Vehicle track matching method, device and equipment and storage medium
By obtaining the target vehicle's driving trajectory point collection and road network data, and combining the correspondence between the core area and the region, the problem of inaccurate identification of vehicle driving coverage areas in the prior art is solved, and more accurate coverage area identification is achieved.
Patent Information
- Application Number
- CN202310854552.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-12
- Publication Date
- 2025-01-14
AI Technical Summary
The prior art is difficult to accurately determine the coverage area of a specific type of vehicle, resulting in the inability to accurately understand the coverage area of a vehicle.
By obtaining the set of driving trajectory points of the target vehicle, the road section closest to the track point is determined, and the vehicle travel coverage area is determined based on the correspondence between the core area and the region. Specific steps include matching road network data with area boundary data, clustering analysis to determine the core area, and determining the vehicle's driving coverage area based on these relationships.
Accurate determination of the vehicle's coverage area is achieved and the accuracy of coverage identification is improved.
Smart Images
Figure CN119312098A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing, and in particular to a vehicle trajectory matching method, device, equipment and storage medium. Background Art
[0002] In the automotive field, it is usually necessary to collect various vehicle-related data to understand the current situation and make decisions. For example, by collecting the driving coverage areas of specific types of vehicles, the usage of high-precision maps can be understood.
[0003] At present, the geographic location coordinate set of the driving trajectory of a specific type of vehicle is usually matched with the regional boundary data of each region to determine the driving coverage area of the specific type of vehicle. However, there is a situation where the specific type of vehicle reaches the regional boundary but does not reach the core area of the region, and is also determined to cover the region. As a result, the specific type of vehicle cannot be accurately determined. Therefore, how to more accurately determine the area covered by the specific type of vehicle has become a technical problem that urgently needs to be solved. Summary of the invention
[0004] In order to solve the above technical problems, the present disclosure provides a vehicle trajectory matching method, which determines the target road sections belonging to each core area, determines the driving coverage area of the target vehicle set according to the target road sections belonging to each core area and the correspondence between the core area and the region, and can more accurately determine the situation of the driving coverage area of the vehicles in the target vehicle set.
[0005] In a first aspect, the present disclosure provides a vehicle trajectory matching method, the method comprising:
[0006] Acquire a set of driving trajectory points corresponding to a target vehicle set; wherein the target vehicle set includes vehicles of a specific type;
[0007] Adding a road segment in the road network data that is closest to the corresponding driving trajectory point and whose distance is less than a first preset threshold to the target road segment set;
[0008] From the target road segment set, target road segments belonging to each core area are determined respectively; wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road segments in the corresponding region;
[0009] Based on the target road sections belonging to each core area and the corresponding relationship between the core area and the area, the driving coverage area of the target vehicle set is determined.
[0010] In an optional implementation manner, the determining of the driving coverage area of the target vehicle set based on the target road sections corresponding to the core areas and the corresponding relationship between the core areas and the areas includes:
[0011] Determine the number of target road sections belonging to each core area respectively;
[0012] Determine a core area where the number of target road sections is greater than a second preset threshold as a target core area;
[0013] Based on the correspondence between the core area and the region, the region corresponding to the target core area is determined as the driving coverage area of the target vehicle set.
[0014] In an optional implementation manner, before determining the target road segments corresponding to the respective core areas from the target road segment set, the method further includes:
[0015] Match the road section data in the road network data with the regional boundary data of each region to obtain the road section data corresponding to each region;
[0016] Based on the road segment data corresponding to each region, the core areas corresponding to each region are determined.
[0017] In an optional implementation manner, determining the core areas corresponding to the respective regions based on the road segment data corresponding to the respective regions includes:
[0018] By performing cluster analysis on the road section data in each region, the core areas corresponding to each region are determined.
[0019] In an optional implementation manner, the cluster analysis is performed on the road segment data in each region to determine the core areas corresponding to each region, including:
[0020] The centroids of the road segment data in each region are respectively used as sampling points to determine the density parameter and the number of dense points; wherein the density parameter is used to determine the neighborhood range of the sampling point, and the number of dense points is used to determine the density of the core point;
[0021] The density parameter is used as the distance threshold of the neighborhood, and the number of dense points is used as the number of sampling points in the area;
[0022] By performing cluster analysis on the sampling points in each region, the core areas corresponding to each region are determined.
[0023] In an optional implementation, the driving trajectory point set includes geographic location coordinate points of vehicles in the target vehicle set when a preset event occurs.
[0024] In an optional implementation, the preset event includes an automatic assisted navigation driving exit event and / or a lane keeping system exit event.
[0025] In a second aspect, the present disclosure provides a vehicle trajectory matching device, the device comprising:
[0026] An acquisition module, used to acquire a set of driving trajectory points corresponding to a target vehicle set; wherein the target vehicle set includes vehicles of a specific type;
[0027] An adding module, used for adding a road segment in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold to the target road segment set;
[0028] A first determination module is used to determine the target road sections belonging to each core area from the target road section set; wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region;
[0029] The second determination module is used to determine the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the corresponding relationship between the core area and the area.
[0030] In a third aspect, the present disclosure provides a vehicle trajectory matching device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.
[0031] In a fourth aspect, the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device implements the above method.
[0032] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has at least the following advantages:
[0033] The disclosed embodiment provides a vehicle trajectory matching method, first, obtaining a set of driving trajectory points corresponding to a target vehicle set, wherein the target vehicle set includes vehicles of a specific type, and then adding a road section in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold to the target road section set, and from the target road section set, respectively determining target road sections belonging to each core area, wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region, and determining the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the corresponding relationship between the core area and the region. It can be seen that the disclosed embodiment can more accurately determine the situation of the area covered by the driving of vehicles in the target vehicle set by determining the target road sections belonging to each core area and determining the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the corresponding relationship between the core area and the region. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0035] In order to more clearly illustrate the embodiments of the present invention 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.
[0036] Figure 1 A flow chart of a vehicle trajectory matching method provided in an embodiment of the present disclosure;
[0037] Figure 2 A flowchart of another vehicle trajectory matching method provided by an embodiment of the present disclosure;
[0038] Figure 3 A schematic diagram of the structure of a vehicle trajectory matching device provided in an embodiment of the present disclosure;
[0039] Figure 4 A schematic diagram of the structure of a vehicle trajectory matching device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0040] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.
[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.
[0042] In order to more accurately determine the conditions of the areas covered by vehicles in a target vehicle set, the present disclosure provides a vehicle trajectory matching method.
[0043] Specifically, first, a set of driving trajectory points corresponding to the target vehicle set is obtained, wherein the target vehicle set includes vehicles of a specific type, and then a road section in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold is added to the target road section set, and from the target road section set, target road sections belonging to each core area are determined respectively, wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region, and the driving coverage area of the target vehicle set is determined based on the target road sections belonging to each core area and the corresponding relationship between the core area and the region. It can be seen that the embodiment of the present disclosure can more accurately determine the situation of the area covered by the driving of vehicles in the target vehicle set by determining the target road sections belonging to each core area and determining the driving coverage area of the target vehicle set according to the target road sections belonging to each core area and the corresponding relationship between the core area and the region.
[0044] Based on this, the present disclosure provides a vehicle trajectory matching method, referring to Figure 1 , is a flow chart of a vehicle trajectory matching method provided by an embodiment of the present disclosure, the method comprising:
[0045] S101: Obtain a set of driving trajectory points corresponding to a set of target vehicles.
[0046] The target vehicle set includes vehicles of a specific type.
[0047] In the disclosed embodiment, the target vehicle set may be a set consisting of vehicles of a specific type (such as vehicles of a specific model of a specific brand, etc.). Specifically, the specific type of vehicle may be a vehicle with a preset identification. Accordingly, the driving trajectory point set corresponding to the target vehicle set may be a set of geographic location coordinate points of each vehicle in the target vehicle set during its driving process.
[0048] In order to reduce the consumption of storage and computing resources caused by the huge amount of driving trajectory point data, in an optional implementation, the driving trajectory point set may be the geographical location coordinate points when a preset event occurs to vehicles in the target vehicle set.
[0049] The preset event refers to a pre-set event, such as an event in which a vehicle encounters an obstacle and performs emergency braking during driving.
[0050] In the disclosed embodiment, when a vehicle in the target vehicle set encounters an obstacle and brakes urgently during driving, the geographic location coordinate point at the time of the event is recorded and added to the driving trajectory point set.
[0051] In an optional implementation, the preset event may also include an automatic assisted navigation driving exit event and / or a lane keeping system exit event.
[0052] In the disclosed embodiment, when an automatic assisted navigation driving exit event occurs in a vehicle in the target vehicle set, the geographic location coordinate point at the time when the automatic assisted navigation driving exit event occurs is recorded and added to the driving trajectory point set.
[0053] In another embodiment of the present disclosure, when a lane keeping system exit event occurs in a vehicle in the target vehicle set, the geographic location coordinate point at the time when the lane keeping system exit event occurs is recorded and added to the driving trajectory point set.
[0054] In another embodiment of the present disclosure, when an automatic assisted navigation driving exit event occurs in a vehicle in the target vehicle set, and a lane keeping system exit event occurs in a vehicle in the target vehicle set, the geographic location coordinate point at the time when the automatic assisted navigation driving exit event occurs and the geographic location coordinate point at the time when the lane keeping system exit event occurs are recorded, and then the recorded geographic location coordinate point is added to the driving trajectory point set.
[0055] It can be seen that through the above implementation mode, the geographical location coordinate points of the vehicles in the target vehicle set when the preset events occur are used as the driving trajectory point set corresponding to the target vehicle set, which can reduce the consumption of storage and computing resources caused by the huge amount of driving trajectory point data and speed up data processing.
[0056] S102: Add the road segment in the road network data that is closest to the corresponding driving trajectory point and whose distance is less than a first preset threshold to the target road segment set.
[0057] The first preset threshold may be set based on demand, such as the first preset threshold may be set to 50 meters. The embodiment of the present disclosure does not impose any limitation on the first preset threshold.
[0058] In the disclosed embodiment, a road segment may also be referred to as a road segment link, which refers to a road segment connecting two nodes in a road network (ie, a road traffic network).
[0059] In an optional implementation, after obtaining the driving trajectory point set corresponding to the target vehicle set, the target road section corresponding to each driving trajectory point in the driving trajectory point set is first determined, and then the distance between each driving trajectory point and its corresponding target road section is determined. If it is determined that the distance is less than a first preset threshold, the target road section whose distance to the corresponding driving trajectory point is less than the first preset threshold is added to the target road section set.
[0060] In the disclosed embodiment, GIS (Geographic Information System) software can be used to determine the target road section corresponding to each driving trajectory point in the driving trajectory point set, that is, the road section in the road network data that is closest to the corresponding driving trajectory point. This process can be completed using existing methods, so it will not be repeated here.
[0061] S103: Determine the target road sections belonging to each core area from the target road section set.
[0062] Among them, the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region.
[0063] In the embodiment of the present disclosure, before respectively determining the target road sections belonging to each core area from the target road section set, it is first necessary to determine the core areas corresponding to each region.
[0064] In an optional implementation, the road section data in the road network data are matched with the regional boundary data of each region to obtain the road section data corresponding to each region, and the core area corresponding to each region is determined based on the road section data corresponding to each region.
[0065] Among them, the road segment data in the road network data refers to the road segment data connecting two nodes in the road traffic network, which is the basic unit that constitutes the road network data.
[0066] The regional boundary data of each region refers to the data of the geographical area boundary of each region.
[0067] In the disclosed embodiment, the road section data in the road network data are matched with the regional boundary data of each region respectively, and the road section data that has an intersection with the regional boundary of each region is selected to obtain the road section data corresponding to each region respectively, and then based on the road section data corresponding to each region respectively, the core area corresponding to each region is determined.
[0068] In an optional implementation, cluster analysis is performed on the road segment data in each region to determine the core areas corresponding to each region.
[0069] Specifically, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm can be used to perform cluster analysis on the road section data in each region, and then determine the core areas corresponding to each region.
[0070] Specifically, in an optional implementation, the centroid of the road section data in each region is used as the sampling point, the density parameter and the number of dense points are determined, the density parameter is used as the distance threshold of the neighborhood, and the number of dense points is used as the number of sampling points in the field. By performing cluster analysis on the sampling points in each region, the core areas corresponding to each region are determined.
[0071] The density parameter is used to determine the neighborhood range of the sampling point, and the number of dense points is used to determine the density of the core points.
[0072] In the disclosed embodiment, the centroid of the road section data in each region is first extracted, and the centroid of the road section data in each region is used as the sampling point. Then, the density parameter and the number of dense points are determined. Then, through the DBSCAN algorithm, the density parameter is used as the distance threshold of the neighborhood, and the number of dense points is used as the number of sampling points in the field. By performing cluster analysis on the sampling points in each region, clusters of core areas corresponding to each region are obtained, and the areas corresponding to the clusters of core areas corresponding to each region are determined as the core areas corresponding to each region.
[0073] S104: Determine the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the correspondence between the core area and the area.
[0074] In the disclosed embodiment, if it is determined that the core area contains the target road section, the area corresponding to the core area containing the target road section is determined based on the correspondence between the core area and the area, and the area corresponding to the core area containing the target road section is determined as the driving coverage area of the target vehicle set.
[0075] In addition, in order to more accurately determine the driving coverage area of the target vehicle set, in an optional implementation, the number of target road sections belonging to each core area is first determined respectively, and the core area where the number of target road sections is greater than the second preset threshold is determined as the target core area, and then based on the correspondence between the core area and the area, the area corresponding to the target core area is determined as the driving coverage area of the target vehicle set.
[0076] The second preset threshold can be set based on demand, and the embodiment of the present disclosure does not impose any limitation on the first preset threshold.
[0077] In the disclosed embodiment, the number of target road sections belonging to each core area is first determined respectively, and it is determined whether the number of target road sections in each core area is greater than a second preset threshold value. If it is determined whether the number of target road sections is greater than the second preset threshold value, the core area in which the number of target road sections is greater than the second preset threshold value is determined as the target core area. Then, based on the correspondence between the core area and the region, the region corresponding to the target core area is determined, and the region corresponding to the target core area is determined as the driving coverage area of the target vehicle set.
[0078] In the vehicle trajectory matching method provided by the embodiment of the present disclosure, first, a set of driving trajectory points corresponding to the target vehicle set is obtained, wherein the target vehicle set includes vehicles of a specific type, and then the road section in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold is added to the target road section set, and from the target road section set, the target road sections belonging to each core area are determined respectively, wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region, and the driving coverage area of the target vehicle set is determined based on the target road sections belonging to each core area and the corresponding relationship between the core area and the region. It can be seen that the embodiment of the present disclosure can more accurately determine the situation of the area covered by the driving of vehicles in the target vehicle set by determining the target road sections belonging to each core area and determining the driving coverage area of the target vehicle set according to the target road sections belonging to each core area and the corresponding relationship between the core area and the region.
[0079] Based on the above embodiments, the present disclosure provides a vehicle trajectory matching method. Figure 2 , is a flow chart of another vehicle trajectory matching method provided by an embodiment of the present disclosure, the method comprising:
[0080] First, the road network data and regional boundary data are obtained, and then the road section data in the road network data and the regional boundary data of each region are respectively mined to obtain the road network data corresponding to each region. Then, the road network data corresponding to each region are clustered and analyzed to obtain the core areas corresponding to each region.
[0081] Specifically, based on the road network data corresponding to each region, the centroids of the road section data in each region (i.e., the basic units constituting the road network data) are extracted respectively, and the centroids of the road section data in each region are used as sampling points to determine the density parameter and the number of dense points. Then, through the DBSCAN algorithm, the density parameter is used as the distance threshold of the neighborhood, and the number of dense points is used as the number of sampling points in the field. By performing cluster analysis on the sampling points in each region, the clusters of the core areas corresponding to each region are obtained, and the areas corresponding to the clusters of the core areas corresponding to each region are determined as the core areas corresponding to each region.
[0082] A driving trajectory point set corresponding to the target vehicle set is obtained, and a road segment in the road network data that is closest to the corresponding driving trajectory point and whose distance is less than a first preset threshold is added to the target road segment set.
[0083] The driving trajectory point set is a set of geographical location coordinate points when a preset event occurs to vehicles in the target vehicle set.
[0084] After obtaining the core areas and target road section sets corresponding to each region, the number of target road sections belonging to each core area is determined respectively, and it is determined whether the number of target road sections in each core area is greater than the second preset threshold. If it is determined whether the number of target road sections is greater than the second preset threshold, the core area where the number of target road sections is greater than the second preset threshold is determined as the target core area, and then based on the correspondence between the core area and the region, the region corresponding to the target core area is determined, and the region corresponding to the target core area is determined as the driving coverage area of the target vehicle set.
[0085] It can be seen that the embodiment of the present disclosure determines the target road sections belonging to each core area, determines the driving coverage area of the target vehicle set according to the target road sections belonging to each core area and the correspondence between the core area and the area, and can more accurately determine the situation of the driving coverage area of the vehicles in the target vehicle set.
[0086] In addition, the embodiment of the present disclosure uses the set of geographic location coordinate points when preset events occur in the vehicles in the target vehicle set as the set of driving trajectory points corresponding to the target vehicle set, which can reduce the consumption of storage and computing resources caused by the huge amount of driving trajectory point data and speed up data processing.
[0087] Based on the same inventive concept as the above embodiment, the present disclosure also provides a vehicle trajectory matching device, referring to Figure 3 , is a schematic diagram of the structure of a vehicle trajectory matching device provided in an embodiment of the present disclosure, wherein the vehicle trajectory matching device 300 comprises:
[0088] The acquisition module 301 is used to acquire a set of driving trajectory points corresponding to a target vehicle set; wherein the target vehicle set includes vehicles of a specific type;
[0089] An adding module 302, configured to add a road segment in the road network data that is closest to the corresponding driving trajectory point and whose distance is less than a first preset threshold to the target road segment set;
[0090] The first determination module 303 is used to determine the target road sections belonging to each core area from the target road section set; wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region;
[0091] The second determination module 304 is used to determine the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the corresponding relationship between the core area and the area.
[0092] In an optional implementation manner, the second determining module 304 includes:
[0093] A first determination submodule is used to determine the number of target road sections belonging to each core area respectively;
[0094] A second determination submodule, configured to determine a core area where the number of target road sections is greater than a second preset threshold as a target core area;
[0095] The third determination submodule is used to determine the area corresponding to the target core area as the driving coverage area of the target vehicle set based on the corresponding relationship between the core area and the area.
[0096] In an optional implementation, the device further includes:
[0097] A matching module is used to match the road section data in the road network data with the regional boundary data of each region to obtain the road section data corresponding to each region;
[0098] The third determination module is used to determine the core areas corresponding to the respective regions based on the road section data corresponding to the respective regions.
[0099] In an optional implementation manner, the third determination module is specifically configured to:
[0100] By performing cluster analysis on the road section data in each region, the core areas corresponding to each region are determined.
[0101] In an optional implementation manner, the third determination module is specifically configured to:
[0102] The fourth determination submodule is used to use the centroid of the road section data in each area as a sampling point to determine a density parameter and a number of dense points; wherein the density parameter is used to determine the neighborhood range of the sampling point, and the number of dense points is used to determine the density of the core point;
[0103] A fifth determination submodule is used to use the density parameter as a distance threshold of the neighborhood and the number of dense points as the number of sampling points in the area;
[0104] The sixth determination submodule is used to determine the core areas corresponding to the respective regions by performing cluster analysis on the sampling points in the respective regions.
[0105] In an optional implementation, the driving trajectory point set includes geographic location coordinate points of vehicles in the target vehicle set when a preset event occurs.
[0106] In an optional implementation, the preset event includes an automatic assisted navigation driving exit event and / or a lane keeping system exit event.
[0107] In the vehicle trajectory matching device provided by the embodiment of the present disclosure, the embodiment of the present disclosure provides a vehicle trajectory matching method, first, obtain a set of driving trajectory points corresponding to the target vehicle set, wherein the target vehicle set includes vehicles of a specific type, add the road section in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold to the target road section set, and from the target road section set, respectively determine the target road sections belonging to each core area, wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region, and based on the target road sections belonging to each core area and the corresponding relationship between the core area and the region, determine the driving coverage area of the target vehicle set. It can be seen that the embodiment of the present disclosure can more accurately determine the situation of the area covered by the driving of vehicles in the target vehicle set by determining the target road sections belonging to each core area and determining the driving coverage area of the target vehicle set according to the target road sections belonging to each core area and the corresponding relationship between the core area and the region.
[0108] In addition to the above-mentioned method and apparatus, the embodiment of the present disclosure further provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device implements the vehicle trajectory matching method described in the embodiment of the present disclosure.
[0109] In addition, the present disclosure also provides a vehicle trajectory matching device, see Figure 4 As shown, it may include:
[0110] Processor 401, memory 402, input device 403 and output device 404. The number of processors 401 in the vehicle trajectory matching device can be one or more. Figure 4 In some embodiments of the present disclosure, the processor 401, the memory 402, the input device 403 and the output device 404 may be connected via a bus or other means, wherein: Figure 4 The example of connecting through bus is taken in the following.
[0111] The memory 402 can be used to store software programs and modules. The processor 401 runs the software programs and modules stored in the memory 402, thereby performing various functional applications and data processing of the vehicle trajectory matching device. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. The input device 403 may be used to receive input digital or character information, and to generate signal input related to user settings and function control of the vehicle trajectory matching device.
[0112] Specifically in this embodiment, the processor 401 will load the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 will run the applications stored in the memory 402, thereby realizing various functions of the above-mentioned vehicle trajectory matching device.
[0113] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0114] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A vehicle trajectory matching method, characterized in that: The method comprises: Acquire a set of driving trajectory points corresponding to a target vehicle set; wherein the target vehicle set includes vehicles of a specific type; Adding a road segment in the road network data that is closest to the corresponding driving trajectory point and whose distance is less than a first preset threshold to the target road segment set; From the target road segment set, target road segments belonging to each core area are determined respectively; wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road segments in the corresponding region; Based on the target road sections belonging to each core area and the corresponding relationship between the core area and the area, the driving coverage area of the target vehicle set is determined.
2. The method according to claim 1, characterized in that The determining of the driving coverage area of the target vehicle set based on the target road sections corresponding to the core areas and the corresponding relationship between the core areas and the areas includes: Determine the number of target road sections belonging to each core area respectively; Determine a core area where the number of target road sections is greater than a second preset threshold as a target core area; Based on the correspondence between the core area and the region, the region corresponding to the target core area is determined as the driving coverage area of the target vehicle set.
3. The method according to claim 1, characterized in that Before determining the target road sections corresponding to the respective core areas from the target road section set, the method further includes: Match the road section data in the road network data with the regional boundary data of each region to obtain the road section data corresponding to each region; Based on the road segment data corresponding to each region, the core areas corresponding to each region are determined.
4. The method according to claim 3, characterized in that The determining of the core areas corresponding to the respective regions based on the road segment data corresponding to the respective regions includes: By performing cluster analysis on the road section data in each region, the core areas corresponding to each region are determined.
5. The method according to claim 4, characterized in that The cluster analysis is performed on the road segment data in each region to determine the core areas corresponding to each region, including: The centroids of the road segment data in each region are respectively used as sampling points to determine the density parameter and the number of dense points; wherein the density parameter is used to determine the neighborhood range of the sampling point, and the number of dense points is used to determine the density of the core point; The density parameter is used as the distance threshold of the neighborhood, and the number of dense points is used as the number of sampling points in the area; By performing cluster analysis on the sampling points in each region, the core areas corresponding to each region are determined.
6. The method according to claim 1, characterized in that The driving trajectory point set includes geographic location coordinate points when a preset event occurs to vehicles in the target vehicle set.
7. The method according to claim 6, wherein the preset event comprises an automatic assisted navigation driving exit event and / or a lane keeping system exit event.
8. A vehicle trajectory matching device, characterized in that: The device comprises: An acquisition module, used to acquire a set of driving trajectory points corresponding to a target vehicle set; wherein the target vehicle set includes vehicles of a specific type; An adding module, used for adding a road segment in the road network data that is closest to the corresponding driving trajectory point and the distance is less than a first preset threshold to the target road segment set; A first determination module is used to determine the target road sections belonging to each core area from the target road section set; wherein the core area has a corresponding relationship with the region, and the core area is an area with dense road sections in the corresponding region; The second determination module is used to determine the driving coverage area of the target vehicle set based on the target road sections belonging to each core area and the corresponding relationship between the core area and the area.
9. A vehicle trajectory matching device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device implements the method according to any one of claims 1 to 7.