Speed limit processing method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to a speed limit processing method and device, electronic equipment, a storage medium and a vehicle. The method comprises the steps that multiple lane groups in a target area in a high-precision map and multiple road lines in a target area in a standard map are acquired; sampling points matched with the target shape point set are determined from all the sampling points in the multiple road lines, a target matching point set is obtained, and the target shape point set comprises shape points of all the lane lines in the target lane group; determining a sampling point matched with each shape point in the target shape point set from a plurality of sampling points in the target matching point set to obtain a plurality of matching point pairs; and determining the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair so as to determine the speed limit value of each shape point in the target shape point set.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a speed limit processing method, device, electronic device, storage medium and vehicle. Background Art

[0002] At present, with the continuous development of electronic technology, the content that maps can present to people is becoming more and more abundant. High-precision maps are reconstructions of the road environment. High-precision maps contain a large amount of information on the location, type, width of lane lines, as well as road elements such as traffic lights and traffic signs, forming an accurate three-dimensional representation of the road network. At the same time, high-precision maps also contain various information related to the vehicle's driving environment, such as speed limit information. With the rapid development of high-precision maps, more and more vehicles need to use high-precision maps to achieve assisted driving or automatic driving.

[0003] Due to the high-precision map's high road network density and complex scenes, in order to ensure efficiency and quality, the current method of obtaining speed limit information on high-precision maps is mainly to use professional surveying and mapping vehicles to collect speed limit values ​​on the road. The collection process takes a long time, and it is impossible to collect speed limit values ​​on the road frequently. However, when there is frequent road construction in a certain area, this method will not be able to meet actual needs, resulting in untimely updates of speed limit information in the high-precision map, affecting the use of assisted driving or automatic driving functions.

[0004] Therefore, how to obtain the speed limit value on the road in a timely manner and update the data in the high-precision map without using professional surveying vehicles has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a speed limit processing method, device, electronic device, storage medium and vehicle, which can reduce the complexity of obtaining speed limit information of high-precision maps, thereby reducing time consumption and improving the efficiency of obtaining speed limit information of high-precision maps.

[0006] In a first aspect, an embodiment of the present application provides a speed limit processing method, comprising: obtaining multiple lane groups in a target area in a high-precision map, and multiple road lines in a target area in a standard map; determining, from each sampling point in the multiple road lines, a sampling point that matches a target shape point set to obtain a target matching point set, the target shape point set including shape points of each lane line in the target lane group, the target lane group being any one of the multiple lane groups, and the road section indicated by the target matching point set including the road section indicated by the target lane group; determining, from the multiple sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set to obtain multiple matching point pairs, each matching point pair including a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point; determining the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, the target matching point pair being any one of the multiple matching point pairs.

[0007] In some embodiments of the present application, the sampling point that matches the target shape point set is determined from each sampling point in the multiple road lines to obtain the target matching point set, including: using a convex hull algorithm to determine the convex hull of the target shape point set to obtain a target polygon composed of the outermost shape points of the target shape point set; performing spatial operations on the target polygon and the sampling point set to obtain the target matching point set, the target matching point set includes each sampling point in the sampling point set that is located within the target polygon, and the sampling point set includes each sampling point in the multiple road lines.

[0008] In some embodiments of the present application, the sampling point that matches the target shape point set is determined from each sampling point in the multiple road lines to obtain the target matching point set, including: based on the target shape point set, generating a target road section reference line corresponding to the target shape point set, the driving direction of the target road section reference line is the same as the driving direction of the target lane line, and the curve change trend of the target road section reference line is the same as the curve change trend of the target lane line; the target lane line is any lane line in the target lane group; from each sampling point in the multiple road lines, determine the sampling point whose distance to the target road section reference line is less than or equal to the distance threshold to obtain the target matching point set.

[0009] In some embodiments of the present application, the method determines, from each sampling point in a plurality of road lines, a sampling point that matches a target shape point set to obtain a target matching point set, including: determining, from a plurality of road lines, at least one road line that matches a target lane group, wherein the road sections indicated by each road line in the at least one road line overlap with the road sections indicated by the target lane group; and determining, from each sampling point in the at least one road line, a sampling point that matches a target shape point set to obtain a target matching point set.

[0010] In some embodiments of the present application, the sampling points that match each shape point in the target shape point set are determined from multiple sampling points in the target matching point set to obtain multiple matching point pairs, including: calculating the distance between the one shape point and each sampling point in the target matching point set to obtain multiple distances corresponding to the one shape point; determining the sampling point corresponding to the minimum value of the multiple distances corresponding to the one shape point as the one sampling point, to obtain a matching point pair including the one shape point and the one sampling point, so as to obtain multiple matching point pairs.

[0011] In some embodiments of the present application, the method of determining, from a plurality of sampling points in a target matching point set, sampling points that respectively match each shape point in a target shape point set to obtain a plurality of matching point pairs includes: grouping the plurality of sampling points in the target matching point set according to the driving directions indicated by corresponding road lines to obtain a plurality of matching point subsets; determining, from the plurality of matching point subsets, a target matching point subset that has the shortest distance from the one shape point; determining the one sampling point in the target matching point subset that has the shortest distance from the one shape point to obtain a matching point pair including the one shape point and the one sampling point, to obtain a plurality of matching point pairs.

[0012] In some embodiments of the present application, determining a target matching point subset with the shortest distance from the shape point from multiple matching point subsets includes: taking the shape point as a target set, determining the Hausdorff distance between the target set and each matching point subset, and obtaining multiple Hausdorff distances; determining the matching point subset corresponding to the minimum value of the multiple Hausdorff distances as the target matching point subset; or determining the distance between the shape point and each matching point subset respectively, and obtaining multiple target distances corresponding to the shape point, each target distance being the average value of the distance between the shape point and each sampling point in the corresponding matching point subset, or each target distance being the sum of the distance between the shape point and each sampling point in the corresponding matching point subset; determining the matching point subset corresponding to the minimum value of the multiple target distances as the target matching point subset.

[0013] In some embodiments of the present application, after the speed limit value of the sampling point in the target matching point pair is determined as the speed limit value of the shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, the method further includes: determining the speed limit value of the shape point and the average of the speed limit values ​​of multiple shape points adjacent to the shape point as the final speed limit value of the shape point to determine the final speed limit value of each shape point in the target shape point set.

[0014] In a second aspect, an embodiment of the present application provides a speed limit processing device, comprising: an acquisition module, used to acquire multiple lane groups in a target area in a high-precision map, and multiple road lines in a target area in a standard map; a determination module, used to determine, from each sampling point in the multiple road lines, a sampling point that matches a target shape point set, to obtain a target matching point set, the target shape point set includes shape points of each lane line in the target lane group, the target lane group is any one of the multiple lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group; from the multiple sampling points in the target matching point set, determine the sampling points that match each shape point in the target shape point set respectively, to obtain multiple matching point pairs, each matching point pair includes a shape point in the target shape point set, and a sampling point in the target matching point set that is closest to the shape point; determine the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair, so as to determine the speed limit value of each shape point in the target shape point set, the target matching point pair is any one of the multiple matching point pairs.

[0015] In some embodiments of the present application, the determination module is specifically used to determine the convex hull of the target shape point set using a convex hull algorithm to obtain a target polygon composed of the outermost shape points of the target shape point set; perform spatial operations on the target polygon and the sampling point set to obtain the target matching point set, the target matching point set includes each sampling point in the sampling point set that is located within the target polygon, and the sampling point set includes each sampling point in the multiple road lines.

[0016] In some embodiments of the present application, the determination module is specifically used to generate a target road section reference line corresponding to the target shape point set based on the target shape point set, the driving direction of the target road section reference line is the same as the driving direction of the target lane line, and the curve change trend of the target road section reference line is the same as the curve change trend of the target lane line; the target lane line is any lane line in the target lane group; from each sampling point in the multiple road lines, determine the sampling point whose distance to the target road section reference line is less than or equal to the distance threshold, and obtain the target matching point set.

[0017] In some embodiments of the present application, the determination module is specifically used to determine at least one road line that matches the target lane group from multiple road lines, and the road sections indicated by each road line in the at least one road line overlap with the road sections indicated by the target lane group; from each sampling point in the at least one road line, determine the sampling point that matches the target shape point set to obtain the target matching point set.

[0018] In some embodiments of the present application, the determination module is specifically used to calculate the distance between the shape point and each sampling point in the target matching point set to obtain multiple distances corresponding to the shape point; determine the sampling point corresponding to the minimum value of the multiple distances corresponding to the shape point as the sampling point, and obtain a matching point pair including the shape point and the sampling point to obtain multiple matching point pairs.

[0019] In some embodiments of the present application, the determination module is specifically used to group multiple sampling points in the target matching point set according to the driving direction indicated by the corresponding road line to obtain multiple matching point subsets; determine the target matching point subset with the shortest distance from the one shape point from the multiple matching point subsets; determine the one sampling point in the target matching point subset with the shortest distance from the one shape point to obtain a matching point pair including the one shape point and the one sampling point, so as to obtain multiple matching point pairs.

[0020] In some embodiments of the present application, the determination module is specifically used to take the one shape point as a target set, determine the Hausdorff distance between the target set and each matching point subset, and obtain multiple Hausdorff distances; determine the matching point subset corresponding to the minimum value of the multiple Hausdorff distances as the target matching point subset; or, determine the distance between the one shape point and each matching point subset respectively, and obtain multiple target distances corresponding to the one shape point, each target distance is the average value of the distance between the one shape point and each sampling point in the corresponding one matching point subset, or each target distance is the sum of the distance between the one shape point and each sampling point in the corresponding one matching point subset; determine the matching point subset corresponding to the minimum value of the multiple target distances as the target matching point subset.

[0021] In some embodiments of the present application, the determination module is also used to determine the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, and then determine the speed limit value of the shape point and the average of the speed limit values ​​of multiple shape points adjacent to the shape point as the final speed limit value of the shape point to determine the final speed limit value of each shape point in the target shape point set.

[0022] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, wherein the processor is used to execute a computer program stored in a memory, and when the computer program is executed by the processor, the steps of any one of the speed limit processing methods provided in the first aspect are implemented.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the speed limit processing methods provided in the first aspect are implemented.

[0024] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising: the speed limit processing device as described in the second aspect, or the electronic device as described in the third aspect, or the computer-readable storage medium as described in the fourth aspect.

[0025] In a sixth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program or instructions. When the computer program product runs on a processor, the processor executes the computer program or instructions to implement the steps of the speed limit processing method described in the first aspect.

[0026] In the seventh aspect, an embodiment of the present application provides a chip, which includes a processor, a memory and a communication interface, the communication interface is coupled to the processor, the memory is used to store programs or instructions that can be run on the processor, and the processor is used to execute the program or instructions to implement the steps of the speed limit processing method described in the first aspect.

[0027] Compared with the prior art, the technical solution provided by the embodiment of the present application has the following advantages: In the embodiment of the present application, multiple lane groups in the target area in the high-precision map and multiple road lines in the target area in the standard map are obtained; from each sampling point in the multiple road lines, a sampling point matching the target shape point set is determined to obtain a target matching point set (the target shape point set includes the shape points of each lane line in the target lane group, the target lane group is any one of the multiple lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group, that is, a set of sampling points that overlap with the road section indicated by each lane group is found); from multiple sampling points in the target matching point set, a sampling point matching the target shape point set is determined to obtain a target matching point set. In the target shape point set, the sampling points that match each shape point in the target shape point set are determined to obtain multiple matching point pairs (each matching point pair includes a shape point in the target shape point set, and a sampling point in the target matching point set that is closest to the shape point, that is, the sampling point in the target matching point set that is closest to each shape point in the target shape point set is determined); the speed limit value of the sampling point in the target matching point pair is determined as the speed limit value of the shape point in the target matching point pair, so as to determine the speed limit value of each shape point in the target shape point set (the target matching point pair is any one of the multiple matching point pairs, that is, the speed limit value of each shape point in the target lane group is determined). In this way, the speed limit information of the high-precision map is quickly obtained by mapping the speed limit information of the high-precision map through the speed limit information of the standard map, which reduces the complexity of obtaining the speed limit information of the high-precision map, thereby reducing the time consumption and improving the efficiency of obtaining the speed limit information of the high-precision map. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0030] Figure 1 A schematic diagram of a road line and lane group of a road section provided in this application;

[0031] Figure 2 A flow chart of a speed limit processing method provided for this application;

[0032] Figure 3 A flowchart of another speed limit processing method provided for this application;

[0033] Figure 4A flowchart of another speed limit processing method provided by this application;

[0034] Figure 5 A flowchart of another speed limit processing method provided by this application;

[0035] Figure 6 A schematic diagram of the structure of a speed limit processing device provided in this application;

[0036] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0037] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0039] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0040] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0041] Tile: A map tile is a map slice file that contains a series of scales and a certain map range. Map tiles are organized in a pyramid structure, and each tile can be uniquely marked by level, row and column number.

[0042] Map match: Binding a trajectory to a road network is a way of calculating which roads a trajectory passes through in the road network and determining the matching relationship between roads and trajectories.

[0043] The trajectory is the data generated during the vehicle's driving process. The trajectory can be composed of multiple trajectory points, where each trajectory point can include various specific information, such as specific location information, time information, speed information, etc.

[0044] Electronic maps are mainly divided into high-precision maps and standard maps.

[0045] Standard maps mainly provide positioning, retrieval, and path planning functions in non-assisted driving or non-autonomous driving scenarios, and standard maps can achieve full road network coverage. Among them, standard maps are maps composed of topological network elements based on road lines (links), with an accuracy of meters, and only depict the location and shape of roads. Standard maps have relatively low accuracy and can only reach the road level, not the lane level.

[0046] The standard map is represented by standard (SD) road network data. The roads in the standard map are represented by road lines, and each road can be composed of one or more road lines. For example, Figure 1 (a) in the figure shows a schematic diagram of a road in a standard map. A road can be composed of a road line A and a road line B, that is, a road can be divided into multiple sections, each section corresponding to a road line. A road line is composed of sampling points and is the digitized representation of an actual road. Among them, sampling points are points necessary to describe a road line. There are at least 2 sampling points on a road line. When a road line represents a relatively straight road or a relatively straight road section, there can be only 2 sampling points on this road line.

[0047] In this embodiment, the road line can be a line segment with a direction, that is, a vector. The road line data is the data of each sampling point on this road. The road line in the standard map and its corresponding road line data are obtained, that is, the data of each sampling point corresponding to each road line is obtained from the standard road network data corresponding to the standard map.

[0048] A high-precision map is a reconstruction of the road environment. It contains a large amount of information about lane positions, types, widths, and road elements such as traffic lights and traffic signs, forming an accurate three-dimensional representation of the road network. High-precision maps are mainly used for assisted driving or autonomous driving, and they are only for specific areas, not all road networks. Since high-precision maps are based on lane-level topological network elements, compared with standard maps, high-precision maps are more accurate and can reach the lane level. The data dimensions of high-precision maps are also richer, adding detailed information such as lane line type, lane width, and roadside landmarks.

[0049] HD maps are represented by HD road network data. Roads in HD maps are represented by lane groups, and each road can consist of one or more lane groups. Figure 1(b) in the figure is a schematic diagram of a road in a high-precision map. A road can be composed of lane group A and lane group B, that is, a road can be divided into multiple sections, each section corresponds to a lane group. Each lane group includes multiple actual lane lines and at least one virtual lane line. The actual lane line is the lane line actually marked on the road; the virtual lane line is the virtual line between two actual lane lines. The virtual lane line is generally the center line between two actual lane lines. The virtual lane line is generally not marked on the road, but it is necessary to use the virtual lane line in the process of assisted driving or automatic driving. For example, the vehicle needs to drive along the virtual lane line during automatic driving, so the virtual lane line is included in the lane group of the high-precision map.

[0050] Both the actual lane line and the virtual lane line are composed of some densely distributed points that describe the shape of the corresponding lane line. The points on the lane line are generally called shape points. In the high-precision map, adjacent lane groups are separated by lane group connecting lines. A lane group is between two adjacent lane group connecting lines. Each actual lane line and each virtual lane line in the lane group is a directional vector, and the actual lane line and the virtual lane line in the same lane have the same direction, which is the same as the direction in which vehicles are allowed to travel in the lane. Lane line data is the data of each shape point in the lane. The lane group in the high-precision map and its corresponding lane group data are obtained, that is, the data of the actual lane line shape points and the data of the virtual lane line shape points in each lane group are obtained from the high-precision road network data corresponding to the high-precision map. For example, if there are three lanes on a highway, each lane group in the corresponding high-precision map includes 4 actual lane lines and 3 virtual lane lines, wherein the 4 actual lane lines are used to distinguish the three lanes, and a virtual lane line is also included between the two actual lane lines of each lane. For example, if the three lanes on a highway are divided into sections of 50 meters, then the four actual lane lines and three virtual lane lines within 50 meters form a lane group. Figure 1 Lane group A and lane group B shown in (b) of FIG. In the same road, the road line and the lane group may not be completely matched. The length of the road segment indicated by the road line is usually longer than the length of the road segment indicated by the lane group. A lane group may correspond to one road line or two road lines (a part of the lane group corresponds to one of the road lines, and the other part of the lane group corresponds to the other road line).

[0051] Currently, the speed limit information of high-precision maps is obtained through manual independent surveying and marking. However, this method has the disadvantages of high cost and large workload.

[0052] The present application is applied to the scenario of obtaining speed limit data of high-precision maps. The electronic device in the embodiment of the present application can be a vehicle-mounted terminal, or a mobile phone, notebook, computer, etc. that is communicatively connected to the vehicle. The specific details can be determined based on actual conditions and are not limited here.

[0053] The technical solution of the present application is explained in detail below through several specific embodiments.

[0054] Figure 2 A flow chart of a speed limit processing method provided for this application is shown as follows: Figure 2 As shown, the speed limit processing method may include the following steps 101 to 104.

[0055] 101. Obtain multiple lane groups in the target area in the high-precision map and multiple road lines in the target area in the standard map.

[0056] The target area may be a tile in the map, or an area where a province is located, or an area where a highway is located. The specific area may be determined based on actual conditions and is not limited here.

[0057] It can be understood that the standard road network data corresponding to the target area is obtained from the standard road network data corresponding to the standard map, and then the standard road network data corresponding to the target area is split into units of road lines to obtain data of each road line in multiple road lines, that is, data of each sampling point corresponding to each road line.

[0058] It can be understood that the high-precision road network data corresponding to the target area is obtained from the high-precision road network data corresponding to the high-precision map, and then the high-precision road network data corresponding to the target area is split into lane groups to obtain the data of each lane group in the multiple lane groups, that is, the data of each shape point of each lane line (actual lane line and virtual lane line) corresponding to each lane group.

[0059] In some embodiments of the present application, when the coordinate system of the standard map is consistent with the coordinate system of the high-precision map, the above step 101 is directly executed; when the coordinate system of the standard map is inconsistent with the coordinate system of the high-precision map, before directly executing the above step 101, the high-precision map and the standard map are spatially aligned to align the high-precision map and the standard map to the same coordinate system. The specific alignment process refers to the existing related technology and is not limited here.

[0060] 102. From each sampling point in the plurality of road lines, determine a sampling point that matches a target shape point set to obtain a target matching point set, wherein the target shape point set includes shape points of each lane line in a target lane group, the target lane group is any one of the plurality of lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group.

[0061] It can be understood that based on the data of each road line in the multiple road lines and the data of each lane group in the multiple lane groups, a target matching point set corresponding to each sampling point matching the target shape point set corresponding to the target lane group is determined.

[0062] It should be noted that, for each lane group, the sampling point set matching the shape point set corresponding to the lane group is determined according to the above method (the method for determining the target matching point set matching the target shape point set corresponding to the target lane group), specifically, based on the data of each road line in the multiple road lines and the data of each lane group in the multiple lane groups, the set of each sampling point matching the shape point set (such as the target shape point set) corresponding to each lane group (such as the target lane group) is determined (such as the target matching point set). The road section indicated by the shape point set of a lane group overlaps with the road section indicated by the set of sampling points matching the lane group (such as the road section indicated by the target shape point set overlaps with the road section indicated by the target matching point set).

[0063] 103. Determine, from the multiple sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set to obtain multiple matching point pairs, each matching point pair including a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point.

[0064] It can be understood that each shape point in the target shape point set corresponding to the target lane group is matched with each sampling point in the target matching point set to obtain a plurality of matching point pairs.

[0065] Among them, each matching point pair corresponds to each shape point in the target shape point set one by one, and the shape points in different matching point pairs are different, but the sampling points may be the same or different. That is to say, in multiple matching point pairs, one shape point matches one sampling point, but one sampling point may match one shape point or multiple shape points.

[0066] Each sampling point in the target matching point set may have a matching shape point, or some sampling points in the target matching point set may have matching shape points and some sampling points may not have matching shape points, which is not limited here.

[0067] 104. Determine the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, wherein the target matching point pair is any one of the multiple matching point pairs.

[0068] The speed limit value of each shape point in the target shape point set is determined according to the method described in step 104 above. Similarly, the speed limit value of each shape point in the shape point set corresponding to each lane group in the target area is also determined according to the method described in steps 102 to 104 above.

[0069] In an embodiment of the present application, multiple lane groups in a target area in a high-precision map and multiple road lines in a target area in a standard map are obtained; from each sampling point in the multiple road lines, a sampling point that matches the target shape point set is determined to obtain a target matching point set (the target shape point set includes shape points of each lane line in the target lane group, the target lane group is any one of the multiple lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group, that is, a set of sampling points that overlap with the road section indicated by each lane group is found); from the multiple sampling points in the target matching point set, a sampling point that matches the target shape point set is determined. The sampling points that match each shape point in the target shape point set are respectively matched to obtain multiple matching point pairs (each matching point pair includes a shape point in the target shape point set, and a sampling point in the target matching point set that is closest to the shape point, that is, the sampling point in the target matching point set that is closest to each shape point in the target shape point set is determined); the speed limit value of the sampling point in the target matching point pair is determined as the speed limit value of the shape point in the target matching point pair, so as to determine the speed limit value of each shape point in the target shape point set (the target matching point pair is any one of the multiple matching point pairs, that is, the speed limit value of each shape point in the target lane group is determined). In this way, the speed limit information of the high-precision map is quickly obtained by mapping the speed limit information of the standard map to the speed limit information of the high-precision map, which reduces the complexity of obtaining the speed limit information of the high-precision map, thereby reducing the time consumption and improving the efficiency of obtaining the speed limit information of the high-precision map.

[0070] In some embodiments of the present application, Figure 2 ,like Figure 3 As shown, the above step 102 can be specifically implemented through the following steps 102a and 102b.

[0071] 102a. Determine the convex hull of the target shape point set using a convex hull algorithm to obtain a target polygon composed of the outermost shape points of the target shape point set.

[0072] The convex hull is a concept relative to a point set. For a certain point set, the convex hull is a subset of some of the points in it. The points in this subset form a convex polygon that completely surrounds all the points in the point set. There is a vivid metaphor about the convex hull: regard the points in the point set as nails, and use a rubber band to cover all the nails. In the end, the rubber band is a convex hull, and the nail that tightens the rubber band is the vertex in the convex hull.

[0073] Currently, two common methods are Graham scanning method and Andrew algorithm. For details, please refer to the existing related technologies and are not limited here.

[0074] 102b. Perform spatial operation on the target polygon and the sampling point set to obtain the target matching point set.

[0075] The target matching point set includes each sampling point in the sampling point set that is located within the target polygon, and the sampling point set includes each sampling point in the multiple road lines.

[0076] The spatial operation is an operation to determine whether a point is within a spatial polygon, and may be, for example, a crossing number algorithm, a winding number algorithm, etc., which are not limited here.

[0077] In the embodiment of the present application, the target matching point set that matches the target shape point set can be quickly determined through the convex hull algorithm and the spatial algorithm, thereby improving the efficiency of obtaining the speed limit information of the high-precision map.

[0078] In some embodiments of the present application, the above step 102 can be specifically implemented through the following steps 102c and 102d.

[0079] 102c. Based on the target shape point set, generate a target road section reference line corresponding to the target shape point set.

[0080] Among them, the driving direction of the target road section reference line is the same as the driving direction of the target lane line, and the curve change trend of the target road section reference line is the same as the curve change trend of the target lane line; the target lane line is any lane line in the target lane group.

[0081] Exemplarily, the target road section reference line can be any lane line in the target lane group (actual lane line or virtual lane line), or it can be any lane line in the target lane group within the road section indicated by the target lane group, with the same driving direction as the target lane line and the same curve change trend as the target lane line. No limitation is made here.

[0082] Among them, the target lane line can be any lane line in the target lane group (actual lane line or virtual lane line), which is not limited here.

[0083] 102d. From each sampling point in the plurality of road lines, determine a sampling point whose distance to the reference line of the target road section is less than or equal to a distance threshold, and obtain the target matching point set.

[0084] The distance threshold may be determined based on actual conditions and is not limited here.

[0085] It can be understood that the road section indicated by the target matching point set determined according to the above steps 102c and 102d may completely overlap with the road section indicated by the target shape point set, or may partially overlap with the road section indicated by the target shape point set (but in this case, all the sampling points of the road line corresponding to the road section indicated by the target shape point set are in the target matching point set).

[0086] In an embodiment of the present application, by first generating a target road section reference line corresponding to the target shape point set, and then determining, from each sampling point in the multiple road lines, a sampling point whose distance to the target road section reference line is less than or equal to a distance threshold, to obtain the target matching point set, the target matching point set that matches the target shape point set can be quickly determined, thereby improving the efficiency of obtaining speed limit information of the high-precision map.

[0087] In some embodiments of the present application, the above step 102 can be specifically implemented through the following steps 102e to 102f.

[0088] 102e. Determine at least one road line that matches the target lane group from the multiple road lines, wherein the road sections indicated by each road line in the at least one road line overlap with the road sections indicated by the target lane group.

[0089] It can be understood that, because in general, the length of the road section indicated by the lane line is longer than the length of the road section indicated by the lane group, in general, the at least one road line that matches the target lane group is one road line or two road lines. If there is one at least one road line that matches the target lane group, the road section indicated by the one road line includes the road section indicated by the target lane group; if there are two at least one road lines that match the target lane group, the road sections indicated by the two road lines include the road section indicated by the target lane group, and the two road lines are two adjacent road lines.

[0090] 102f. Determine, from each sampling point in at least one road line, a sampling point that matches the target shape point set to obtain a target matching point set.

[0091] For the specific implementation method of step 102f, reference may be made to the relevant descriptions of the above steps 102a and 102b or the above steps 102c and 102d, which will not be repeated here.

[0092] In an embodiment of the present application, by first determining at least one road line that matches the target lane group from multiple road lines, and then determining a sampling point that matches the target shape point set from each sampling point in the at least one road line, a target matching point set is obtained. This can reduce the amount of computation, thereby quickly determining the target matching point set that matches the target shape point set, and further improving the efficiency of acquiring speed limit information of the high-precision map.

[0093] In some embodiments of the present application, the above step 103 can be specifically implemented through the following steps 103a and 103b.

[0094] 103a. Calculate the distance between the one shape point and each sampling point in the target matching point set to obtain multiple distances corresponding to the one shape point.

[0095] 103b. Determine the sampling point corresponding to the minimum value among the multiple distances corresponding to the one shape point as the one sampling point, and obtain a matching point pair including the one shape point and the one sampling point, so as to obtain multiple matching point pairs.

[0096] In an embodiment of the present application, by calculating the distance between each shape point in the target shape point set and each sampling point in the target matching point set, and then determining each shape point and the sampling point with the smallest distance to each shape point as a matching point pair, multiple matching point pairs can be quickly determined, thereby improving the efficiency of obtaining speed limit information of the high-precision map.

[0097] In some embodiments of the present application, Figure 3 ,like Figure 4 As shown, the above step 103 can be specifically implemented through the following steps 103c to 103e.

[0098] 103c. Grouping the multiple sampling points in the target matching point set according to the driving directions indicated by the corresponding road lines to obtain multiple matching point subsets.

[0099] Each matching point subset includes at least one sampling point among a plurality of sampling points in the target matching point set.

[0100] In some embodiments of the present application, multiple sampling points in the target matching point set may be evenly grouped according to the driving direction indicated by the corresponding road line, with the number of sampling points included in each matching point subset being the same, to obtain multiple matching point subsets.

[0101] In some embodiments of the present application, multiple sampling points in the target matching point set can be grouped according to the driving direction indicated by the corresponding road line and the length of the road section indicated by the sampling points included in each matching point subset as a standard to obtain multiple matching point subsets.

[0102] 103d. Determine, from the multiple matching point subsets, a target matching point subset having the shortest distance from the shape point.

[0103] 103e. Determine the sampling point in the target matching point subset that has the shortest distance from the shape point, and obtain a matching point pair including the shape point and the sampling point, so as to obtain a plurality of matching point pairs.

[0104] In an embodiment of the present application, multiple sampling points in the target matching point set are first divided into multiple matching point subsets, and then the matching point subset with the shortest distance from each shape point is determined, and finally each shape point and the sampling point with the shortest distance in the matching point subset corresponding to each shape point are determined as a matching point pair, which saves computing power and improves the efficiency of determining matching point pairs.

[0105] In some embodiments of the present application, Figure 4 ,like Figure 5 As shown, the above step 103d can be specifically implemented through the following steps 103d1 and 103d2.

[0106] 103d1. Take the shape point as a target set, determine the Hausdorff distance between the target set and each matching point subset, and obtain multiple Hausdorff distances.

[0107] 103d2. Determine the matching point subset corresponding to the minimum value among the multiple Hausdorff distances as the target matching point subset.

[0108] The Hausdorff distance is the largest of all distances from each point in a set to the nearest point in another set. In the embodiment of the present application, since there is only one shape point in the target set, the Hausdorff distance between the target set and each matching point subset is the distance from the one shape point to the nearest sampling point in each matching point subset (i.e., the minimum value of the distance from the one shape point to each sampling point in each matching point subset).

[0109] In the embodiment of the present application, the amount of calculation can be reduced by using the Hausdorff distance, and the target matching point subset corresponding to the shape point can be quickly determined, thereby improving the efficiency of obtaining the speed limit information of the high-precision map.

[0110] In some embodiments of the present application, the above step 103d can be specifically implemented through the following steps 103d3 and 103d4.

[0111] 103d3. Determine the distance between the one shape point and each matching point subset, and obtain multiple target distances corresponding to the one shape point.

[0112] Each target distance is an average value of the distances between the shape point and each sampling point in a corresponding matching point subset, or each target distance is a sum of the distances between the shape point and each sampling point in a corresponding matching point subset.

[0113] 103d4. Determine the matching point subset corresponding to the minimum value among the multiple target distances as the target matching point subset.

[0114] In an embodiment of the present application, the average value of the distance between the shape point and each sampling point in the corresponding matching point subset, or the sum of the distance between the shape point and each sampling point in the corresponding matching point subset, is used as the distance between the shape point and each matching point subset. This allows the target matching point subset corresponding to the shape point to be quickly determined, thereby improving the efficiency of acquiring speed limit information of the high-precision map.

[0115] In some embodiments of the present application, after the above step 104, the speed limit processing method provided by the embodiment of the present application may further include the following step 105.

[0116] 105. Determine the speed limit value of the shape point and the average of the speed limit values ​​of multiple shape points adjacent to the shape point as the final speed limit value of the shape point, so as to determine the final speed limit value of each shape point in the target shape point set.

[0117] Among them, the multiple shape points adjacent to the one shape point may be a shape point located before the one shape point and a shape point located after the one shape point in the driving direction of the target lane group, or may be at least two shape points located before the one shape point in the driving direction of the target lane group, or may be at least two shape points located after the one shape point in the driving direction of the target lane group, or may be at least one shape point located before the one shape point and at least one shape point located after the one shape point in the driving direction of the target lane group. The specific shape points can be determined according to actual conditions and are not limited here.

[0118] In the embodiment of the present application, since the speed limit values ​​corresponding to multiple adjacent shape points are generally not much different and generally do not fluctuate back and forth, the speed limit value of one shape point and the average of the speed limit values ​​of multiple shape points adjacent to the one shape point are determined as the final speed limit value of the one shape point, thereby achieving smoothing of the speed limit value of one shape point, which can improve the accuracy of the speed limit information of the high-precision map.

[0119] The present application also provides a speed limit processing device, Figure 6 A schematic diagram of the structure of a speed limit processing device provided in this application, such as Figure 6As shown, the speed limit processing device includes: an acquisition module 601, which is used to acquire multiple lane groups in a target area in a high-precision map and multiple road lines in a target area in a standard map; a determination module 602, which is used to determine, from each sampling point in the multiple road lines, a sampling point that matches a target shape point set to obtain a target matching point set, the target shape point set includes shape points of each lane line in the target lane group, the target lane group is any one of the multiple lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group; from the multiple sampling points in the target matching point set, determine the sampling points that match each shape point in the target shape point set respectively to obtain multiple matching point pairs, each matching point pair includes a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point; determine the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair, so as to determine the speed limit value of each shape point in the target shape point set, the target matching point pair is any one of the multiple matching point pairs.

[0120] In some embodiments of the present application, the determination module 602 is specifically used to determine the convex hull of the target shape point set using a convex hull algorithm to obtain a target polygon composed of the outermost shape points of the target shape point set; perform spatial operations on the target polygon and the sampling point set to obtain the target matching point set, the target matching point set includes each sampling point in the sampling point set that is located within the target polygon, and the sampling point set includes each sampling point in the multiple road lines.

[0121] In some embodiments of the present application, the determination module 602 is specifically used to generate a target road section reference line corresponding to the target shape point set based on the target shape point set, the driving direction of the target road section reference line is the same as the driving direction of the target lane line, and the curve change trend of the target road section reference line is the same as the curve change trend of the target lane line; the target lane line is any lane line in the target lane group; from each sampling point in the multiple road lines, determine the sampling point whose distance to the target road section reference line is less than or equal to the distance threshold, and obtain the target matching point set.

[0122] In some embodiments of the present application, the determination module 602 is specifically used to determine at least one road line that matches the target lane group from multiple road lines, and the road sections indicated by each road line in the at least one road line overlap with the road sections indicated by the target lane group; from each sampling point in the at least one road line, determine the sampling point that matches the target shape point set to obtain the target matching point set.

[0123] In some embodiments of the present application, the determination module 602 is specifically used to calculate the distance between the one shape point and each sampling point in the target matching point set to obtain multiple distances corresponding to the one shape point; determine the sampling point corresponding to the minimum value of the multiple distances corresponding to the one shape point as the one sampling point, and obtain a matching point pair including the one shape point and the one sampling point to obtain multiple matching point pairs.

[0124] In some embodiments of the present application, the determination module 602 is specifically used to group multiple sampling points in the target matching point set according to the driving direction indicated by the corresponding road line to obtain multiple matching point subsets; determine the target matching point subset with the shortest distance from the one shape point from the multiple matching point subsets; determine the one sampling point in the target matching point subset with the shortest distance from the one shape point to obtain a matching point pair including the one shape point and the one sampling point, so as to obtain multiple matching point pairs.

[0125] In some embodiments of the present application, the determination module 602 is specifically used to take the one shape point as a target set, determine the Hausdorff distance between the target set and each matching point subset, and obtain multiple Hausdorff distances; determine the matching point subset corresponding to the minimum value of the multiple Hausdorff distances as the target matching point subset; or, determine the distance between the one shape point and each matching point subset respectively, and obtain multiple target distances corresponding to the one shape point, each target distance is the average value of the distance between the one shape point and each sampling point in the corresponding one matching point subset, or each target distance is the sum of the distance between the one shape point and each sampling point in the corresponding one matching point subset; determine the matching point subset corresponding to the minimum value of the multiple target distances as the target matching point subset.

[0126] In some embodiments of the present application, the determination module 602 is also used to determine the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, and then determine the speed limit value of the shape point and the average of the speed limit values ​​of multiple shape points adjacent to the shape point as the final speed limit value of the shape point to determine the final speed limit value of each shape point in the target shape point set.

[0127] It should be noted that the above-mentioned speed limit processing device can be the electronic device in the above-mentioned method embodiment of the present application, or it can be a functional module and / or functional entity in the electronic device that can implement the functions of the device embodiment, and the embodiment of the present application is not limited.

[0128] In the embodiments of the present application, each module can implement the speed limit processing method provided in the above method embodiments and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0129] Figure 7 The structural diagram of an electronic device provided in the embodiment of the present application is used to exemplarily illustrate the electronic device that implements any speed limit processing method in the embodiment of the present application, and should not be understood as a specific limitation on the embodiment of the present application.

[0130] like Figure 7 As shown, the electronic device 700 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0131] Typically, the following devices may be connected to the I / O interface 705: input devices 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 708 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 700 having various devices is shown, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0132] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processor 701, the functions defined in any speed limit processing method provided in the embodiment of the present application can be executed.

[0133] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0134] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0135] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0136] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains multiple lane groups in the target area in the high-precision map and multiple road lines in the target area in the standard map; determines a sampling point that matches a target shape point set from each sampling point in the multiple road lines to obtain a target matching point set, the target shape point set includes shape points of each lane line in the target lane group, the target lane group is any one of the multiple lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group; determines a sampling point that matches each shape point in the target shape point set from multiple sampling points in the target matching point set to obtain multiple matching point pairs, each matching point pair includes a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point; determines the speed limit value of the sampling point in the target matching point pair as the speed limit value of the shape point in the target matching point pair, so as to determine the speed limit value of each shape point in the target shape point set, the target matching point pair is any one of the multiple matching point pairs.

[0137] In an embodiment of the present application, a computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include but are not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed completely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0138] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The units involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0140] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0141] In the context of the present application, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0142] An embodiment of the present application further provides a vehicle, comprising: the above-mentioned speed limit processing device, or the above-mentioned electronic device, or the above-mentioned computer-readable storage medium.

[0143] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0144] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0145] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A speed limit processing method, characterized in that: The method comprises: Acquire multiple lane groups in a target area in a high-precision map and multiple road lines in the target area in a standard map; Determine, from each sampling point in the plurality of road lines, a sampling point that matches a target shape point set to obtain a target matching point set, wherein the target shape point set includes shape points of each lane line in a target lane group, the target lane group is any one of the plurality of lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group; Determine, from a plurality of sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set, to obtain a plurality of matching point pairs, each matching point pair comprising a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point; The speed limit value of one sampling point in the target matching point pair is determined as the speed limit value of one shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, and the target matching point pair is any one of the multiple matching point pairs.

2. The method according to claim 1, characterized in that The step of determining, from each sampling point in the plurality of road lines, a sampling point that matches a target shape point set to obtain a target matching point set comprises: Determine the convex hull of the target shape point set using a convex hull algorithm to obtain a target polygon composed of the outermost shape points of the target shape point set; The target polygon and the sampling point set are spatially operated to obtain the target matching point set, wherein the target matching point set includes each sampling point in the sampling point set that is located within the target polygon, and the sampling point set includes each sampling point in the multiple road lines.

3. The method according to claim 1, characterized in that The step of determining, from each sampling point in the plurality of road lines, a sampling point that matches a target shape point set to obtain a target matching point set comprises: Based on the target shape point set, a target road section reference line corresponding to the target shape point set is generated, the driving direction of the target road section reference line is the same as the driving direction of the target lane line, and the curve change trend of the target road section reference line is the same as the curve change trend of the target lane line; the target lane line is any lane line in the target lane group; From each sampling point in the plurality of road lines, a sampling point whose distance to the target road section reference line is less than or equal to a distance threshold is determined to obtain the target matching point set.

4. The method according to claim 1, characterized in that: The step of determining, from each sampling point in the plurality of road lines, a sampling point that matches a target shape point set to obtain a target matching point set comprises: Determine at least one road line matching the target lane group from the plurality of road lines, wherein a road section indicated by each road line in the at least one road line overlaps with a road section indicated by the target lane group; From each sampling point in the at least one road line, a sampling point matching the target shape point set is determined to obtain the target matching point set.

5. The method according to claim 1, characterized in that The step of determining, from the plurality of sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set to obtain a plurality of matching point pairs comprises: Calculating the distance between the one shape point and each sampling point in the target matching point set to obtain a plurality of distances corresponding to the one shape point; A sampling point corresponding to a minimum value among a plurality of distances corresponding to the one shape point is determined as the one sampling point, and a matching point pair including the one shape point and the one sampling point is obtained to obtain the plurality of matching point pairs.

6. The method according to claim 1, characterized in that The step of determining, from the plurality of sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set to obtain a plurality of matching point pairs comprises: Grouping multiple sampling points in the target matching point set according to the driving directions indicated by the corresponding road lines to obtain multiple matching point subsets; Determine, from the plurality of matching point subsets, a target matching point subset with the shortest distance from the one shape point; The one sampling point in the target matching point subset having the shortest distance from the one shape point is determined to obtain a matching point pair including the one shape point and the one sampling point, so as to obtain the multiple matching point pairs.

7. The method according to claim 6, characterized in that The step of determining, from the plurality of matching point subsets, a target matching point subset with the smallest distance from the one shape point comprises: Taking the one shape point as a target set, determining the Hausdorff distance between the target set and each of the matching point subsets, and obtaining a plurality of Hausdorff distances; Determine the matching point subset corresponding to the minimum value among the multiple Hausdorff distances as the target matching point subset; or, Determine the distance between the one shape point and each matching point subset, respectively, to obtain a plurality of target distances corresponding to the one shape point, each target distance being an average value of the distances between the one shape point and each sampling point in a corresponding matching point subset, or each target distance being a sum of the distances between the one shape point and each sampling point in a corresponding matching point subset; A matching point subset corresponding to a minimum value among the multiple target distances is determined as the target matching point subset.

8. The method according to any one of claims 1 to 7, characterized in that After determining the speed limit value of one sampling point in the target matching point pair as the speed limit value of one shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, the method further includes: The speed limit value of the one shape point and the average of the speed limit values ​​of multiple shape points adjacent to the one shape point are determined as the final speed limit value of the one shape point to determine the final speed limit value of each shape point in the target shape point set.

9. A speed limit processing device, characterized in that: include: An acquisition module, used to acquire multiple lane groups in a target area in a high-precision map, and multiple road lines in the target area in a standard map; A determination module, configured to determine, from each sampling point in the plurality of road lines, a sampling point that matches a target shape point set, to obtain a target matching point set, wherein the target shape point set includes shape points of each lane line in a target lane group, the target lane group is any one of the plurality of lane groups, and the road section indicated by the target matching point set includes the road section indicated by the target lane group; Determine, from a plurality of sampling points in the target matching point set, sampling points that respectively match each shape point in the target shape point set, to obtain a plurality of matching point pairs, each matching point pair comprising a shape point in the target shape point set and a sampling point in the target matching point set that is closest to the shape point; The speed limit value of one sampling point in the target matching point pair is determined as the speed limit value of one shape point in the target matching point pair to determine the speed limit value of each shape point in the target shape point set, and the target matching point pair is any one of the multiple matching point pairs.

10. An electronic device, characterized in that: include: A processor, the processor is used to execute a computer program stored in a memory, and the computer program, when executed by the processor, implements the steps of the speed limit processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the speed limit processing method according to any one of claims 1 to 8 are implemented.

12. A vehicle, characterized in that: include: The speed limit processing device as claimed in claim 9, or the electronic device as claimed in claim 10, or includes the computer-readable storage medium as claimed in claim 11.

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