elements in the at least two sampling data into a piece in space and represents points of non-continuous elements as discrete end points in space.

7. A head unit, comprising: The device comprises a memory and a processor connected to each other; The memory stores computer instructions, and the processor executes the computer instructions to perform the lightweight memory route mapping method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to perform the lightweight memory route mapping method of any one of claims 1 to 5.

9. A computer program product, characterised in that, The computer readable storage medium stores computer instructions for causing a computer to perform the lightweight memory route mapping method of any one of claims 1 to 5.

10. A vehicle characterized by comprising: The computer readable storage medium stores computer instructions for causing a computer to perform the lightweight memory route mapping method of any one of claims 1 to 5. The device comprises a vehicle terminal controller and an acquisition device, wherein The acquisition device is configured to collect road vector information and driving track information in real time during travel of a user vehicle on a preset route, and report the road vector information and the driving track information to the vehicle terminal controller; the road vector information includes a plurality of road vector elements, and the driving track information refers to a track route traveled by the user vehicle on the preset route. The car machine controller is configured to acquire the road vector information and the driving track information reported by the collection device, acquire navigation information provided by a car-end navigation map application, perform graph optimization on the driving track information, and generate high-precision track information; perform aggregation processing on the plurality of road vector elements according to the high-precision track information and the road vector information, and generate N pieces of lightweight road information on the preset route, where N is greater than or equal to 1; and create a memory map of the preset route according to the N pieces of lightweight road information and the navigation information. The car machine controller is specifically configured to acquire at least two pieces of sampling data recorded at different sampling moments for the plurality of road vector elements; and aggregate the at least two pieces of sampling data by using a spatial distance algorithm to generate the N pieces of lightweight road information; and the spatial distance algorithm is that points of continuous elements in the at least two pieces of sampling data are aggregated into a piece in space, and points of non-continuous elements are represented by discrete end points in space.

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