Map data processing method and apparatus, and electronic device and storage medium
By acquiring and processing node coordinates in map data, virtual graphic information and driving guidance lines are constructed, solving the accuracy problem of intersection recognition for intelligent driving vehicles in urban roads, and achieving smooth vehicle passage and improved safety.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- CHONGQING CHANGAN TECH CO LTD
- Filing Date
- 2025-09-19
- Publication Date
- 2026-07-23
Smart Images
Figure CN2025122552_23072026_PF_FP_ABST
Abstract
Description
A method and apparatus for processing map data, an electronic device, and a storage medium.
[0001] This application claims priority to Chinese Patent Application No. 202510077146.4, filed on January 17, 2025, entitled "A method and apparatus for processing map data, electronic device, and storage medium", the entire contents of which are incorporated herein by reference. Technical Field
[0002] This invention relates to the field of data processing technology, and in particular to methods and apparatuses for processing map data, electronic devices, and storage media. Background Technology
[0003] Intelligent driving, also known as autonomous driving or driverless driving, refers to the autonomous driving of vehicles through computer systems. It integrates various advanced technologies such as sensor technology and computer vision, aiming to improve road safety, traffic efficiency, and the driving experience. With the gradual development of intelligent driving technology and the increasing sophistication of its functions, its operational scope is gradually expanding from highways and expressways to urban roads.
[0004] In urban road scenarios, a vehicle's excellent intersection-crossing performance is a crucial element in perfecting intelligent driving in urban environments. To ensure that intelligent driving vehicles can smoothly pass through intersections, they first need to stably and accurately identify the intersection ahead.
[0005] In related technologies, deep learning and visual perception methods are used for intersection recognition. However, these methods are often limited by visual sensors and training data. Specifically, in complex scenes, visual sensors are easily occluded and cannot provide satisfactory performance, often resulting in missed detections and false detections. In addition, limited sensing distance is also a major factor restricting its performance. Therefore, related technologies have significant limitations and low accuracy in intersection recognition. Summary of the Invention
[0006] In view of the above problems, methods, apparatus, electronic devices, and storage media for processing map data that overcome or at least partially solve the above problems are proposed, including:
[0007] A method for processing map data, applied to electronic devices, includes:
[0008] In response to a vehicle's request to identify a target intersection, map data is obtained;
[0009] From the map data, identify multiple node data corresponding to the target intersection; the types of node data include the coordinates of the entry node and the coordinates of the exit node.
[0010] Construct virtual graphic information corresponding to the target intersection based on data from multiple nodes;
[0011] Obtain the vehicle's driving direction information, and determine the target entry node coordinates and target exit node coordinates in multiple node data based on the virtual graphics information and driving direction information;
[0012] Based on the coordinates of the target entry node and the target exit node, a virtual driving guide line for the vehicle is constructed.
[0013] A map data processing apparatus, the apparatus comprising:
[0014] The map data acquisition module is used to acquire map data in response to vehicle requests to identify target intersections;
[0015] The node data determination module is used to determine multiple node data corresponding to the target intersection from the map data; the types of node data include the coordinates of the entering node and the coordinates of the exit node.
[0016] The virtual graphic information construction module is used to construct virtual graphic information corresponding to the target intersection based on data from multiple nodes;
[0017] The target node determination module is used to acquire the vehicle's driving direction information and determine the target entry node coordinates and target exit node coordinates in multiple node data based on the virtual graphics information and driving direction information.
[0018] The virtual driving guide line construction module is used to construct a virtual driving guide line for the vehicle based on the coordinates of the target entry node and the target exit node.
[0019] An electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the above-described method for processing map data.
[0020] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for processing map data.
[0021] A computer program product, characterized in that it includes computer execution instructions, which, when executed by a processor, implement the above-mentioned method for processing map data.
[0022] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features, objects, and advantages of this application will become apparent from the specification, drawings, and claims. Attached Figure Description
[0023] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 is a flowchart of a map data processing method according to an embodiment of this application;
[0025] Figure 2 is a flowchart of another map data processing method provided in an embodiment of this application;
[0026] Figure 3 is a structural block diagram of a map data processing device provided in an embodiment of this application. Detailed Implementation
[0027] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0028] Referring to Figure 1, a flowchart of a map data processing method according to an embodiment of this application is shown, which may specifically include the following steps:
[0029] Step 101: In response to the vehicle's request to identify the target intersection, obtain map data;
[0030] In this embodiment, the vehicle can be a vehicle that has entered intelligent driving mode, and the target intersection can be an intersection that the vehicle needs to pass through in the direction of travel, preferably the first intersection ahead of the vehicle's travel route.
[0031] The map data can be lightweight map data (hereinafter referred to as lightweight map). Unlike high-precision maps, lightweight maps have a fast iteration and update speed, capable of updating weekly. Lightweight maps can cover urban scenes. Although their content elements are much simpler than those of high-precision maps, retaining only some necessary road data, such as road lines and road feature points, this is also one of the reasons why they can iterate and update so quickly.
[0032] In some embodiments of this application, the target intersection is the intersection that is closest to the vehicle in the direction of vehicle travel.
[0033] Step 102: Determine multiple node data corresponding to the target intersection from the map data; the types of node data include the coordinates of the entry node and the coordinates of the exit node.
[0034] In this embodiment, since the acquired map data includes various types of data, such as node data corresponding to non-intersection nodes, road line data, etc., it is necessary to filter the map data and retain only the node data corresponding to the target intersection.
[0035] The coordinates of the entry node are the coordinates of the node where vehicles enter (hereinafter referred to as the entry point), and the coordinates of the exit node are the coordinates of the node where vehicles exit (hereinafter referred to as the exit point). An intersection typically includes at least one entry point and one exit point. Taking a crossroads formed by four roads as an example, each road will have at least one entry point entering the crossroads and one exit point leaving the crossroads, thus the crossroads contains at least 8 node data points; if the road has multiple lanes, there may be more entry and exit points.
[0036] In the implementation, after acquiring map data, the system first filters out node data corresponding to non-intersection nodes based on attributes, retaining only node data corresponding to intersection nodes, resulting in multiple inbound and outbound intersection coordinates. However, at this point, it's still unclear which node data belongs to which intersection—for example, the first intersection ahead of the vehicle, the second intersection ahead, or other intersections not even in the vehicle's direction of travel. Therefore, it's necessary to cluster the node data according to different intersections, retaining only the node data corresponding to the target intersection. For example, assuming there is node data corresponding to 16 different intersections, clustering can determine that 8 node data belong to intersection number 1, and another 8 node data belong to intersection number 2, with intersection number 1 being the target intersection. Therefore, only the 8 node data corresponding to intersection number 1 are retained.
[0037] As an example, node data can be clustered based on the relative position of each node data point to vehicles and the successive relationships between intersections. The successive relationship between intersections refers to the spatial continuity between one intersection and another; once one intersection is determined, another intersection can be determined through this continuity.
[0038] Step 103: Construct virtual graphic information corresponding to the target intersection based on data from multiple nodes;
[0039] In this embodiment, since the node data includes the coordinates of all entry and exit nodes at the target intersection, the virtual graphic information constructed from the node data can express the geometric features of the target intersection, thus completing the construction of the target intersection for recognition by intelligent driving vehicles and laying the foundation for vehicles to smoothly pass through the intersection. Simultaneously, the virtual graphic information can also determine whether a vehicle has entered or exited the target intersection. As an example, the virtual graphic information can be polygonal, where each side of the polygon can map to at least one set of entry and exit points of one of the roads constituting the intersection. For example, for a crossroads, rectangular virtual graphic information can be constructed, with the four sides of the rectangle corresponding to the entry and exit points of the four roads constituting the crossroads.
[0040] In some embodiments of this application, virtual graphical information corresponding to a target intersection is constructed based on data from multiple nodes, including:
[0041] The vehicle's location information is obtained, and multiple node data are grouped according to the vehicle's location information to obtain multiple node groups; each node group includes the coordinates of a first entering node and a first exiting node.
[0042] The target straight line corresponding to each node group is determined based on the coordinates of the first entry node and the first exit node, and the coordinates of the intersection point between each target straight line are determined.
[0043] The virtual graphic information corresponding to the target intersection is constructed based on the coordinates of the intersection points of the straight lines.
[0044] In this embodiment, since the node data corresponding to the target intersection is still disorganized and consists only of individual coordinate data, it is necessary to group the node data to determine which node data belongs to which location within the intersection. As an example, after obtaining the node data of the intersection, the node data can be divided into node groups such as the entry intersection group, left turn group, right turn group, and straight-ahead group.
[0045] Furthermore, since it's necessary to determine the relative position of the vehicle's current location to the intersection—for example, whether the vehicle has entered or exited the intersection—virtual information about the target intersection needs to be constructed, i.e., virtual graphical information corresponding to the target intersection. Therefore, each node group includes the coordinates of a first entering node and a first exiting node, and each node group can correspond to a target straight line. After calculating the coordinates of the intersection points between the target straight lines corresponding to each node group, virtual graphical information is constructed using these intersection coordinates. Since the virtual graphical information constructed using these intersection coordinates necessarily covers the target intersection corresponding to all node data, it can completely express the geometric features of the target intersection, improving the accuracy of constructing the virtual information corresponding to the target intersection.
[0046] In practical implementation, the coordinates of the intersection of the lines can be calculated in the following way:
[0047] P intersection =I i ×I i+1
[0048] Among them, P intersection I is the intersection of the lines. i I i+1 For each node group, the corresponding straight line is defined. By constructing a homogeneous coordinate system, the cross product of the equations corresponding to the two target straight lines can be used to obtain the coordinates of the intersection point of the lines.
[0049] In some embodiments of this application, after determining the target straight line corresponding to each node group based on the coordinates of the first entering node and the coordinates of the first exit node, and determining the coordinates of the intersection point between each target straight line, the method further includes:
[0050] Determine whether the coordinates of each intersection point of the straight lines meet the preset coordinate range;
[0051] If the coordinates of the intersection of the lines meet the preset coordinate range, retain the coordinates of the intersection of the lines.
[0052] In this embodiment of the application, since the two target straight lines may be parallel or approximately parallel, the coordinates of the intersection point of the straight lines may not exist or the coordinates of the intersection point of the straight lines may be too far away from the coordinates corresponding to the vehicle. Therefore, it is necessary to determine whether the coordinates of each intersection point of the straight lines meet the preset coordinate range. If they do not meet the range, they are removed; if they do meet the range, they are retained.
[0053] In some embodiments of this application, vehicle location information is obtained, and node data is grouped according to the vehicle location information to obtain multiple node groups, including:
[0054] Based on the vehicle's location information, determine the coordinates of the second entry node that is closest to the vehicle from multiple node data;
[0055] Based on the data from multiple nodes and the total number of data points, determine the coordinates of the centroid points corresponding to the data from multiple nodes;
[0056] Determine the first vector composed of the data of each node and the coordinates of the centroid, and the second vector composed of the coordinates of the second entering node and the coordinates of the centroid.
[0057] Determine the target angle formed by each first vector and second vector, and sort the node data according to the size of the target angle to obtain the node sorting result;
[0058] Based on the node sorting results and vehicle location information, multiple node data are grouped to obtain multiple node groups.
[0059] In this embodiment of the application, the coordinates of the second entry node are the coordinates of the reference point, the centroid is the center point of the geometric figure formed by all entry points and all exit points in the node data, and the coordinates of the centroid are the coordinates of the center point.
[0060] Furthermore, a first vector is formed by combining the coordinates of each node (i.e., the coordinates of the entering or exiting node) with the coordinates of the centroid, and a second vector is formed by combining the coordinates of the second entering node with the coordinates of the centroid. The target angle between each of the first and second vectors is then determined. The magnitude of this target angle reflects the relative position of each node data point to the vehicle at the target intersection, allowing for subsequent grouping of each node data point into node groups. For example, in the case of a crossroads, when the target angle is 75°, the entering or exiting point corresponding to that node data point may be located to the left of the vehicle's direction of travel; when the target angle is 225°, the entering or exiting point may be located in front of the vehicle's direction of travel; and when the target angle is 320°, the entering or exiting point may be located to the right of the vehicle's direction of travel. Moreover, the target angles corresponding to entering and exiting points belonging to the same road are relatively similar, facilitating the grouping of node data.
[0061] Furthermore, by sorting each node's data according to the size of its corresponding target angle, spatially continuous node data can be obtained. For example, sorting by target angle from largest to smallest will yield continuous node data in a counter-clockwise direction centered on the second entry node (i.e., the reference point).
[0062] Furthermore, by combining the vehicle's location information, the relative positional relationship between the vehicle and the corresponding entry or exit point of the node data is determined, and the node data is grouped. Taking an intersection as an example, according to the sorting results of the target angle from largest to smallest and combined with the relative positional relationship between the vehicle and each node data, the sorted node data can be divided into right-turn group, straight-ahead group, left-turn group, and intersection entry group in a counterclockwise direction.
[0063] In some embodiments of this application, determining the target angle formed by each first vector and second vector includes:
[0064] Determine the cross product of the first vector and the second vector, and the dot product of the first vector and the second vector;
[0065] The target radians of the first and second vectors are determined by the cross product and dot product of the vectors, and the target radians are converted into target angles.
[0066] In the specific implementation, let the coordinates of the second entry node be P. r The coordinates of the centroid are Where P iFor each entering or exiting node coordinate, n represents the total number of node data points, i.e., the total number of entering and exiting node coordinates. Let the first vector be V. ci The second vector is V cr Let the target angle be θ. The target angle can be obtained in the following way:
[0067] Among them, V ci ×V cr That is, the cross product of vectors, V ci ·V cr This is the vector dot product. By processing the vector cross product and vector dot product with inverse trigonometric functions, we can obtain the target radian formed by the first and second vectors. Then, the target radian can be converted into the target angle using the RAD_DEG() function.
[0068] Step 104: Obtain the vehicle's driving direction information, and determine the target entry node coordinates and target exit node coordinates in multiple node data based on the virtual graphics information and driving direction information.
[0069] In this embodiment, the virtual graphic information can reflect the geometric features of the target intersection. Therefore, combined with the vehicle's driving direction information, the target entry node coordinates and target exit node coordinates required for the vehicle to pass through the target intersection in the vehicle's driving direction can be quickly determined, so that the vehicle can pass through the target intersection smoothly.
[0070] In some embodiments of this application, the virtual graphics information includes multiple edge information, each edge information including at least one third entry node coordinate and at least one second exit node coordinate. Determining the target entry node coordinate and target exit node coordinate in the multiple node data based on the virtual graphics information and driving direction information includes:
[0071] Among multiple edge information, determine the target entry edge information and target exit edge information that match the driving direction information;
[0072] The target entry node coordinates are determined from at least one third entry node coordinate in the target entry edge information, and the target exit node coordinates are determined from at least one second exit node coordinate in the target exit edge information.
[0073] In some embodiments of this application, when the target intersection is a crossroads, the virtual graphic information corresponding to the target intersection is the virtual graphic information of a rectangle, which includes information about four sides.
[0074] In practical implementation, taking a crossroads as an example, the corresponding information is virtual rectangular graphics. The rectangular edge closest to the vehicle's direction of travel is designated as edge 1. The other rectangular edges are designated as edge 2, edge 3, and edge 4 in a clockwise direction. Each rectangular edge (i.e., edge information) corresponds to at least one exit point and one entry point, along with their corresponding coordinates. When the vehicle's direction of travel is determined to be going straight, edge 1 can be identified as the target entry edge, and edge 3, opposite edge 1, can be identified as the target exit edge.
[0075] Furthermore, the coordinates of the target entry node are determined from the coordinates of multiple third entry nodes in the information of edge 1, i.e., the target entry edge, and the coordinates of the target exit point are determined from the coordinates of multiple second exit nodes in the information of edge 3, i.e., the target exit edge.
[0076] Step 105: Construct a virtual driving guide line for the vehicle based on the coordinates of the target entry node and the target exit node.
[0077] In this embodiment, since lane markings are often not planned at intersections in urban areas, a virtual driving guide line can be constructed using the target entry node coordinates and the target exit node coordinates to allow the vehicle to pass smoothly through the target intersection. As an example, once the target entry node coordinates and the target exit node coordinates are determined, a smooth curve can be used to connect the entry point corresponding to the target entry node coordinates and the exit point corresponding to the target exit node coordinates; this curve is the vehicle's virtual driving guide line.
[0078] In some embodiments of this application, as shown in Figure 2, a map data processing flow is also provided, as detailed below:
[0079] 1. Receive lightweight map data, cluster the node data in the lightweight map data, and determine multiple node data corresponding to the target intersection.
[0080] 2. Based on the vehicle's location information, group the data from multiple nodes to obtain multiple node groups.
[0081] 3. Determine whether the number of multiple node groups is greater than the threshold, and determine whether each node group includes at least one inbound node coordinate and one outbound node coordinate;
[0082] 4.1 If the number of multiple node groups is greater than the threshold (e.g., greater than 2), and each node group includes at least one inbound node coordinate and one outbound node coordinate, then construct the convex polygon corresponding to the target intersection (i.e., the virtual graphic information corresponding to the target intersection) based on the multiple node groups.
[0083] 4.2 If the number of multiple node groups is not greater than the threshold, or if there are node groups that do not enter or exit node coordinates, then construct the convex polygon corresponding to the target intersection (i.e., the virtual graphic information corresponding to the target intersection) based on the multiple node groups, and then return to step 1.
[0084] 5. Based on virtual graphic information and driving direction, determine the coordinates of the target entering the node and the target leaving the node from multiple node data;
[0085] 6. Construct a virtual driving guide line based on the coordinates of the target entry node and the target exit node.
[0086] The embodiments of this application have the following advantages: In response to a vehicle's request to identify a target intersection, the embodiments of this application acquire map data, determine multiple node data corresponding to the target intersection from the map data, wherein the types of node data include entry node coordinates and exit node coordinates, construct virtual graphic information corresponding to the target intersection based on the multiple node data, acquire the vehicle's driving direction information, and determine the target entry node coordinates and target exit node coordinates in the multiple node data based on the virtual graphic information and driving direction information, construct a virtual driving guide line for the vehicle based on the target entry node coordinates and target exit node coordinates. The virtual graphic information that can reflect the geometric features of the target intersection is constructed solely from the multiple node data corresponding to the target intersection in the map data, which is equivalent to identifying the intersection without being restricted by visual sensors, improving the flexibility and accuracy of intersection identification. At the same time, a virtual guide line for vehicle driving is also constructed, enabling vehicles to pass through the intersection smoothly and improving the safety of intelligent driving.
[0087] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.
[0088] Referring to Figure 3, a schematic diagram of a map data processing apparatus according to an embodiment of this application is shown, which may specifically include the following modules:
[0089] The map data acquisition module 301 is used to acquire map data in response to a vehicle's request to identify a target intersection;
[0090] The node data determination module 302 is used to determine multiple node data corresponding to the target intersection from the map data; wherein, the types of node data include the coordinates of the entering node and the coordinates of the exit node;
[0091] The virtual graphic information construction module 303 is used to construct virtual graphic information corresponding to the target intersection based on data from multiple nodes.
[0092] The target node determination module 304 is used to acquire the vehicle's driving direction information and determine the target entry node coordinates and target exit node coordinates in multiple node data based on the virtual graphics information and driving direction information.
[0093] The virtual driving guide line construction module 305 is used to construct a virtual driving guide line for the vehicle based on the coordinates of the target entry node and the target exit node.
[0094] In some embodiments of this application, the virtual graphics information construction module 303 includes:
[0095] The grouping submodule is used to obtain the vehicle's location information and group the data of multiple nodes according to the vehicle's location information to obtain multiple node groups; each node group includes the coordinates of a first entering node and a first exiting node.
[0096] The line intersection point coordinate determination submodule is used to determine the target line corresponding to each node group based on the coordinates of the first entry node and the first exit node, and to determine the line intersection point coordinates between each target line.
[0097] The virtual graphic information construction submodule is used to construct virtual graphic information corresponding to the target intersection based on the coordinates of the intersection points of straight lines.
[0098] In some embodiments of this application, the grouping submodule includes:
[0099] The second entry node coordinate determination subunit is used to determine the coordinates of the second entry node closest to the vehicle from multiple node data based on the vehicle's location information.
[0100] The centroid coordinate determination sub-unit is used to determine the centroid coordinates corresponding to multiple node data based on multiple node data and the total number of node data.
[0101] The first vector and the second vector determine the sub-unit, which is used to determine the first vector composed of the data of each node and the coordinates of the centroid point, and the second vector composed of the coordinates of the second entering node and the coordinates of the centroid point;
[0102] The node sorting result determines the sub-unit, which is used to determine the target angle formed by each first vector and second vector, and sorts the data of each node according to the size of the target angle to obtain the node sorting result;
[0103] The grouping subunit is used to group multiple node data according to the node sorting results and vehicle location information to obtain multiple node groups.
[0104] In some embodiments of this application, the target node determination module 304 includes:
[0105] The target edge information determination submodule is used to determine the target entry edge information and target exit edge information that match the driving direction information from multiple edge information.
[0106] The target node determination submodule is used to determine the target entry node coordinates from at least one third entry node coordinate in the target entry edge information, and to determine the target exit node coordinates from at least one second exit node coordinate in the target exit edge information.
[0107] In some embodiments of this application, the virtual graphics information includes multiple edge information, each edge information including at least one third entry node coordinate and at least one second exit node coordinate.
[0108] In some embodiments of this application, the device further includes:
[0109] The line intersection point coordinate determination module is used to determine whether the coordinates of each line intersection point meet the preset coordinate range;
[0110] The line intersection coordinate filtering module is used to retain the line intersection coordinates when they meet a preset coordinate range.
[0111] In some embodiments of this application, the node sorting result determines the subunit, including:
[0112] The vector product determines the sub-unit, which is used to determine the cross product of the first vector and the second vector, as well as the dot product of the first vector and the second vector;
[0113] The target angle determination sub-unit is used to determine the target radians of the first and second vectors based on the vector cross product and vector dot product, and convert the target radians into target angles.
[0114] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.
[0115] Some embodiments of this application also provide an electronic device that may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the above-described method for processing map data.
[0116] Some embodiments of this application also provide a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-described method for processing map data.
[0117] Some embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described method for processing map data.
[0118] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.
[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0121] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.
[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.
[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable terminal equipment, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.
[0124] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.
[0125] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the aforementioned element.
[0126] The above provides a detailed description of the map data processing methods, apparatus, electronic devices, and storage media. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for processing map data, wherein, Applied to electronic devices, the method includes: In response to a vehicle's request to identify a target intersection, map data is obtained; From the map data, multiple node data corresponding to the target intersection are determined; wherein, the types of node data include inbound node coordinates and outbound node coordinates; Based on the data from the multiple nodes, construct virtual graphic information corresponding to the target intersection; Obtain the vehicle's driving direction information, and determine the target entry node coordinates and target exit node coordinates from the plurality of node data based on the virtual graphics information and the driving direction information; and A virtual driving guide line for the vehicle is constructed based on the coordinates of the target entry node and the target exit node.
2. The method according to claim 1, wherein, The step of constructing the virtual graphic information corresponding to the target intersection based on the multiple node data includes: The location information of the vehicle is obtained, and the multiple node data are grouped according to the location information of the vehicle to obtain multiple node groups; wherein, each node group includes a first entry node coordinate and a first exit node coordinate; Based on the coordinates of the first entering node and the first exiting node, determine the target straight line corresponding to each node group, and determine the coordinates of the intersection point between each target straight line; and The virtual graphic information corresponding to the target intersection is constructed based on the coordinates of the intersection of the straight lines.
3. The method according to claim 2, wherein, The step of grouping the multiple node data according to the vehicle's location information to obtain multiple node groups includes: Based on the vehicle's location information, determine the coordinates of the second entry node closest to the vehicle from the plurality of node data; Based on the multiple node data and the total number of the multiple node data, determine the centroid coordinates corresponding to the multiple node data; Determine a first vector composed of each node data and the centroid coordinates, and a second vector composed of the second entering node coordinates and the centroid coordinates; Determine the target angle formed by each of the first vectors and the second vectors, and sort each node data according to the magnitude of the target angle to obtain a node sorting result; and The node data is grouped according to the node sorting result and the vehicle location information to obtain multiple node groups.
4. The method according to claim 3, wherein, Determining the target angle formed by each of the first vector and the second vector includes: Determine the cross product of the first vector and the second vector, and the dot product of the first vector and the second vector; and The target radians of the first vector and the second vector are determined based on the cross product and the dot product of the vectors, and the target radians are converted into the target angles.
5. The method according to claim 2, wherein, After determining the target straight line corresponding to each node group based on the coordinates of the first entering node and the first exiting node, and determining the coordinates of the intersection point between each target straight line, the method further includes: Determine whether the coordinates of each intersection point of the stated lines satisfy a preset coordinate range; and When the coordinates of the intersection of the lines meet the preset coordinate range, the coordinates of the intersection of the lines are retained.
6. The method according to any one of claims 1 to 5, wherein, The virtual graphics information includes multiple edge information, each edge information including at least one third entry node coordinate and at least one second exit node coordinate.
7. The method according to claim 6, wherein, Determining the target entry node coordinates and target exit node coordinates from the plurality of node data based on the virtual graphics information and the driving direction information includes: Among the plurality of edge information, target entry edge information and target exit edge information that match the driving direction information are determined; and The target entry node coordinates are determined from at least one of the third entry node coordinates in the target entry edge information, and the target exit node coordinates are determined from at least one of the second exit node coordinates in the target exit edge information.
8. The method according to claim 6, wherein, When the target intersection is a crossroads, the virtual graphic information corresponding to the target intersection is the virtual graphic information of a rectangle, which includes information about four sides.
9. The method according to any one of claims 1 to 5, wherein, The target intersection is the intersection that is closest to the vehicle in the direction of the vehicle's travel.
10. A map data processing apparatus, wherein, The device includes: The map data acquisition module is used to acquire map data in response to vehicle requests to identify target intersections; The node data determination module is used to determine multiple node data corresponding to the target intersection from the map data; wherein, the type of node data includes the coordinates of the entry node and the coordinates of the exit node; A virtual graphic information construction module is used to construct virtual graphic information corresponding to the target intersection based on the multiple node data; The target node determination module is used to acquire the vehicle's driving direction information and determine the coordinates of the target entry node and the target exit node in the plurality of node data based on the virtual graphics information and the driving direction information; and The virtual driving guide line construction module is used to construct a virtual driving guide line for the vehicle based on the coordinates of the target entry node and the target exit node.
11. The apparatus according to claim 10, wherein, The virtual graphics information construction module includes: The grouping submodule is used to obtain the location information of the vehicle and group the multiple node data according to the location information of the vehicle to obtain multiple node groups; wherein, each node group includes a first entry node coordinate and a first exit node coordinate; The line intersection coordinate determination submodule is used to determine the target line corresponding to each node group based on the coordinates of the first entering node and the first exiting node, and to determine the coordinates of the line intersection points between each of the target lines; and The virtual graphic information construction submodule is used to construct the virtual graphic information corresponding to the target intersection based on the coordinates of the intersection points of the straight lines.
12. The apparatus according to claim 11, wherein, The grouping submodule includes: The second entry node coordinate determination subunit is used to determine the coordinates of the second entry node closest to the vehicle from the plurality of node data based on the vehicle's position information. The centroid coordinate determination subunit is used to determine the centroid coordinates corresponding to the multiple node data based on the multiple node data and the total number of the multiple node data. The first vector and second vector determining subunit is used to determine the first vector composed of each node data and the centroid coordinates, and the second vector composed of the second entering node coordinates and the centroid coordinates. A node sorting result determination subunit is used to determine the target angle formed by each of the first vector and the second vector, and sort each node data according to the magnitude of the target angle to obtain the node sorting result; and The grouping subunit is used to group the multiple node data according to the node sorting result and the vehicle location information to obtain multiple node groups.
13. An electronic device, wherein, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements a method for processing map data as described in any one of claims 1 to 9.
14. A computer-readable storage medium, wherein, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method for processing map data as described in any one of claims 1 to 9.
15. A computer program product, wherein, It includes computer execution instructions, which, when executed by a processor, implement the map data processing method as described in any one of claims 1 to 9.