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, and the method comprises the steps: carrying out the matching of a plurality of road lines and a plurality of tracks in a target region, and determining at least one target track segment matched with a target road line; determining at least one matched sampling point matched with each track point from a plurality of sampling points of the target road line to obtain a plurality of matching relationships; determining a speed limit value of the to-be-processed sampling point according to the speed value of at least one target track point matched with the to-be-processed sampling point based on the plurality of matching relationships; determining sampling points matched with the target shape point set from each sampling point of the plurality of road lines to obtain a target matching point set; 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 one sampling point in the target matching point pair as the speed limit value of one shape point in the target matching point pair.

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 cost of obtaining speed limit information of high-precision maps and improve 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: performing trajectory matching road network processing on multiple road lines in a target area and multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, wherein the distance between any trajectory point on each trajectory segment in at least one trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold; taking one road line among the multiple road lines as the target road line, and determining at least one target trajectory segment matching the target road line; for each trajectory point in at least one target trajectory segment, determining at least one matching sampling point matching each trajectory point from multiple sampling points in the target road line, and obtaining multiple matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point matching the target trajectory point, the distance between the matching sampling point in the matching relationship and the target trajectory point is less than the distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in at least one target trajectory segment; based on the multiple matching relationships, according to at least one target trajectory point matching the sampling point to be processed speed value, determine the speed limit value of the sampling point to be processed, so as to determine the speed limit value of each sampling point in the multiple road lines, and the sampling point to be processed is any one of the multiple sampling points in the target road line; obtain multiple lane groups in the target area in the high-precision map; from each sampling point in the multiple road lines, determine the sampling point that matches the target shape point set 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; 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, and 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 a sampling point in the target matching point pair as the speed limit value of a 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, and the target matching point pair is any one of the multiple matching point pairs.

[0007] In a second aspect, an embodiment of the present application provides a speed limit processing device, including: a matching module, used to perform trajectory matching road network processing on multiple road lines in a target area and multiple trajectories in the target area, and obtain at least one trajectory segment matching each road line, wherein the distance between any trajectory point on each trajectory segment in at least one trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold; a determination module, used to take one of the multiple road lines as the target road line, and determine at least one target trajectory segment matching the target road line; for each trajectory point in at least one target trajectory segment, at least one matching sampling point matching each trajectory point is determined from multiple sampling points in the target road line, respectively, to obtain multiple matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point matching the target trajectory point, the distance between the matching sampling point in the matching relationship and the target trajectory point is less than the distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in at least one target trajectory segment; based on multiple matching relationships, according to at least one target trajectory point matching the sampling point to be processed The speed value of the sampling point to be processed is used to determine the speed limit value of each sampling point in the multiple road lines, and the sampling point to be processed is any one of the multiple sampling points in the target road line; the acquisition module is used to obtain multiple lane groups in the target area in the high-precision map; the speed limit module is used to determine the sampling points that match the target shape point set from each sampling point in the multiple road lines to obtain the 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 A road section including a target lane group indication; from a plurality of sampling points in a target matching point set, sampling points respectively matching each shape point in a target shape point set are determined to obtain a plurality of 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; a speed limit value of a sampling point in the target matching point pair is determined as a speed limit value of a shape point in the target matching point pair to determine a speed limit value of each shape point in the target shape point set, and the target matching point pair is any one of the plurality of matching point pairs.

[0008] 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.

[0009] 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.

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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, firstly, by performing trajectory matching road network processing on multiple road lines in the target area and multiple trajectories in the target area, at least one trajectory segment matching each road line is obtained (wherein, each trajectory segment belongs to a different trajectory, and the distance between any trajectory point on each trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold); one road line among the multiple road lines is taken as the target road line, and at least one target trajectory segment matching the target road line is determined; for each trajectory point in the at least one target trajectory segment, at least one matching sampling point matching each trajectory point is determined from the multiple sampling points in the target road line, and multiple matching relationships are obtained (each matching relationship includes the target trajectory point and at least one matching sampling point matching the target trajectory point); point, the distance between each matching sampling point and the target trajectory point is less than the distance between the sampling point that does not match the target trajectory point among the multiple sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in at least one target trajectory segment); based on multiple matching relationships, according to the speed value of at least one target trajectory point that matches the sampling point to be processed (any one of the multiple sampling points in the target road line), the speed limit value of the sampling point to be processed is determined (because each matching relationship includes the target trajectory point and at least one matching sampling point that matches the target trajectory point, therefore, taking the sampling point to be processed as a unit, at least one target trajectory point that matches the sampling point to be processed can be determined from the multiple matching relationships, and then according to the speed value of the at least one target trajectory point, the speed limit value of the sampling point to be processed is determined), so as to determine the speed limit value of each sampling point in the multiple road lines. Then, multiple lane groups in the target area in the high-precision 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 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 the multiple sampling points in the target matching point set, a sampling point that matches each shape point in the target shape point set is determined. sampling points, and 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, determining the sampling point in the target matching point set that is closest to each shape point in the target shape point set); 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 (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 value of the sampling point of the standard map is first mapped by the speed value of the trajectory point on the historical driving trajectory, and then the speed limit information of the high-precision map is mapped by the speed limit information of the standard map. The speed limit information of the high-precision map is quickly obtained, 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

[0014] 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.

[0015] 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.

[0016] Figure 1 A schematic diagram of a road line and lane group of a road section provided in this application;

[0017] Figure 2 A flow chart of a speed limit processing method provided for this application;

[0018] Figure 3 A schematic diagram of matching a road line and a track segment provided in this application;

[0019] Figure 4 A flowchart of another speed limit processing method provided by this application;

[0020] Figure 5 A flowchart of another speed limit processing method provided by this application;

[0021] Figure 6 A flowchart of another speed limit processing method provided by this application;

[0022] Figure 7 A flowchart of another speed limit processing method provided by this application;

[0023] Figure 8 A schematic diagram of the structure of a speed limit processing device provided in this application;

[0024] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0025] 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.

[0026] 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.

[0027] 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.

[0028] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Electronic maps are mainly divided into high-precision maps and standard maps.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] HD maps are represented by HD road network data. Roads in HD maps are represented by lane groups, and each road can be composed 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.

[0038] 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).

[0039] In the embodiment of the present application, it is assumed that most users will not drive in violation of traffic rules and exceed the speed limit corresponding to the road. Therefore, the speed limit value of the sampling point of the standard map is mapped by the speed value of the track point on the historical driving track, which is actually the empirical speed limit, that is, the speed limit of the road network of the standard map is matched according to the speed limit experience of most users. For example, if most users on a road drive on this road at a speed value interval, then a speed value in this speed value interval can be determined as the speed limit value of this road, and then the speed limit data of the road network of the standard map can be obtained.

[0040] 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.

[0041] The present application is applied to the scenario of obtaining speed limit information 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 information can be determined based on actual conditions and is not limited here.

[0042] The technical solution of the present application is explained in detail below through several specific embodiments.

[0043] 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 and steps 201 to 204.

[0044] 101. Perform trajectory matching road network processing on multiple road lines and multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, wherein a distance between any trajectory point on each trajectory segment in the at least one trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold.

[0045] The multiple tracks of the target area may be all tracks of the target area or part of tracks of the target area, which is not limited here. The multiple road lines of the target area may be all road lines in the target area.

[0046] 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.

[0047] A trajectory is a historical driving trajectory of a user's vehicle (or social vehicle) collected. A trajectory consists of multiple trajectory points. The trajectory is represented by trajectory data. The trajectory data corresponding to a trajectory includes the data of each trajectory point in the trajectory (including the speed limit value of the trajectory point) and the length data of the trajectory.

[0048] For example, before determining at least one trajectory segment matching a road line, at least one trajectory matching the road line may be determined first, and then at least one trajectory segment matching the road line may be determined from the at least one trajectory matching the road line. The at least one trajectory matching the road line is a trajectory or multiple trajectories in the target area, the indicated road segment of which overlaps with the road segment indicated by the road line.

[0049] For example, based on the longitude and latitude information in the data corresponding to the target road line and the longitude and latitude information in the data corresponding to each trajectory, it can be determined whether the section indicated by the target road line overlaps with the section indicated by a trajectory, and then the trajectory with the overlapping indicated section is determined as the target trajectory that matches the target road line.

[0050] Exemplarily, it is also possible to determine whether the section indicated by the target road line overlaps with the section indicated by a trajectory based on the section identifier in the data corresponding to the target road line and the section identifier in the data corresponding to each trajectory, and then determine the trajectory with the overlapping indicated section as the target trajectory that matches the target road line.

[0051] In some embodiments of the present application, the above step 101 can be specifically implemented through the following steps 101a to 101c.

[0052] 101a. Obtain standard road network data corresponding to a plurality of road lines within a target area from standard road network data corresponding to a standard map.

[0053] The specific method of obtaining the standard road network data in the target area from the standard road network data corresponding to the standard map can refer to the existing related technologies and is not limited here.

[0054] Exemplarily, the standard road network data corresponding to the standard map is filtered according to the spatial boundary corresponding to the target area. The filtering method is to generate a regional spatial polygon according to the spatial boundary of the target area, filter the road lines that are completely within the regional spatial polygon and the road lines that are partially within the regional spatial polygon, and obtain the standard road network data corresponding to multiple road lines. Then, the standard road network data corresponding to the target area is split into road lines as units to obtain the data of each road line in the multiple road lines, that is, the data of each sampling point corresponding to each road line.

[0055] 101b. Acquire trajectory data corresponding to a plurality of trajectories in the target area from a vehicle driving trajectory database, wherein the vehicle driving trajectory database includes vehicle driving trajectory data in the target area and other areas.

[0056] Among them, the specific method of obtaining the trajectory data corresponding to multiple trajectories in the target area from the vehicle driving trajectory database can refer to the existing related technology, which is not limited here.

[0057] Exemplarily, the trajectory data in the vehicle driving trajectory database is filtered according to the spatial boundary corresponding to the target area. The filtering method is to generate a regional spatial polygon according to the spatial boundary of the target area, filter the trajectories that are completely within the spatial polygon and the trajectories that are partially within the spatial polygon, and obtain the trajectory data corresponding to multiple trajectories. Then, the trajectory data corresponding to the multiple trajectories in the target area are split into trajectories as units to obtain the data of each trajectory in the multiple trajectories, that is, the data of each trajectory point corresponding to each trajectory.

[0058] 101c. Performing trajectory matching road network processing on the road network data corresponding to the multiple road lines in the target area and the trajectory data corresponding to the multiple trajectories in the target area to obtain trajectory matching road network data, where the trajectory matching road network data is used to indicate the multiple road lines and at least one trajectory segment respectively matched with each road line.

[0059] Among them, the trajectory matching road network processing can be implemented based on the trajectory binding road network (map match) technology, and can also be implemented by other methods. For details, please refer to the existing related technologies and will not be described in detail here.

[0060] The number of trajectories matched by different road lines may be the same or different. That is, any road line may match one trajectory or multiple trajectories, which is not limited here. A road line is the smallest unit of a road. A road line may match multiple trajectories, and different road lines may also match the same trajectory, that is, two or more road lines may match the same trajectory (for example, at least one trajectory matched with road line 1 includes trajectory 1, and at least one trajectory matched with road line 2 also includes trajectory 1).

[0061] Accordingly, different road lines can match the same or different number of trajectory segments. Any road line can match one trajectory segment or multiple trajectory segments, which is not limited here. A road line is the smallest unit of a road. A road line may match multiple trajectory segments, but different road lines match different trajectory segments. A road line may have multiple matching trajectory segments with a trajectory.

[0062] For example, Figure 3 As shown, road line A matches track 1, track 2, and track 3. There are two track segments on track 1 that match road line A (as shown in the bold part of track 1), one track segment on track 2 that matches road line A (as shown in the bold part of track 2), and one track segment on track 3 that matches road line A (as shown in the bold part of track 3).

[0063] 102. Take one of the multiple road lines as a target road line, and determine at least one target trajectory segment matching the target road line.

[0064] Exemplarily, any one of the multiple road lines can be used as the target road line, and at least one trajectory segment matching each road line is obtained according to the above steps to determine at least one target trajectory segment matching the target road line.

[0065] 103. For each trajectory point in at least one target trajectory segment, determine at least one matching sampling point that matches each trajectory point from multiple sampling points in the target road line, and obtain multiple matching relationships; wherein each matching relationship includes the target trajectory point and at least one matching sampling point that matches the target trajectory point, the distance between the matching sampling point in the matching relationship and the target trajectory point is less than the distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in at least one target trajectory segment.

[0066] It can be understood that after determining at least one target trajectory segment that matches the target road line, a matching relationship can be determined for each trajectory point in the at least one target trajectory segment. The matching relationship is to determine at least one matching sampling point that matches the trajectory point from multiple sampling points in the target road line for a trajectory point, and then obtain matching relationships corresponding to each trajectory point, that is, multiple matching relationships are obtained. For other road lines in the multiple road lines, multiple matching relationships corresponding to other road lines in the multiple road lines are also determined according to the above-mentioned method for corresponding to the target road line.

[0067] The number of sampling points that match the corresponding target trajectory point in each matching relationship may be the same or different, which is not limited here.

[0068] In some embodiments of the present application, the distance from each trajectory point in at least one trajectory matched to each road line obtained by the trajectory matching road network processing to all sampling points on the road line can be calculated for each road line as a unit, and then at least one matching sampling point that is closest to the trajectory point matched to each trajectory point is determined according to the principle of the nearest neighbor algorithm to obtain a matching relationship corresponding to each trajectory point, or a sampling point whose distance to each trajectory point is less than or equal to the matching threshold is determined as at least one matching sampling point corresponding to each trajectory point to obtain a matching relationship corresponding to each trajectory point, that is, the distance between the matching sampling point in the matching relationship and the target trajectory point is less than the distance between other sampling points in the target road line and the target trajectory point. 104. Based on multiple matching relationships, according to the speed value of at least one target trajectory point matched with the sampling point to be processed, the speed limit value of the sampling point to be processed is determined to determine the speed limit value of each sampling point in multiple road lines, and the sampling point to be processed is any one of the multiple sampling points in the target road line.

[0069] In some embodiments of the present application, the size of the speed limit value of the sampling point to be processed may not be limited, or the size of the speed limit value of the sampling point to be processed may be limited. The specific size may be determined based on actual conditions and is not limited here.

[0070] In some embodiments of the present application, the speed limit value of the sampling point to be processed is less than or equal to the maximum value of the speed value of at least one target trajectory point. In this way, it can be ensured that the vehicle drives at the speed limit value of the sampling point to be processed and does not violate the speed limit.

[0071] It can be understood that because each matching relationship includes a target trajectory point and at least one matching sampling point that matches the target trajectory point, therefore, taking the sampling point to be processed as a unit, at least one target trajectory point that matches the sampling point to be processed can be determined from multiple matching relationships, and then the speed limit value of the sampling point to be processed can be determined according to the speed value of the at least one target trajectory point.

[0072] A road line may match multiple trajectories, so a sampling point may match multiple trajectory points.

[0073] The speed limit value of the sampling point to be processed can be the maximum value of the speed values ​​of at least one target trajectory point, the speed limit value of the sampling point to be processed can also be the minimum value of the speed values ​​of at least one target trajectory point, the speed limit value of the sampling point to be processed can also be the average value of the speed values ​​of at least one target trajectory point, the speed limit value of the sampling point to be processed can also be the maximum value of the speed values ​​of at least one target trajectory point, and any one of the minimum value of the speed values ​​of at least one target trajectory point. The specific speed limit value can be determined according to actual conditions and is not limited here.

[0074] In the embodiment of the present application, a plurality of road lines in a target area and a plurality of trajectories in the target area are subjected to trajectory matching road network processing to obtain at least one trajectory segment matching each road line (wherein each trajectory segment belongs to a different trajectory, and the distance between any trajectory point on each trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold); one road line among the plurality of road lines is used as a target road line, and at least one target trajectory segment matching the target road line is determined; for each trajectory point in the at least one target trajectory segment, at least one matching sampling point matching each trajectory point is determined from a plurality of sampling points in the target road line, to obtain a plurality of matching relationships (each matching relationship includes a target trajectory point and at least one matching sampling point matching the target trajectory point, and each matching sampling point matches the target trajectory point). The distance between the target trajectory point and the target trajectory point is less than the distance between the sampling point that does not match the target trajectory point among the multiple sampling points in the target road line and the target trajectory point, and the target trajectory point is any one of the trajectory points in at least one target trajectory segment); based on multiple matching relationships, according to the speed value of at least one target trajectory point that matches the sampling point to be processed (any one of the multiple sampling points in the target road line), the speed limit value of the sampling point to be processed is determined (because each matching relationship includes the target trajectory point and at least one matching sampling point that matches the target trajectory point, therefore, taking the sampling point to be processed as a unit, at least one target trajectory point that matches the sampling point to be processed can be determined from the multiple matching relationships, and then the speed limit value of the sampling point to be processed can be determined according to the speed value of the at least one target trajectory point), so as to determine the speed limit value of each sampling point in the multiple road lines. Therefore, it is achieved that the speed value of the sampling point of the standard map is mapped by the speed value of the trajectory point on the trajectory of historical driving, the cost of obtaining the speed limit information of the standard map is reduced, and the efficiency of obtaining the speed limit information of the standard map is improved.

[0075] In some embodiments of the present application, in multiple matching relationships, each sampling point in multiple road lines may have a matching target trajectory point; in multiple matching relationships, some sampling points in each sampling point in multiple road lines may have matching target trajectory points, while another part of each sampling point in the multiple road lines may not have matching target trajectory points; the specific number can be determined based on actual conditions and is not limited here.

[0076] For example, in combination Figure 2 ,like Figure 4 As shown, the above step 104 can be specifically implemented through the following steps 104a and 104b.

[0077] 104a. Based on multiple matching relationships, determine whether there is a target trajectory point matching the sampling point to be processed in at least one target trajectory segment.

[0078] 104b. When it is determined that at least one target trajectory point matching the sampling point to be processed exists in at least one target trajectory segment, determine a speed limit value of the sampling point to be processed according to a speed value of the at least one target trajectory point matching the sampling point to be processed.

[0079] In the embodiment of the present application, it can be determined first whether there is a target trajectory point matching the sampling point to be processed in at least one target trajectory segment among multiple matching relationships, and if so, the above step 104b is executed, and if not, the following step 105 is executed. In this way, different methods for obtaining the speed limit value of the road line to be processed are determined according to whether there is a target trajectory point matching the sampling point to be processed, and the speed limit value of each sampling point can be quickly determined.

[0080] In some embodiments of the present application, the above step 104b can be specifically implemented through the following step 104b1.

[0081] 104b1. Determine the average value or the minimum value of the speed value of at least one target trajectory point that matches the sampling point to be processed as the speed limit value of the sampling point to be processed.

[0082] In the embodiment of the present application, the average value of the speed value of at least one target trajectory point matching the sampling point to be processed is determined as the speed limit value of the sampling point to be processed, which can basically ensure that the vehicle will not exceed the speed limit when passing through the road section corresponding to the sampling point to be processed at the speed limit value of the sampling point to be processed.

[0083] In the embodiment of the present application, the minimum value of the speed value of at least one target trajectory point matching the sampling point to be processed is determined as the speed limit value of the sampling point to be processed, which can ensure that the vehicle will not exceed the speed limit when passing through the road section corresponding to the sampling point to be processed at the speed limit value of the sampling point to be processed.

[0084] In some embodiments of the present application, Figure 4 ,like Figure 5 As shown, after the above step 104a, the speed limit processing method provided in the embodiment of the present application may further include the following step 105.

[0085] 105. When it is determined that there is no target trajectory point matching the sampling point to be processed in at least one target trajectory segment, determine the speed limit value of the sampling point to be processed according to the speed limit value of at least one speed limit sampling point whose speed limit value has been determined.

[0086] The speed limit value of the sampling point to be processed is less than or equal to the maximum speed limit value of at least one speed limit sampling point.

[0087] Exemplarily, when it is determined that there is no target trajectory point matching the sampling point to be processed in at least one target trajectory segment, the speed limit value of the sampling point to be processed can be determined based on the speed limit value of at least one speed limit sampling point that is closest to the sampling point to be processed and has a determined speed limit value (they may belong to the target road line together with the sampling point to be processed, or may belong to other road lines adjacent to the target road line).

[0088] In an embodiment of the present application, when there is no target trajectory point matching the sampling point to be processed in at least one target trajectory segment, the speed limit value of the sampling point to be processed can be determined based on the speed limit value of at least one speed limit sampling point whose speed limit value has been determined. In this way, the speed limit value of each sampling point in each road line can be determined.

[0089] In some embodiments of the present application, the above step 105 can be specifically implemented by any one of the following steps 105a to 105c.

[0090] 105a. When the sampling point to be processed is the first sampling point in the target road line, the speed limit value of a speed limit sampling point in the target road line that is located in front of the sampling point to be processed and closest to the sampling point to be processed and has a determined speed limit value is determined as the speed limit value of the sampling point to be processed.

[0091] 105b. When the sampling point to be processed is the second sampling point in the target road line, the average value or the minimum value of the speed limit values ​​of the speed limit sampling points in the target road line whose speed limit values ​​have been determined is determined as the speed limit value of the sampling point to be processed.

[0092] 105c. When the sampling point to be processed is the third sampling point in the target road line, the speed limit value of a speed limit sampling point in the target road line that is located behind the sampling point to be processed and closest to the sampling point to be processed and has a determined speed limit value is determined as the speed limit value of the sampling point to be processed.

[0093] In the target road line, the second sampling point is located in front of the first sampling point, and the third sampling point is located in front of the second sampling point.

[0094] Among them, in the driving direction along the target road line, with the sampling point to be processed as the reference, the previous sampling point relative to the sampling point to be processed is the sampling point located in front of the sampling point to be processed, and the next sampling point relative to the sampling point to be processed is the sampling point located behind the sampling point to be processed.

[0095] For example, Figure 3 As shown, among the three sampling points from left to right on the road line A, the left sampling point is located in front of the middle sampling point, and the right sampling point is located behind the middle sampling point.

[0096] In an embodiment of the present application, when there is no target trajectory point matching the sampling point to be processed in at least one target trajectory segment, the speed limit value of the sampling point to be processed can be determined according to the corresponding scheme according to whether the sampling point to be processed is the first sampling point, the second sampling point, or the third sampling point, and concurrent calculation can be achieved in units of road lines to improve processing efficiency.

[0097] In some embodiments of the present application, the first sampling point is the first sampling point in the target road line along the driving direction of the target road line (i.e., the starting sampling point on the corresponding road line); the third sampling point is the last sampling point in the target road line along the driving direction of the target road line (i.e., the ending sampling point on the corresponding road line); the second sampling point is the sampling point in the target road line other than the first sampling point and the third sampling point.

[0098] In the embodiment of the present application, it is distinguished whether the sampling point to be processed is the starting sampling point, the ending sampling point or the intermediate sampling point on the corresponding road line, and the speed limit value of the sampling point to be processed is determined according to the corresponding scheme. In this way, the determined speed limit value is more in line with reality, and concurrent calculation can be achieved in units of road lines to improve processing efficiency.

[0099] In some embodiments of the present application, before the above step 103, the speed limit processing method provided in the embodiments of the present application may further include the following steps 106 and 107.

[0100] 106. Perform equidistant interpolation processing on each road line starting from the corresponding starting sampling point at a preset interval to obtain at least one interpolation sampling point corresponding to each road line.

[0101] The distance between the last sampling point of at least one difference sampling point and the corresponding end sampling point of the road line is less than or equal to a preset interval.

[0102] Among them, the preset interval can be determined according to actual conditions and is not limited here. For example, the preset interval is 1 meter, 2 meters, etc.

[0103] 107. Determine the original sampling point of each road line and the corresponding at least one interpolation sampling point as a plurality of sampling points of each road line.

[0104] In the embodiment of the present application, since the sampling of the road line data of the standard map is relatively sparse, that is, there are relatively few original sampling points on the road line, in order to make the speed limit information of the final standard map more accurate, some sampling points can be interpolated for the road line.

[0105] 201. Obtain multiple lane groups in a target area in a high-precision map.

[0106] 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.

[0107] 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 201 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 201, the high-precision map and the standard map are spatially aligned, and the high-precision map and the standard map are aligned to the same coordinate system. Then step 201 specifically obtains multiple lane groups in the target area in the aligned high-precision map, and obtains multiple road lines in the target area in the aligned standard map. The specific alignment process refers to the existing related technology and is not limited here.

[0108] In the embodiment of the present application, the data corresponding to each sampling point in the multiple road lines used subsequently includes the speed limit value of the sampling point obtained according to part or all of the above steps 101 to 107.

[0109] 202. 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.

[0110] 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.

[0111] 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).

[0112] 203. 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 204. 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.

[0117] The speed limit value of each shape point in the target shape point set is determined according to the method described in step 204 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 202 to 204 above.

[0118] In an embodiment of the present application, a method is provided in which the speed limit value of the sampling point of the standard map is first mapped by the speed value of the trajectory point on the historical driving trajectory, and then the speed limit information of the high-precision map is mapped by the speed limit information of the standard map. This method quickly obtains the speed limit information of the high-precision map, reduces the complexity of obtaining the speed limit information of the high-precision map, and thus reduces the time consumption and improves the efficiency of obtaining the speed limit information of the high-precision map.

[0119] In some embodiments of the present application, Figure 5 ,like Figure 6 As shown, the above step 202 can be specifically implemented through the following steps 202a and 202b.

[0120] 202a. 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.

[0121] 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.

[0122] 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.

[0123] 202b. Perform spatial operation on the target polygon and the sampling point set to obtain the target matching point set.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In some embodiments of the present application, the above step 202 can be specifically implemented through the following steps 202c and 202d.

[0128] 202c. Based on the target shape point set, generate a target road section reference line corresponding to the target shape point set.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 202d. 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.

[0133] The distance threshold may be determined based on actual conditions and is not limited here.

[0134] It can be understood that the road section indicated by the target matching point set determined according to the above steps 202c and 202d 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 at this time 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).

[0135] 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.

[0136] In some embodiments of the present application, the above step 202 can be specifically implemented through the following steps 202e to 202f.

[0137] 202e. 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.

[0138] 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.

[0139] 202f. From each sampling point in at least one road line, determine a sampling point that matches the target shape point set to obtain a target matching point set.

[0140] For the specific implementation method of step 202f, reference may be made to the relevant descriptions of the above steps 202a and 202b or the above steps 202c and 202d, which will not be described in detail here.

[0141] 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.

[0142] In some embodiments of the present application, the above step 203 can be specifically implemented through the following steps 203a and 203b.

[0143] 203a. 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.

[0144] 203b. 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.

[0145] 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.

[0146] In some embodiments of the present application, Figure 6 ,like Figure 7 As shown, the above step 203 can be specifically implemented through the following steps 203c to 203e.

[0147] 203c. 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.

[0148] Each matching point subset includes at least one sampling point among a plurality of sampling points in the target matching point set.

[0149] 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.

[0150] 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.

[0151] 203d. Determine, from the multiple matching point subsets, a target matching point subset having the shortest distance from the shape point.

[0152] 203e. 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.

[0153] 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.

[0154] In some embodiments of the present application, the above step 203d can be specifically implemented through the following steps 203d1 and 203d2.

[0155] 203d1. 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.

[0156] 203d2. Determine the matching point subset corresponding to the minimum value among the multiple Hausdorff distances as the target matching point subset.

[0157] 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).

[0158] 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.

[0159] In some embodiments of the present application, the above step 203d can be specifically implemented through the following steps 203d3 and 203d4.

[0160] 203d3. Determine the distance between the one shape point and each matching point subset, and obtain multiple target distances corresponding to the one shape point.

[0161] 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.

[0162] 203d4. Determine the matching point subset corresponding to the minimum value among the multiple target distances as the target matching point subset.

[0163] 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.

[0164] In some embodiments of the present application, after the above step 204, the speed limit processing method provided by the embodiment of the present application may further include the following step 205.

[0165] 205. 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.

[0166] 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.

[0167] 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.

[0168] The present application also provides a speed limit processing device, Figure 8 A schematic diagram of the structure of a speed limit processing device provided in this application, such as Figure 8 As shown, the speed limit processing device includes:

[0169] A matching module 601 is used to perform trajectory matching road network processing on multiple road lines in a target area and multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, wherein a distance between any trajectory point on each trajectory segment in the at least one trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold;

[0170] The determination module 602 is configured to take one of the multiple road lines as a target road line, and determine at least one target trajectory segment that matches the target road line; for each trajectory point in the at least one target trajectory segment, respectively determine at least one matching sampling point that matches each trajectory point from multiple sampling points in the target road line, and obtain multiple matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point that matches the target trajectory point, and the distance between the matching sampling point in the matching relationship and the target trajectory point is less than the distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any one of the trajectory points in the at least one target trajectory segment; based on the multiple matching relationships, determine the speed limit value of the sampling point to be processed according to the speed value of at least one target trajectory point that matches the sampling point to be processed, so as to determine the speed limit value of each sampling point in the multiple road lines, and the sampling point to be processed is any one of the multiple sampling points in the target road line;

[0171] An acquisition module 603 is used to acquire multiple lane groups in a target area in a high-precision map;

[0172] The speed limit module 604 is used to determine, 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, 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 a 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; from a plurality of sampling points in the target matching point set, determine 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 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 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 a plurality of matching point pairs.

[0173] In some embodiments of the present application, the speed limit value of the sampling point to be processed is less than or equal to the maximum value of the speed value of at least one target trajectory point.

[0174] In some embodiments of the present application, the determination module 602 is specifically used to determine whether there is a target trajectory point matching the sampling point to be processed in at least one target trajectory segment based on multiple matching relationships; when it is determined that there is at least one target trajectory point matching the sampling point to be processed in at least one target trajectory segment, determine the speed limit value of the sampling point to be processed according to the speed value of at least one target trajectory point matching the sampling point to be processed.

[0175] In some embodiments of the present application, the determination module 602 is specifically configured to determine an average value or a minimum value of a speed value of at least one target trajectory point matching the sampling point to be processed as a speed limit value of the sampling point to be processed.

[0176] In some embodiments of the present application, the determination module 602 is specifically used to determine whether there is a target trajectory point matching the sampling point to be processed in at least one target trajectory segment based on multiple matching relationships. When it is determined that there is no target trajectory point matching the sampling point to be processed in at least one target trajectory segment, determine the speed limit value of the sampling point to be processed according to the speed limit value of at least one speed limit sampling point whose speed limit value has been determined, and the speed limit value of the sampling point to be processed is less than or equal to the maximum value of the speed limit values ​​of at least one speed limit sampling point.

[0177] In some embodiments of the present application, the determination module 602 is specifically used to, when the sampling point to be processed is the first sampling point in the target road line, determine the speed limit value of a speed limit sampling point in the target road line that is located in front of the sampling point to be processed and is closest to the sampling point to be processed and has a determined speed limit value as the speed limit value of the sampling point to be processed; when the sampling point to be processed is the second sampling point in the target road line, determine the average value or minimum value of the speed limit values ​​of each speed limit sampling point in the target road line that has a determined speed limit value as the speed limit value of the sampling point to be processed; when the sampling point to be processed is the third sampling point in the target road line, determine the speed limit value of a speed limit sampling point in the target road line that is located behind the sampling point to be processed and is closest to the sampling point to be processed and has a determined speed limit value as the speed limit value of the sampling point to be processed; wherein, in the target road line, the second sampling point is located in front of the first sampling point, and the third sampling point is located in front of the second sampling point.

[0178] In some embodiments of the present application, the first sampling point is the first sampling point in the target road line along the driving direction of the target road line; the third sampling point is the last sampling point in the target road line along the driving direction of the target road line; the second sampling point is the sampling point in the target road line other than the first sampling point and the third sampling point.

[0179] In some embodiments of the present application, the device also includes: an interpolation module, which is used to determine, for each trajectory point in at least one target trajectory segment, at least one matching sampling point that matches each trajectory point from multiple sampling points in the target road line, and before obtaining multiple matching relationships, perform equidistant interpolation processing on each road line starting from the corresponding starting sampling point at a preset interval to obtain at least one interpolation sampling point corresponding to each road line, and the distance between the last sampling point of at least one difference sampling point and the corresponding end sampling point of the road line is less than or equal to the preset interval.

[0180] The determination module 602 is further configured to determine the original sampling points of each road line and the corresponding at least one interpolation sampling point as a plurality of sampling points of each road line.

[0181] In some embodiments of the present application, the speed limit module 604 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.

[0182] In some embodiments of the present application, the speed limit module 604 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.

[0183] In some embodiments of the present application, the speed limit module 604 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.

[0184] In some embodiments of the present application, the speed limit module 604 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.

[0185] In some embodiments of the present application, the speed limit module 604 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.

[0186] In some embodiments of the present application, the speed limit module 604 is specifically used to 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; 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 shape point and each matching point subset respectively, and obtain multiple target distances corresponding to the shape point, each target distance is the average value of the distance between the shape point and each sampling point in the corresponding matching point subset, or each target distance is the sum of the distance between the shape point and each sampling point in the corresponding matching point subset; determine the matching point subset corresponding to the minimum value of the multiple target distances as the target matching point subset.

[0187] In some embodiments of the present application, the speed limit module 604 is also used to determine the speed limit value of the one sampling point in the target matching point pair as the speed limit value of the 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 then determine 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 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.

[0188] 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.

[0189] 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.

[0190] 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 described here.

[0191] Fig. 9 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.

[0192] like Fig. 9 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText 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.

[0197] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0198] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: performs trajectory matching road network processing on multiple road lines in the target area and multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, wherein each trajectory segment belongs to a different trajectory, and the distance between any trajectory point on each trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold;

[0199] Taking one of the multiple road lines as a target road line, and determining at least one target trajectory segment matching the target road line;

[0200] For each trajectory point in at least one target trajectory segment, at least one matching sampling point that matches each trajectory point is determined from a plurality of sampling points in a target road line, to obtain a plurality of matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point that matches the target trajectory point, a distance between each matching sampling point and the target trajectory point is smaller than a distance between a sampling point that does not match the target trajectory point among a plurality of sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in at least one target trajectory segment;

[0201] Based on the multiple matching relationships, determining the speed limit value of the sampling point to be processed according to the speed value of at least one target trajectory point matching the sampling point to be processed, so as to determine the speed limit value of each sampling point in the multiple road lines, the sampling point to be processed being any one of the multiple sampling points in the target road line;

[0202] Obtain multiple lane groups within the target area in the high-precision map;

[0203] 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;

[0204] 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;

[0205] The speed limit value of a sampling point in the target matching point pair is determined as the speed limit value of a 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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 chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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: Performing trajectory matching road network processing on multiple road lines in a target area and multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, wherein a distance between any trajectory point on each trajectory segment in the at least one trajectory segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold; Taking one of the plurality of road lines as a target road line, and determining at least one target trajectory segment matching the target road line; For each trajectory point in at least one target trajectory segment, at least one matching sampling point matching each trajectory point is determined from a plurality of sampling points in the target road line, to obtain a plurality of matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point matching the target trajectory point, a distance between the matching sampling point in the matching relationship and the target trajectory point is less than a distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in the at least one target trajectory segment; Based on the multiple matching relationships, determining a speed limit value of the sampling point to be processed according to a speed value of at least one target trajectory point that matches the sampling point to be processed, so as to determine a speed limit value of each sampling point in the multiple road lines, wherein the sampling point to be processed is any one of the multiple sampling points in the target road line; Obtain multiple lane groups within the target area in the high-precision 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 Before performing trajectory matching road network processing on the multiple road lines in the target area and the multiple trajectories in the target area to obtain at least one trajectory segment matching each road line, the method further includes: Determine the convex hull of the target area using a convex hull algorithm to obtain a spatial polygon composed of the spatial boundary of the target area; Determine the trajectories to be screened that are completely within the spatial polygon and the trajectories to be screened that are partially within the spatial polygon among the plurality of trajectories to be screened as the plurality of trajectories; The road lines to be filtered that are completely located in the spatial polygon and the road lines to be filtered that are partially located in the spatial polygon among the multiple road lines to be filtered are determined as the multiple road lines.

3. The method according to claim 1, characterized in that The determining, based on the multiple matching relationships and according to the speed value of at least one target trajectory point that matches the sampling point to be processed, the speed limit value of the sampling point to be processed includes: Based on the multiple matching relationships, determining whether there is a target trajectory point matching the sampling point to be processed in the at least one target trajectory segment; When it is determined that the at least one target trajectory point matching the sampling point to be processed exists in the at least one target trajectory segment, a speed limit value of the sampling point to be processed is determined according to a speed value of the at least one target trajectory point matching the sampling point to be processed.

4. The method according to claim 3, characterized in that After determining whether there is a target trajectory point matching the sampling point to be processed in the at least one target trajectory segment based on the multiple matching relationships, the method further includes: When it is determined that there is no target trajectory point matching the sampling point to be processed in the at least one target trajectory segment, the speed limit value of the sampling point to be processed is determined according to the speed limit value of at least one speed limit sampling point whose speed limit value has been determined, and the speed limit value of the sampling point to be processed is less than or equal to the maximum value of the speed limit values ​​of the at least one speed limit sampling point.

5. The method according to claim 4, characterized in that The determining the speed limit value of the sampling point to be processed according to the speed limit value of at least one speed limit sampling point whose speed limit value has been determined comprises: When the sampling point to be processed is the first sampling point in the target road line, the speed limit value of a speed limit sampling point in the target road line that is located in front of the sampling point to be processed and closest to the sampling point to be processed and has a determined speed limit value is determined as the speed limit value of the sampling point to be processed; In the case where the sampling point to be processed is the second sampling point in the target road line, an average value or a minimum value of the speed limit values ​​of the speed limit sampling points in the target road line whose speed limit values ​​have been determined is determined as the speed limit value of the sampling point to be processed; When the sampling point to be processed is the third sampling point in the target road line, the speed limit value of a speed limit sampling point in the target road line that is located behind the sampling point to be processed and closest to the sampling point to be processed and has a determined speed limit value is determined as the speed limit value of the sampling point to be processed; Wherein, in the target road line, the second sampling point is located in front of the first sampling point, and the third sampling point is located in front of the second sampling point.

6. The method according to claim 1, characterized in that Before determining, for each trajectory point in at least one target trajectory segment, at least one matching sampling point that matches each trajectory point from a plurality of sampling points in the target road line, and obtaining a plurality of matching relationships, the method further comprises: Performing equidistant interpolation processing on each road line starting from the corresponding starting sampling point at a preset interval to obtain at least one interpolation sampling point corresponding to each road line, wherein the distance between the last sampling point of the at least one difference sampling point and the corresponding ending sampling point of the road line is less than or equal to the preset interval; The original sampling points of each road line and the corresponding at least one interpolation sampling point are determined as a plurality of sampling points of each road line.

7. 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.

8. 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.

9. The method according to claim 8, 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.

10. The method according to any one of claims 1 to 9, 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.

11. A speed limit processing device, characterized in that: include: a matching module, configured to perform a track matching road network processing on a plurality of road lines in a target area and a plurality of tracks in the target area, to obtain at least one track segment matching each road line, wherein a distance between any track point on each track segment in the at least one track segment and at least one sampling point on the corresponding road line is less than or equal to a distance threshold; a determination module, configured to use one of the plurality of road lines as a target road line, and determine at least one target trajectory segment matching the target road line; For each trajectory point in at least one target trajectory segment, at least one matching sampling point matching each trajectory point is determined from a plurality of sampling points in the target road line, to obtain a plurality of matching relationships; wherein each matching relationship includes a target trajectory point and at least one matching sampling point matching the target trajectory point, a distance between the matching sampling point in the matching relationship and the target trajectory point is less than a distance between other sampling points in the target road line and the target trajectory point, and the target trajectory point is any trajectory point in the at least one target trajectory segment; Based on the multiple matching relationships, determining a speed limit value of the sampling point to be processed according to a speed value of at least one target trajectory point that matches the sampling point to be processed, so as to determine a speed limit value of each sampling point in the multiple road lines, wherein the sampling point to be processed is any one of the multiple sampling points in the target road line; An acquisition module is used to acquire multiple lane groups in a target area in a high-precision map; A speed limit 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.

12. 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 10.

13. 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 10 are implemented.

14. A vehicle, characterized in that: include: The speed limit processing device as claimed in claim 11, or the electronic device as claimed in claim 12, or includes the computer-readable storage medium as claimed in claim 13.

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