Multi-branch intersection positioning method, device, equipment, medium and program product

Abstract

The application discloses a multi-fork intersection positioning method, device, equipment, medium and program product, and relates to the technical field of intelligent transportation. The method comprises the following steps: acquiring a target intersection data set of a target intersection in map data of a target area; the target intersection comprises a target node with a number of associated road segments greater than or equal to a preset number of road segments, and associated road segments of the target node; determining a multi-fork intersection data set in the target intersection data according to the road section direction angle of the associated road sections contained in the target intersection; and determining positioning information of the multi-fork intersection according to the multi-fork intersection data set. The multi-fork intersection is identified and positioned based on the map data, and it is not necessary to rely on manual on-site collection of positioning information by personally arriving at the multi-fork intersection, so that the human cost is reduced, the efficiency of multi-fork intersection positioning is improved, and the reliability of multi-fork intersection positioning data is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a method, device, equipment, medium, and program product for locating multiple intersections. Background Technology

[0002] In urban road management, multi-way intersections are key nodes in the traffic network. In complex traffic scenarios, the collection of location information for multi-way intersections often relies on manual labor.

[0003] Relying on manual collection of location information for multi-way intersections requires staff to personally visit each intersection for on-site surveys and data recording, consuming significant manpower and time. Furthermore, human intervention increases the risk of errors, and manually collected data is prone to omissions and lacks reliability.

[0004] With the acceleration of urbanization and the increasing traffic flow, existing methods for manually collecting location information of multi-way intersections can no longer meet the needs of modern traffic management. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, medium, and program product for locating multiple intersections, in order to solve the problems of low efficiency, high labor costs, and low data reliability in the method of manually collecting location information of multiple intersections.

[0006] In a first aspect, embodiments of the present invention provide a method for locating multiple intersections, including:

[0007] Obtain the target intersection dataset from the map data of the target area; the target intersection includes target nodes with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target nodes;

[0008] The multi-intersection dataset in the target intersection dataset is determined based on the road segment direction angles of the associated road segments contained in the target intersection.

[0009] The location information of the multi-intersection is determined based on the multi-intersection dataset.

[0010] Secondly, embodiments of the present invention provide a multi-intersection positioning device, comprising:

[0011] The intersection dataset acquisition module is used to acquire the target intersection dataset from the map data of the target area; the target intersection includes: target nodes with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target nodes;

[0012] A multi-intersection determination module is used to determine the multi-intersection dataset in the target intersection dataset based on the road segment direction angle of the associated road segments contained in the target intersection;

[0013] The multi-intersection positioning module is used to determine the positioning information of the multi-intersection based on the multi-intersection dataset.

[0014] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the multi-intersection positioning method according to any embodiment of the present invention.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the multi-intersection positioning method described in any embodiment of the present invention.

[0019] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the multi-intersection positioning method described in any embodiment of the present invention.

[0020] The technical solution of this invention involves acquiring a target intersection dataset from map data of a target area. The target intersection includes a target node with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target node. A multi-way intersection dataset is determined based on the road segment direction angles of the associated road segments included in the target intersection. The location information of the multi-way intersection is then determined based on the multi-way intersection dataset. By determining the number and direction angles of the roads associated with nodes in the map data, intersections designated as multi-way intersections are identified, and their location information is determined based on the map data. This achieves the identification and location of multi-way intersections based on map data. Since it eliminates the need for manual on-site collection of location information at multi-way intersections, it solves the problems of low efficiency, high labor costs, and low reliability associated with manual collection of multi-way intersection location information. This reduces labor costs, improves the efficiency of multi-way intersection location, and enhances the reliability of multi-way intersection location data by eliminating the need for manual intervention and horizontal measurement.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart of a multi-intersection positioning method provided in an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of a multi-intersection positioning device provided in an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the structure of an electronic device for implementing the multi-intersection positioning method of this invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Figure 1This is a flowchart illustrating a multi-intersection positioning method provided in an embodiment of the present invention. This embodiment is applicable to situations where multi-intersections are located based on map data. The method can be executed by a multi-intersection positioning device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110. Obtain the target intersection dataset from the map data of the target area; the target intersection includes the target node with a number of associated road segments greater than or equal to the preset number of road segments, and the associated road segments of the target node.

[0030] The target area can be understood as an area with multiple intersections awaiting analysis and location. The map data for the target area can come from a Standard Definition Map (SD map). An SD map is a navigation map, and its data can include: road topology, road type, and road attributes. Road attributes can include road direction angle, road gradient, road latitude and longitude, and speed limits. In an SD map, nodes and links are the basic elements that make up the map. Nodes represent connection points in the road network, such as road intersections or the start and end points of roads. Links represent a section of road between two nodes.

[0031] In this embodiment, an associated road segment can be understood as a road segment connected to a node, and the associated road segments of a node constitute an intersection. A target node can be understood as a node whose number of associated road segments is greater than or equal to a preset number of road segments. A target intersection includes: the target node and its associated road segments. The preset number of road segments can be understood as a preset minimum number of associated road segments for a target node, which can be determined based on the definition of the number of branches included in a multi-branch intersection. For example, an intersection with three or more branches is generally defined as a multi-branch road, and the preset number of branches can be set to three; if an intersection with five or more branches is defined as a multi-branch road, the preset number of branches can be set to five. The target intersection dataset can be understood as a set consisting of the target node and associated road segments in the target intersection.

[0032] Specifically, spatial matching is performed between the selected target area data and map data to determine the map data for the target area. All nodes in the map data of the target area are obtained, and the number of road segments associated with each node is counted. Nodes with a number of associated road segments greater than or equal to a preset number of road segments are identified as target nodes, and each target node and its associated road segments are identified as a target intersection. Data for each target intersection is then extracted from the map data to form a target intersection dataset.

[0033] S120. Determine the multi-intersection dataset in the target intersection dataset based on the road segment direction angles of the associated road segments included in the target intersection.

[0034] A multi-intersection dataset can be understood as a collection of data representing target intersections with multiple branching roads. This dataset can include road topology, road type, and road attributes. Road attributes can include road direction angle, road slope, road latitude and longitude, and speed limits. The road segment direction angle can be understood as the angle between the travel direction of the associated road segment and true north in the map coordinate system. A multi-intersection can be understood as an intersection with multiple branching roads. It is understood that one or more multi-intersections may exist within a target area.

[0035] Specifically, the direction angles of the road segments associated with a multi-way intersection should all be different. Therefore, based on the direction angles of the associated road segments included in the target intersection dataset, we determine whether the target intersection is a multi-way intersection. Then, we obtain the dataset corresponding to the target intersection that is a multi-way intersection from the target intersection dataset, thus creating the multi-way intersection dataset.

[0036] S130. Based on the multi-intersection dataset of the target intersection dataset, determine the location information of the multi-intersection.

[0037] The location information for a multi-way intersection can include the location information of the target nodes and associated roads within the target intersection. The location information for a multi-way intersection can be represented using scalar coordinates or as a location vector that includes both coordinates and road segment direction.

[0038] Specifically, the location information of the multi-intersection is determined based on the location information of the target node and associated road segments contained in the multi-intersection dataset.

[0039] The technical solution of this invention involves acquiring a target intersection dataset from map data of a target area. The target intersection includes: a target node with a number of associated road segments greater than or equal to a preset number of road segments, and associated road segments of the target node. A multi-way intersection dataset is determined based on the road segment direction angles of the associated road segments included in the target intersection. The location information of the multi-way intersection is then determined based on the multi-way intersection dataset. By determining the number and direction angles of roads associated with nodes in the map data, intersections designated as multi-way intersections are identified, and their location information is determined based on the map data. This achieves the identification and location of multi-way intersections based on map data, eliminating the need for manual on-site collection of location information at the intersections, reducing labor costs, improving the efficiency of multi-way intersection location, and enhancing the reliability of the location data by eliminating the need for manual intervention and level measurement.

[0040] As an optional embodiment of this application, S120, determining the road segment direction angle of the associated road segments included in the target intersection, is based on the multi-intersection dataset in the target intersection dataset, including:

[0041] S121. Based on the target intersection dataset, determine the road segment direction angle of the associated road segment for each target node in the target intersection, and determine the road segment angle range into which the associated road segment direction angle enters.

[0042] In this context, the road segment angle interval can be understood as an interval dividing the angle range of related road segments. For example, the road segment angle range of 0° to 360° can be divided into multiple road segment angle intervals. For instance, starting from due north as 0°, eight intervals can be divided in 45° increments.

[0043] Specifically, based on the location information of the target nodes and associated road segments contained in the target intersection dataset, the road segment direction angle of the associated road segment is calculated, and the road segment angle interval into which the road segment direction angle falls is determined.

[0044] S122. Target intersections where the number of road segment angle intervals is greater than or equal to the preset number of intervals are defined as multi-way intersections.

[0045] The number of preset intervals can be determined according to the definition of a multi-way intersection. For example, if an intersection with five or more branches is defined as a multi-way intersection, the number of preset intervals can be set to five.

[0046] Specifically, for each target intersection, the number of road segment angles falling into the road segment angle intervals of the multiple associated road segments included in the target intersection is counted; if the number of falling into the road segment angle intervals is greater than or equal to the preset number of intervals, the target intersection is determined to be a multi-way intersection.

[0047] S123. Determine the multi-intersection dataset based on the multi-intersections in the target intersection dataset.

[0048] Specifically, the dataset corresponding to the target intersections that are considered to be multi-way intersections is obtained from the target intersection dataset and used as the multi-way intersection dataset.

[0049] This embodiment initially identifies potential multi-way intersections by utilizing the directional characteristics of road segments at multi-way intersections. Then, it uses a crowdsourced trajectory dataset to represent the trajectory features of multi-way intersections, thereby identifying multi-way intersections from the potential ones, further improving the accuracy and reliability of multi-way intersection identification.

[0050] In complex traffic scenarios, multi-way intersections may intersect with other roads in space, such as elevated roads, which can interfere with the identification of multi-way intersections. In order to further improve the accuracy and reliability of multi-way intersection identification, this application combines map data and crowdsourced trajectory data for multi-way intersection identification.

[0051] As an optional embodiment of this application, the road segment direction angles of the associated road segments included in the target intersection are determined based on the multi-intersection dataset in the target intersection dataset, including:

[0052] S121. Determine potential multi-branch intersections based on the road segment direction angles of the associated road segments of the target nodes in the target intersection dataset.

[0053] Among them, potential multi-way intersections can be understood as intersections that may have multiple branches but require further confirmation to determine whether they are target intersections with multiple branches.

[0054] Specifically, based on the target intersection dataset, the road segment direction angle of the associated road segment of each target node in the target intersection is determined, and the road segment angle interval into which the road segment direction angle of the associated road segment is entered is determined; target intersections in which the number of road segment angle intervals into which the road segment direction angle is entered is determined as potential multi-branch intersections.

[0055] S122. Obtain the potential multi-way intersection dataset from the target intersection dataset, and determine the multi-way intersection dataset from the potential multi-way intersections based on the obtained valid crowdsourced trajectory dataset of the target area.

[0056] Among them, the effective crowdsourced trajectory dataset can be understood as a pre-processed crowdsourced trajectory dataset. A crowdsourced trajectory dataset can be understood as a collection of trajectory data collected through crowdsourcing, which can originate from the movement trajectories recorded and shared by vehicles traveling on roads in the target area using GPS devices or other positioning tools. The potential multi-way intersection dataset can be understood as the dataset corresponding to target intersections that are considered potential multi-way intersections.

[0057] Specifically, the target intersection dataset is obtained from the target intersection dataset as a potential multi-way intersection dataset, and the effective crowdsourced trajectory dataset for the target area is also obtained. Based on the potential multi-way intersection dataset and the effective crowdsourced trajectory dataset, multi-way intersections are further identified from the potential multi-way intersections according to the trajectory features of the multi-way intersections. Trajectory features may include, for example, trajectory speed and / or trajectory direction.

[0058] This embodiment utilizes the distribution characteristics of the direction angles of road segments at multi-way intersections to initially identify potential multi-way intersections. Then, it employs a crowdsourced trajectory dataset to represent the trajectory features of multi-way intersections, thereby identifying multi-way intersections from the potential ones, further improving the accuracy and reliability of multi-way intersection identification.

[0059] As an optional embodiment of this application, S122, determining multiple intersections from potential multiple intersections based on the obtained valid crowdsourced trajectory dataset of the target area, includes:

[0060] S1221. Spatial matching is performed between the effective crowdsourced trajectory dataset and the potential multi-intersection dataset to obtain the matching trajectory dataset that matches the potential multi-intersection.

[0061] The matching trajectory dataset can be understood as a collection of trajectory data that spatially matches potential multi-way intersections within the effective crowdsourced trajectory dataset.

[0062] Specifically, based on spatial geographic location, the effective crowdsourced trajectory dataset and the potential multi-way intersection dataset are joined to obtain a matching trajectory dataset consisting of matching trajectories corresponding to each potential multi-way intersection in the potential multi-way intersection dataset.

[0063] S1222. Based on the trajectory direction angles in the matching trajectory dataset, determine the multi-intersection dataset from the potential multi-intersection dataset.

[0064] Specifically, since a multi-way intersection has multiple branches, and the direction angle of the driving trajectory on each branch is different, for each potential multi-way intersection, based on the trajectory direction angles of each trajectory in the matching trajectory dataset corresponding to the potential multi-way intersection, it can be further confirmed whether the potential multi-way intersection conforms to the trajectory direction angle distribution pattern of multi-way intersections. Potential multi-way intersections that conform to the trajectory direction angle distribution pattern are identified as multi-way intersections. The multi-way intersection dataset is then determined from the potential multi-way intersection dataset.

[0065] This embodiment determines the trajectory dataset that matches the multi-way intersection and uses the trajectory direction angle in the trajectory dataset to identify the multi-way intersection from the potential multi-way intersections. It makes full use of the distribution pattern of trajectory direction angles of multi-way intersections, thereby improving the accuracy and reliability of multi-way intersection identification.

[0066] As an optional implementation of the above embodiments, S1221, spatial matching is performed between the obtained effective crowdsourced trajectory dataset of the target area and the potential multi-way intersection dataset to obtain a matching trajectory dataset that matches the potential multi-way intersection, including:

[0067] A1. Obtain the valid crowdsourced trajectory dataset for the target area.

[0068] For example, a valid crowdsourced trajectory dataset for a target area can be obtained by: spatially matching the selected target area data and the crowdsourced trajectory dataset to determine the crowdsourced trajectory dataset for the target area; preprocessing the crowdsourced trajectory dataset to obtain a valid crowdsourced trajectory dataset; the preprocessing may include: data filtering and cleaning.

[0069] A2. Project the trajectory points of each valid trajectory in the valid crowdsourced trajectory dataset onto the potential multi-intersection dataset to obtain the projection points corresponding to the trajectory points.

[0070] Specifically, for each valid trajectory in the valid crowdsourced trajectory dataset, the trajectory points are projected into the potential multi-way intersection dataset according to the location information of the trajectory points contained in the trajectory, so as to obtain the projection point corresponding to each trajectory point.

[0071] A3. Based on the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection, and the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection, determine the matching trajectory of the potential multi-way intersection.

[0072] In a specific example, the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection is calculated, as well as the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection. Valid trajectories with an average distance less than a preset distance and / or a difference in direction angle less than a preset angle are determined as the matching trajectories of the potential multi-way intersection.

[0073] In another specific example, the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection is calculated. Trajectories with an average distance less than a preset distance or ranked in ascending order of average distance are identified as potential matching trajectories for the potential multi-way intersection. Potential matching trajectories with a direction angle difference of the associated road segment of the potential multi-way intersection less than a preset angle are identified as matching trajectories for the potential multi-way intersection.

[0074] In another specific example, the direction angle difference between each valid trajectory and the associated road segment of the potential multi-way intersection is calculated. The trajectories with a direction angle difference less than a preset angle or ranked in ascending order of direction angle difference are determined as potential matching trajectories for the potential multi-way intersection. The trajectories with an average distance less than a preset distance from the associated road segment of the potential multi-way intersection are determined as matching trajectories for the potential multi-way intersection.

[0075] A4. Obtain the matching trajectory dataset, which consists of the valid crowdsourced trajectory data corresponding to the matching trajectory of potential multi-way intersections, from the valid crowdsourced trajectory dataset.

[0076] Specifically, after determining the matching trajectory for potential multi-way intersections, the dataset corresponding to the trajectory used as the matching trajectory for potential multi-way intersections is obtained from the effective crowdsourced trajectory dataset.

[0077] This embodiment determines the spatially matching trajectory of potential multi-way intersections by using the difference in direction angle and projection distance between the associated road segments of multi-way intersections and the trajectories in the effective crowdsourced trajectory dataset. This provides a foundation for further determining multi-way intersections in map data based on the effective crowdsourced trajectory data.

[0078] In an optional embodiment, A1, based on the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection, and the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection, determines the matching trajectory for the potential multi-way intersection, including:

[0079] A11. Calculate the difference in direction angle between the effective trajectory and the associated road segment of the potential multi-way intersection; determine the effective trajectory with a direction angle difference less than a preset angle as the potential matching trajectory of the potential multi-way intersection.

[0080] The direction angle difference can be understood as the difference between the direction angle of the effective trajectory and the direction angle of the associated road segment. The direction angle of the effective trajectory can be the angle between the direction of movement of the effective trajectory and the due north direction in the map coordinate system, while the direction angle of the associated road segment can be the angle between the road travel direction of the associated road segment and the due north direction in the map coordinate system. The preset angle can be determined based on the positioning accuracy of the map data. A potential matching trajectory can be understood as an effective trajectory that may spatially match a potential multi-way intersection.

[0081] Specifically, the direction angle of each valid trajectory and the direction angle of each associated road segment at a potential multi-way intersection are calculated, and the difference in direction angle between each valid trajectory and each associated road segment is calculated. Valid trajectories with a direction angle difference less than a preset angle are considered potential matching trajectories that may spatially match the associated road segments at potential multi-way intersections.

[0082] A12. Calculate the average projected distance from the potential matching trajectory to the associated road segment of the corresponding potential multi-way intersection; determine the potential matching trajectory with an average distance less than the preset distance as the matching trajectory of the potential multi-way intersection.

[0083] The preset distance can be determined based on the positioning accuracy of the map data.

[0084] Specifically, for each potential matching trajectory, the average distance from the projection point corresponding to each trajectory point of the potential matching trajectory to the associated road segment of the potential multi-way intersection is calculated to obtain the average distance; the potential matching trajectory of the potential multi-way intersection is determined by the potential matching trajectory whose distance to any associated road segment of the potential multi-way intersection is less than the preset distance.

[0085] In this embodiment, the potential matching trajectory is first determined by the difference in direction angle, which greatly reduces the distance calculation of the matching trajectory. Then, the matching trajectory is determined from the potential matching trajectory by the distance from the projection point corresponding to the trajectory point to the associated road segment of the potential multi-way intersection. This reduces the amount of calculation while performing spatial matching in all aspects.

[0086] As another optional implementation of the above embodiments, S1222, determining multiple intersections from potential multiple intersections based on the trajectory direction angles in the matching trajectory dataset, includes:

[0087] B1. Calculate the trajectory direction angle of the line connecting every two adjacent trajectory points along the travel direction in the matching trajectory dataset, and determine the trajectory angle range into which the trajectory direction angle enters.

[0088] B2. Potential multi-way intersections whose number of trajectory angle intervals is greater than or equal to the preset number of intervals are identified as multi-way intersections.

[0089] B3. Determine the multi-intersection dataset based on the multi-intersections in the potential multi-intersection dataset.

[0090] The trajectory angle interval can be understood as multiple intervals for dividing the direction angle of the trajectory. For example, the trajectory angle range of 0° to 360° can be divided into multiple trajectory angle intervals. For example, taking due north as 0°, starting from 0°, divide into 8 intervals at 45° intervals.

[0091] Specifically, the trajectory movements of multiple related road segments at a multi-way intersection exhibit distinct directions of motion. When the number of trajectories is sufficiently large, the trajectory direction angles at the multi-way intersection display clear angular distinctions. Therefore, for the matching trajectory dataset corresponding to potential multi-way intersections, every two adjacent trajectory points along the travel direction on each valid trajectory are connected, the trajectory direction angle of the connecting line is calculated, and the trajectory angle interval into which the trajectory direction angle falls is determined. The number of trajectory angle intervals into which the trajectory direction angle falls is counted. If the number of intervals into which the trajectory direction angle falls is greater than or equal to a preset number of intervals, the potential multi-way intersection is identified as a multi-way intersection. The multi-way intersection dataset corresponding to the multi-way intersection is then obtained from the potential multi-way intersection dataset.

[0092] This embodiment utilizes the characteristics of trajectory movement direction at multi-way intersections. Based on the trajectory point direction angles of the matched trajectory dataset, it identifies multi-way intersections from potential multi-way intersections determined by map data. Compared to methods that rely entirely on manual on-site surveys, this reduces labor costs and improves efficiency. Furthermore, since it does not require human intervention or rely on manual measurement, and combines matched effective trajectory crowdsourced trajectory data with map data to determine multi-way intersections, it further improves the accuracy and reliability of multi-way intersection identification.

[0093] As an optional embodiment of this application, S130, determining the location information of the multi-intersection based on the multi-intersection dataset, includes:

[0094] The location vector of the multi-intersection is determined based on the location of the target node of the multi-intersection in the multi-intersection dataset, as well as the length and direction of the associated road segment.

[0095] The location of the target node can be represented by the centroid coordinates of the target intersection.

[0096] Specifically, based on the multi-intersection dataset, the location of the target nodes within the multi-intersection is determined. The direction of the associated road segments of the target nodes and their lengths in each direction are used to determine the location vector of the multi-intersection, thus achieving localization of the multi-intersection. Additionally, the total length scalar of the associated road segments of the nodes at the multi-intersection can be determined as the mileage of the multi-intersection, depending on requirements.

[0097] This embodiment uses the location vector formed by the location of the target node at the multi-way intersection and the length and direction of the associated road segments to represent the positioning information of the multi-way intersection. The location information can be used to determine the location of the multi-way intersection and the distribution of the branch road segments.

[0098] Figure 2 This is a schematic diagram of a multi-intersection positioning device provided in an embodiment of the present invention. Figure 2 As shown, the device includes: an intersection dataset acquisition module 210, a multi-intersection determination module 220, and a multi-intersection positioning module 230; wherein,

[0099] The intersection dataset acquisition module 210 is used to acquire the target intersection dataset from the map data of the target area; the target intersection includes: target nodes with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target nodes;

[0100] The multi-intersection determination module 220 is used to determine the multi-intersection dataset in the target intersection dataset based on the road segment direction angle of the associated road segments included in the target intersection;

[0101] The multi-intersection positioning module 230 is used to determine the positioning information of the multi-intersection based on the multi-intersection dataset.

[0102] The technical solution of this invention involves acquiring a target intersection dataset from map data of a target area. The target intersection includes: a target node with a number of associated road segments greater than or equal to a preset number of road segments, and associated road segments of the target node. A multi-way intersection dataset is determined from the target intersection dataset based on the road segment direction angles of the associated road segments included in the target intersection. The location information of the multi-way intersection is then determined based on the multi-way intersection dataset. By determining the number and direction angles of roads associated with nodes in the map data, intersections designated as multi-way intersections are identified, and their location information is determined based on the map data. This achieves the identification and location of multi-way intersections based on map data, eliminating the need for manual on-site collection of location information at the intersections, reducing labor costs, improving the efficiency of multi-way intersection location, and enhancing the reliability of the multi-way intersection location data.

[0103] Optionally, the multi-intersection determination module 220 includes:

[0104] The first road segment direction angle determination submodule is used to determine the road segment direction angle of the associated road segment of each target node in the target intersection based on the target intersection dataset, and to determine the road segment angle range into which the road segment direction angle of the associated road segment enters.

[0105] The first multi-intersection determination submodule is used to determine the target intersection as a multi-intersection if the number of road segment angle intervals into which the road segment direction angles enter is greater than or equal to the number of preset intervals.

[0106] The first multi-intersection dataset determination submodule is used to determine the multi-intersection dataset based on the multi-intersections in the target intersection dataset.

[0107] Optionally, the multi-intersection determination module 220 includes:

[0108] The potential multi-way intersection determination submodule is used to determine potential multi-way intersections based on the road segment direction angles of the associated road segments of the target nodes in the target intersection dataset.

[0109] The second multi-way intersection determination submodule is used to obtain the potential multi-way intersection dataset of the potential multi-way intersection from the target intersection dataset, and determine the multi-way intersection dataset from the potential multi-way intersection based on the obtained valid crowdsourced trajectory dataset of the target area.

[0110] Optionally, the second multi-way intersection determination submodule includes:

[0111] The trajectory matching unit is used to spatially match the effective crowdsourced trajectory dataset with the potential multi-way intersection dataset to obtain a matching trajectory dataset that matches the potential multi-way intersection.

[0112] A multi-way intersection determination unit is used to determine a multi-way intersection dataset from the potential multi-way intersection dataset based on the trajectory direction angle in the matching trajectory dataset.

[0113] Optionally, the trajectory matching unit includes:

[0114] The trajectory data acquisition subunit is used to acquire the effective crowdsourced trajectory dataset of the target area;

[0115] The projection subunit is used to project the trajectory points of each valid trajectory in the valid crowdsourced trajectory dataset onto the potential multi-intersection dataset to obtain the projection points corresponding to the trajectory points.

[0116] The matching trajectory determination subunit is used to determine the matching trajectory of the potential multi-way intersection based on the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection, and the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection.

[0117] The trajectory data acquisition subunit is used to acquire a matching trajectory dataset from the effective crowdsourced trajectory dataset, which consists of the effective crowdsourced trajectory data corresponding to the matching trajectory of the potential multi-way intersection.

[0118] Optionally, a trajectory matching determination sub-unit is used specifically for:

[0119] Calculate the difference in direction angle between the effective trajectory and the associated road segment of the potential multi-way intersection;

[0120] The effective trajectory with the direction angle difference less than a preset angle is determined as the potential matching trajectory of the potential multi-way intersection;

[0121] Calculate the average projected distance from the potential matching trajectory to the associated road segment corresponding to the potential multi-way intersection;

[0122] The potential matching trajectory with an average distance less than a preset distance is determined as the matching trajectory of the potential multi-way intersection.

[0123] Optionally, the multi-intersection determination unit is specifically used for:

[0124] Calculate the trajectory direction angle of the line connecting every two adjacent trajectory points along the travel direction in the matching trajectory dataset, and determine the trajectory angle range into which the trajectory direction angle enters;

[0125] Potential multi-way intersections whose number of trajectory angle intervals is greater than or equal to the number of preset intervals are identified as multi-way intersections.

[0126] The multi-way intersection dataset is determined based on the multi-way intersections in the potential multi-way intersection dataset.

[0127] Optionally, the potential multi-way intersection determination submodule is specifically used for:

[0128] Based on the target intersection dataset, determine the road segment direction angle of the associated road segment for each target node in the target intersection, and determine the road segment angle range into which the road segment direction angle of the associated road segment enters;

[0129] Target intersections whose number of road segment angle intervals is greater than or equal to the preset number of intervals are identified as potential multi-way intersections.

[0130] Optionally, the multi-intersection positioning module 230 is specifically used for:

[0131] The position vector of the multi-way intersection is determined based on the position of the target node of the multi-way intersection in the multi-way intersection dataset, as well as the length and direction of the associated road segment.

[0132] The multi-intersection positioning device provided in the embodiments of the present invention can execute the multi-intersection positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0133] Figure 3 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0134] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0135] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0136] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as multi-way intersection localization methods.

[0137] In some embodiments, the multi-way intersection localization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multi-way intersection localization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the multi-way intersection localization method by any other suitable means (e.g., by means of firmware).

[0138] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] In some embodiments, the multi-intersection localization method can be implemented as a computer program, which is implicitly included in a computer program product. When executed by a processor, the computer program implements the multi-intersection localization method of the present invention. The computer program product can be understood as a software product that primarily implements its solution through a computer program. The computer program used to implement the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer program causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0141] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0142] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0143] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0144] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0145] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for locating multiple intersections, characterized in that, include: Obtain the target intersection dataset from the map data of the target area; The target intersection includes a target node with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target node; The multi-intersection dataset in the target intersection data is determined based on the road segment direction angles of the associated road segments included in the target intersection. The location information of the multi-intersection is determined based on the multi-intersection dataset.

2. The multi-intersection positioning method according to claim 1, characterized in that, The step of determining the multi-intersection dataset in the target intersection data based on the road segment direction angles of the associated road segments included in the target intersection includes: Based on the target intersection dataset, determine the road segment direction angle of the associated road segment for each target node in the target intersection, and determine the road segment angle range into which the road segment direction angle of the associated road segment enters; A target intersection in which the number of road segment angle intervals into which the road segment direction angles enter is greater than or equal to the number of preset intervals is defined as a multi-branch intersection. The multi-way intersection dataset is determined based on the multi-way intersections in the target intersection dataset.

3. The multi-intersection positioning method according to claim 1, characterized in that, The step of determining the multi-intersection dataset in the target intersection data based on the road segment direction angles of the associated road segments included in the target intersection includes: Potential multi-way intersections are determined based on the road segment orientation angles of the associated road segments of the target nodes in the target intersection dataset; The potential multi-way intersection dataset is obtained from the target intersection dataset, and the multi-way intersection dataset is determined from the potential multi-way intersections based on the obtained valid crowdsourced trajectory dataset of the target area.

4. The multi-intersection positioning method according to claim 3, characterized in that, The step of determining the multi-way intersection dataset from the potential multi-way intersections based on the obtained valid crowdsourced trajectory dataset of the target area includes: Spatial matching is performed between the effective crowdsourced trajectory dataset and the potential multi-way intersection dataset to obtain a matching trajectory dataset that matches the potential multi-way intersection. The multi-way intersection dataset is determined from the potential multi-way intersection dataset based on the trajectory direction angle in the matching trajectory dataset.

5. The multi-intersection positioning method according to claim 4, characterized in that, The step of spatially matching the effective crowdsourced trajectory dataset with the potential multi-way intersection dataset to obtain a matching trajectory dataset that matches the potential multi-way intersection includes: Obtain the valid crowdsourced trajectory dataset for the target area; Project the trajectory points of each valid trajectory in the valid crowdsourced trajectory dataset onto the potential multi-intersection dataset to obtain the projection points corresponding to the trajectory points; The matching trajectory for the potential multi-way intersection is determined based on the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection, and the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection. The matching trajectory dataset is formed by extracting the valid crowdsourced trajectory data corresponding to the matching trajectory of the potential multi-way intersection from the valid crowdsourced trajectory dataset.

6. The multi-intersection positioning method according to claim 5, characterized in that, The step of determining the matching trajectory for the potential multi-way intersection based on the average distance from the projection point of each valid trajectory to the associated road segment of the potential multi-way intersection, and the difference in direction angle between the valid trajectory and the associated road segment of the potential multi-way intersection, includes: Calculate the difference in direction angle between the effective trajectory and the associated road segment of the potential multi-way intersection; The effective trajectory with the direction angle difference less than a preset angle is determined as the potential matching trajectory of the potential multi-way intersection; Calculate the average projected distance from the potential matching trajectory to the associated road segment corresponding to the potential multi-way intersection; The potential matching trajectory with an average distance less than a preset distance is determined as the matching trajectory of the potential multi-way intersection.

7. The multi-intersection positioning method according to claim 4, characterized in that, The step of determining the multi-way intersection dataset from the potential multi-way intersection dataset based on the trajectory direction angle in the matching trajectory dataset includes: Calculate the trajectory direction angle of the line connecting every two adjacent trajectory points along the travel direction in the matching trajectory dataset, and determine the trajectory angle range into which the trajectory direction angle enters; Potential multi-way intersections whose number of trajectory angle intervals is greater than or equal to the number of preset intervals are identified as multi-way intersections. The multi-way intersection dataset is determined based on the multi-way intersections in the potential multi-way intersection dataset.

8. The multi-intersection positioning method according to claim 3, characterized in that, The step of determining potential multi-way intersections based on the road segment direction angles of the associated road segments of the target nodes in the target intersection dataset includes: Based on the target intersection dataset, determine the road segment direction angle of the associated road segment for each target node in the target intersection, and determine the road segment angle range into which the road segment direction angle of the associated road segment enters; Target intersections whose number of road segment angle intervals is greater than or equal to the preset number of intervals are identified as potential multi-way intersections.

9. The multi-intersection positioning method according to any one of claims 1-8, characterized in that, Determining the location information of the multi-intersection based on the multi-intersection dataset includes: The position vector of the multi-way intersection is determined based on the position of the target node of the multi-way intersection in the multi-way intersection dataset, as well as the length and direction of the associated road segment.

10. A multi-intersection positioning device, characterized in that, include: The intersection dataset acquisition module is used to acquire the target intersection dataset from the map data of the target area; The target intersection includes: a target node with a number of associated road segments greater than or equal to a preset number of road segments, and the associated road segments of the target node; A multi-intersection determination module is used to determine the multi-intersection dataset in the target intersection dataset based on the road segment direction angle of the associated road segments contained in the target intersection; The multi-intersection positioning module is used to determine the positioning information of the multi-intersection based on the multi-intersection dataset.

11. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-way intersection positioning method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multi-intersection positioning method according to any one of claims 1-9.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the multi-intersection positioning method according to any one of claims 1-9.

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