CN117290570A - Navigation road network data generation method, device, equipment and storage medium - Google Patents
Navigation road network data generation method, device, equipment and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a navigation road network data generation method, apparatus, device and storage medium. According to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data, and the data of the lane line of the lane is included in the data of the lane; determining a center line of the lane based on the data of the lane lines; the navigation road network data is generated based on the center line of the lane, the data of the lane can be directly obtained from the high-precision road network data, then the center line of the lane is generated based on the data of the lane, and further the navigation road network data is generated, mapping of the navigation road network data is not needed, mapping resources are saved, and the navigation road network data can be quickly and accurately generated.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a navigation road network data generation method, device, equipment and storage medium.
Background
With the rapid development of navigation technology, navigation maps (Standard Definition Map, SD maps) are becoming increasingly important for the travel of users.
The navigation map application of the user terminal can plan the optimal travel path for the user and provide more and more road navigation services according to the travel demands of the user, so that the travel of the user is greatly facilitated.
The navigation map is generated from navigation road network data. At present, navigation road network data is mainly generated by combining satellite mapping with manual flow labeling, and therefore, the efficiency is low and the accuracy is low, and a method for quickly and accurately generating the navigation road network data is needed to overcome the defects in the generation of the navigation road network data in the prior art.
Disclosure of Invention
In order to solve the technical problems, the present disclosure provides a navigation road network data generation method, a device, equipment and a storage medium.
A first aspect of an embodiment of the present disclosure provides a navigation road network data generating method, including:
acquiring lane data from the high-precision road network data, wherein the lane data comprises lane line data of a lane;
determining a center line of the lane based on the data of the lane lines;
navigation road network data is generated based on the center line of the lane.
A second aspect of an embodiment of the present disclosure provides a navigation road network data generating apparatus, including:
the acquisition module is used for acquiring lane data from the high-precision road network data, wherein the lane data comprises lane line data of the lanes;
the determining module is used for determining the center line of the lane based on the data of the lane line;
and the generation module is used for generating navigation road network data based on the central line of the lane.
A third aspect of the disclosed embodiments provides a computer device comprising a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, implements a method as in the first aspect described above.
A fourth aspect of the disclosed embodiments provides a computer readable storage medium having a computer program stored therein, which when executed by a processor, implements a method as in the first aspect described above.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data, and the data of the lane line of the lane is included in the data of the lane; determining a center line of the lane based on the data of the lane lines; the navigation road network data is generated based on the center line of the lane, the data of the lane can be directly obtained from the high-precision road network data, then the center line of the lane is generated based on the data of the lane, and further the navigation road network data is generated, mapping of the navigation road network data is not needed, mapping resources are saved, and the navigation road network data can be quickly and accurately generated.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a flowchart of a navigation road network data generating method provided in an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of a lane provided by an embodiment of the present disclosure;
FIG. 3 is a flowchart of another navigation road network data generation method provided by an embodiment of the present disclosure;
FIG. 4 is a flow chart of yet another method for generating navigation road network data provided by an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a navigation road network data generating device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
In the related art, navigation road network data is mainly generated by combining satellite mapping with manual flow labeling, and has low efficiency and low accuracy.
Aiming at the defects of the related technology in generating navigation road network data, the embodiment of the disclosure provides a navigation road network data generation method, device, equipment and storage medium, which can directly acquire data of a lane from high-precision road network data, then generate a center line of the lane based on the data of the lane, and further generate the navigation road network data, so that mapping of the navigation road network data is not needed, mapping resources are saved, and the navigation road network data can be quickly and accurately generated.
The navigation path network data generating method provided by the embodiment of the disclosure may be executed by a computer device, where the computer device may be an electronic device, a vehicle-mounted device or a server. Electronic devices include, but are not limited to, smart phones, palm top computers, tablet computers, wearable devices with display screens, desktop computers, notebook computers, all-in-one computers, smart home devices, and the like. The server can be an independent server or a cluster of a plurality of servers, and can comprise a local server and a server erected at a cloud.
In order to better understand the inventive concepts of the embodiments of the present disclosure, the technical solutions of the embodiments of the present disclosure are described below in conjunction with exemplary embodiments.
Fig. 1 is a flowchart of a method for generating navigation road network data according to an embodiment of the present disclosure, where, as shown in fig. 1, the method for generating navigation road network data according to an embodiment of the present disclosure includes steps 110 to 130:
and 110, acquiring lane data from the high-precision road network data, wherein the lane data comprises lane line data of the lane.
The high-precision road network data in the embodiments of the present disclosure may be understood as data constituting a high-precision Map (HD Map), which is an electronic Map having higher precision and more data dimensions than a conventional navigation Map, and may provide road information accurate to coordinate precision of sub-meter level, and surrounding static information related to traffic in addition to the road information. The high-precision road network data comprises road data such as lane information including positions, types, widths, gradients, curvatures and the like of lane lines, fixed object information such as traffic signs, traffic lights and the like around the lanes, lane height limits, water-level crossings, barriers and other road details, and infrastructure information such as overhead objects, protective barriers, numbers, road edge types, roadside landmarks and the like. The high-precision road network data can be constructed through a perception technology and stored in a corresponding high-precision road network data server, and the computer equipment can acquire the high-precision road network data of a corresponding area from the high-precision road network data server according to requirements.
In the embodiment of the disclosure, the computer device may acquire data of a lane from the high-precision road network data, and the data of the lane may include data of a lane line of the lane. The lane lines may include solid lines or broken lines between lanes and lane edges, and are composed of a plurality of points. Fig. 2 shows a schematic view of a lane, as shown in fig. 2, the lane 200 comprises a lane line 201, a lane line 202, a lane line 203 and a lane line 204, wherein the lane line 201 and the lane line 202 are solid lines, the lane line 203 and the lane line 204 are broken lines, and the lane line divides the lane into three sub-lanes, namely a sub-lane 1, a sub-lane 2 and a sub-lane 3.
The data of the lane line in the embodiment of the present disclosure may include data of points on the lane line, the data of the points on the lane line may include position data of all points constituting the lane line, the position data of the points may include coordinate information of the points and directions of the points, the coordinate information of the lane group to which the data of the lane line belongs may be determined based on the data of the lane line, the coordinate information in the embodiment of the present disclosure may be understood as longitude and latitude information, and the data of all points on the lane line may represent the length, width and curvature of the lane line.
And 120, determining the center line of the lane based on the data of the lane lines.
In the presently disclosed embodiments, the center line of the lane may be understood as a center line parallel to the lane direction, which is determined by the outermost boundary of the lane.
In some embodiments, determining the centerline of the lane based on the lane line data may include steps 121-122:
step 121, determining the boundary of the lane based on the position data of the points on the lane line of the lane.
In the embodiment of the disclosure, the data of the lane line may include position data of a point on the lane line.
In some embodiments, after acquiring data of the lane line of the lane, the computer device may determine the boundary of the lane based on the position data of the point on the lane line. Specifically, the computer device may determine the boundary of the lane according to the position data of some points on the outermost periphery of the lane line of the lane.
In other embodiments, the computer device may determine the boundary of the lane based on the location data of points on the lane line of the lane based on a scatter profile algorithm. The scattered point contour algorithm is a simple and effective algorithm for rapidly extracting the contour, can overcome the defect that the contour is inaccurate due to the fact that the contour is extracted by the point cloud contour extraction algorithm according to boundary characteristics, and can rapidly and accurately identify the contour.
Step 122, determining the center line of the lane based on the boundary of the lane.
In embodiments of the present disclosure, after determining the boundary of the lane, the computer device may determine a centerline of the lane based on the boundary of the lane.
In some embodiments, determining the centerline of the lane based on the boundaries of the lane may include steps 12201-12202:
step 12201, performing space division on the boundary of the lane along the direction perpendicular to the lane line to obtain a preset number of polygons.
In the embodiment of the disclosure, after determining the boundary of the lane, the computer device may spatially divide the boundary of the lane along the direction perpendicular to the lane line to obtain a preset number of polygons. For example, the boundary of the lane may be spatially segmented based on a binary spatial segmentation tree algorithm to obtain a preset number of polygons. Therefore, some complex lane boundaries can be divided into some simpler polygons, and subsequent processing is convenient.
Step 12202, connecting the centroids of each polygon in the preset number of polygons in turn, and generating a center line of the lane.
In the embodiment of the disclosure, after obtaining the preset number of polygons of the lane, the computer device may determine the centroid position of each polygon, where the centroid position may be understood as the intersection point position of multiple diagonal lines of the polygon, and then connect the centroids of each polygon in pairs in turn, so as to generate the center line of the lane.
And 130, generating navigation road network data based on the center line of the lane.
In the embodiment of the disclosure, after obtaining the center line of the lane, the computer device may determine the center line of the lane as navigation road network data corresponding to the lane, then may connect the center line of the lane with the center lines of other lanes in the route where the lane is located, obtain the center line of the route where the lane is located, and then connect the center line of the route, so as to generate navigation road network data corresponding to the route.
According to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data, and the data of the lane line of the lane is included in the data of the lane; determining a center line of the lane based on the data of the lane lines; the navigation road network data is generated based on the center line of the lane, the data of the lane can be directly obtained from the high-precision road network data, then the center line of the lane is generated based on the data of the lane, and further the navigation road network data is generated, mapping of the navigation road network data is not needed, mapping resources are saved, and the navigation road network data can be quickly and accurately generated.
Fig. 3 is a flowchart of another method for generating navigation road network data according to an embodiment of the present disclosure, where, as shown in fig. 3, the method for generating navigation road network data according to an embodiment of the present disclosure includes steps 310 to 350:
and 310, acquiring data of a lane from the high-precision road network data, wherein the data of the lane comprises data of lane lines of the lane.
The content of the embodiments of the present disclosure may refer to step 110, which is not described herein.
And 320, dividing the data of the lanes to obtain data of at least one lane group of the lanes.
In the embodiment of the disclosure, after the computer device acquires the data of the lanes, the data of the lanes may be segmented, for example, the lanes may be segmented along a direction perpendicular to the lane lines, so as to obtain at least one lane group of the lanes, and then the data of the lane group is acquired from the data of the lanes. For example, as shown in fig. 2, the dividing line may divide the lane 200 into 4 lane groups, such as lane group a, lane group B, lane group C, and lane group D, where the lane group a includes a lane line 201, a lane line 202, a lane line 203, and a lane line 204, the lane line 201 and the lane line 202 are solid lines, and the lane line 203 and the lane line 204 are broken lines.
The data of the lane group in the embodiment of the disclosure may include data of a lane line in the lane group and a direction of a lane to which the data of the lane line belongs, the data of the lane line may include data of points on the lane line, the data of the points on the lane line may include position data of all points constituting the lane line, the position data of the points may include coordinate information of the points and a direction of the points, the coordinate information of the lane group to which the data of the lane line belongs may be determined based on the data of the lane line, the coordinate information in the embodiment of the disclosure may be understood as longitude and latitude information, and the data of all the points on the lane line may represent a length, a width and a curvature of the lane line.
And 330, determining the center line of the lane group based on the data of the lane lines in the data of the lane group.
In the disclosed embodiments, the center line of the lane group may be understood as a center line parallel to the lane direction, which is determined by the outermost boundary of the lane group. For example, as shown in fig. 2, A1 is a center line defined by the lane group a.
In the embodiments of the present disclosure, after obtaining the data of at least one lane group of the lanes, the computer apparatus may determine a center line of at least one lane group of the lanes based on the data of the lane lines in the data of the lane group.
In some embodiments, determining the center line of the lane group based on the data of the lane lines in the data of the lane group may include steps 331-332:
step 331, determining the boundary of the lane group based on the position data of the points on the lane lines in the lane group.
In an embodiment of the present disclosure, the data of the lane lines may include position data of points on the lane lines, and the computer device may determine the boundary of the lane group based on the position data of the points on the lane lines in the lane group after obtaining the data of at least one lane group included in the acquired lane. Specifically, the computer device may determine the boundary of the lane group according to the position data of some points on the outermost periphery on the lane line in the lane group data.
In some embodiments, the computer device may determine the boundary of the lane group based on the location data of points on the lane lines in the lane group based on a scatter profile algorithm. The scattered point contour algorithm is a simple and effective algorithm for rapidly extracting the contour, can overcome the defect that the contour is inaccurate due to the fact that the contour is extracted by the point cloud contour extraction algorithm according to boundary characteristics, and can rapidly and accurately identify the contour.
In other embodiments, determining the boundary of the lane group based on the data of the points on the lane lines in the lane group may include steps 33101-33104:
step 33101, for any two points on the lane line, if the distance between the two points is smaller than the preset threshold, determining two target points from the perpendicular bisector of the line between the two points, where the distance between each target point and any one of the two points is half of the preset threshold.
Specifically, for any two points on the lane line, if the distance between the two points is greater than or equal to a preset threshold, the points are excluded. Thus, some outliers, which are some points in the lane group data that do not represent lane lines, such as lines of roadside parking spaces, some mesh lines, etc., may be eliminated, and the data processing amount may be reduced. If the distance between the two points is smaller than the preset threshold value, two target points are determined from the perpendicular bisector of the connecting line between the two points, and the distance between each target point and any one of the two points is one half of the preset threshold value. The fineness degree of the boundary is determined by the preset threshold value, and the smaller the preset threshold value is, the finer the boundary is, and the more accurate the obtained boundary is.
Step 33102, determining two circles with the two target points as circle centers and the half of the preset threshold as radius.
In this embodiment of the present disclosure, after determining two target points, the computer device may determine two circles with the two target points as circle centers and with a half of a preset threshold as a radius, where the determined two circles are symmetrical about a line connecting the two target points, and are also symmetrical about a perpendicular bisector of the line connecting the two target points.
In step 33103, if any one of the two circles does not include any other point than the two points, the data of the two points is determined to be boundary point data.
In the embodiment of the disclosure, if no other points than the two points are included in any one of the two circles, it is explained that the two points are already the points at the outermost periphery of the lane group, that is, the position data of the two points are determined as the boundary point position data.
And 33104, determining the boundary of the lane group according to the boundary point position data in all points of the lane line.
In the embodiment of the disclosure, after determining that the position data of two points at the outermost periphery of the lane group is boundary point position data, the computer device determines boundary point position data in all points of the lane line, and determines the boundary of the lane group based on the boundary point position data in all points. For example, the boundary points may be connected by smooth line segments to obtain the boundary of the lane group.
Step 332, determining the center line of the lane group based on the boundary of the lane group.
In the embodiment of the disclosure, after determining the boundary of the lane group, the computer apparatus may determine the center line of the boundary of the lane group as the center line of the lane group.
In some embodiments, determining the center line of the lane group based on the boundaries of the lane group may include steps 33201-33202:
and 33201, performing space division on the boundary of the lane group along the direction perpendicular to the lane lines to obtain a preset number of polygons.
In the embodiment of the disclosure, after determining the boundary of the lane group, the computer device may spatially divide the boundary of the lane group along the direction perpendicular to the lane line to obtain a preset number of polygons. For example, the boundary of the lane group may be spatially segmented based on a binary spatial segmentation tree algorithm to obtain a preset number of polygons. Therefore, some complex lane group boundaries can be divided into some simpler polygons, and subsequent processing is convenient.
Step 33202, connecting the centroids of each polygon in the preset number of polygons in sequence to generate the center line of the lane group.
In the embodiment of the disclosure, after obtaining the preset number of polygons of the lane group, the computer device may determine the centroid position of each polygon, where the centroid position may be understood as the intersection point position of multiple diagonal lines of the polygon, and then connect the centroids of each polygon in pairs in turn, so as to generate the center line of the lane group.
And 340, connecting the central lines of at least one lane group to generate the central lines of lanes.
In the embodiment of the disclosure, after obtaining the center line of at least one lane group, the computer device may connect the center lines of at least one lane group to generate the center line of the lane.
In some embodiments, the computer device may first determine the order of arrangement of the at least one lane group in the lanes, and then sequentially connect the centerlines of the at least one lane group in the order of arrangement. The arrangement order of the lane groups in the lanes can be determined when dividing the lane groups.
In other embodiments, the computer device may obtain the lane direction of the lane and the coordinate information of the lane group from the data of the lane group, then determine the arrangement sequence of the lane groups based on the lane direction and the coordinate information of the lane group, and sequentially connect the center lines of at least one lane group according to the arrangement sequence to generate the center line of the lane.
And 350, generating navigation road network data based on the central line of the lane.
The content of the embodiments of the present disclosure may refer to the above step 130, and will not be described herein.
According to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data, and the data of the lane line of the lane is included in the data of the lane; dividing the data of the lanes to obtain data of at least one lane group of the lanes; determining a center line of the lane group based on data of lane lines in the data of the lane group; connecting the central lines of at least one lane group to generate the central lines of lanes; the navigation road network data is generated based on the center line of the lane, the data of the lane can be directly obtained from the high-precision road network data, then the data of the lane is divided into the data of at least one lane group, the center line of the lane is generated based on the data of the lane group, and then the navigation road network data is generated, mapping of the navigation road network data is not needed, mapping resources are saved, and the navigation road network data can be quickly and accurately generated.
Fig. 4 is a flowchart of another method for generating navigation road network data according to an embodiment of the present disclosure, as shown in fig. 4, where the method for generating navigation road network data according to an embodiment of the present disclosure includes steps 410 to 450:
step 410, obtaining data of a lane from the high-precision road network data, wherein the data of the lane comprises data of lane lines of the lane.
The content of the embodiments of the present disclosure may refer to step 110, which is not described herein.
And step 420, dividing the data of the lanes to obtain data of at least one lane group of the lanes.
The content of the embodiments of the present disclosure may refer to step 320, which is not described herein.
And 430, determining the center line of the lane group based on the data of the lane lines in the data of the lane group.
The content of the embodiments of the present disclosure may refer to step 330, which is not described herein.
Step 440, matching at least one vehicle driving track data of the lane with the data of the lane group to obtain the lane group matched with any vehicle driving track data.
In the embodiment of the disclosure, after the vehicle travels in the lane to which the lane group belongs, the server may acquire a travel track of each vehicle traveling in the lane, and store travel track data included in the travel track. The driving track may be composed of a plurality of track points, the driving track data may include data of a plurality of track points composing the driving track, information of a lane through which the vehicle is driven, coordinate information of the vehicle when the vehicle is driven, and the like, and the data of the track points includes coordinate information of points and directions of the points, and it may be understood that the driving track of the vehicle may pass through the entire lane when the number of the driving tracks of the vehicle of the lane is sufficiently large.
In the embodiment of the disclosure, after obtaining the center line of at least one lane group of the lanes, the computer device may obtain at least one vehicle driving track data of the lanes from the server, match the vehicle driving track data with the data of the lane groups, and obtain each lane group matched with any vehicle driving track data, i.e. obtain which lane groups in the lanes the any vehicle driving track passes through.
In some embodiments, the computer device may determine whether the vehicle driving trajectory data and the data of the lane group match according to whether the coordinate information of the vehicle included in the vehicle driving trajectory data and the coordinate information of the lane group included in the data of the lane group are the same or whether the coordinate difference is within a preset range.
If the coordinate information of the vehicle included in the vehicle driving track data is the same as the coordinate information of the lane group included in the data of the lane group or the coordinate difference value is within a preset range, the computer equipment can consider that the vehicle driving track is matched with the lane group; otherwise, the vehicle travel track may be considered to be mismatched with the lane group. Thus, the computer device can obtain the lane group with the matched vehicle driving track.
And 450, connecting the central lines of the matched lane groups to obtain the central lines of the lanes.
In the embodiment of the disclosure, after obtaining the plurality of lane groups matched with the vehicle driving track data, the computer device may determine a connection sequence between the plurality of lane groups according to the vehicle driving track data, where the connection sequence may be understood as a precursor successor relationship between the plurality of lane groups, that is, a head-tail connection sequence between any two matched lane groups, and connect centerlines of the matched lane groups according to the connection sequence between the plurality of matched lane groups, so as to obtain centerlines of lanes to which the lane groups belong.
In some embodiments, in the process of connecting the center lines of the matched lane groups, the center lines of any two adjacent lane groups may not be connected, so that the center lines of the matched lane groups are connected to obtain the center lines of the lanes, and the method may further include steps 451-454:
step 451, the center line of the first lane group and the center line of the second lane group, which are adjacent but not connected, among the center lines of the matched lane groups are acquired.
In the embodiment of the disclosure, the computer device may acquire the adjacent but unconnected first lane group center line and second lane group center line from the center lines of the lane groups matched with any one of the vehicle driving track data.
In some embodiments, the computer device may combine the centerlines of adjacent lane groups two by two into a pair. As shown in fig. 2, the center line of the lane group a is A1, the center line of the lane group B is B1, the center line of the lane group C is C1, and the center line of the lane group D is D1. After the central line of each lane group of the lanes is obtained, the computer equipment combines A1B1 into a pair by two, B1C1 into a pair by two, and C1D1 into a pair by two.
After the central lines of two adjacent lane groups are combined into a pair in pairs, the computer equipment can further judge whether the pair of the two lane groups with the central lines in pairs is continuous or not, and acquire the central lines of the discontinuous two adjacent lane groups as the central lines of the first lane group and the central lines of the second lane group. For example, the centerlines C1 and D1 of the lane groups in fig. 2 are the centerlines of the discontinuous adjacent two lane groups.
In other embodiments, the computer device obtains the center line of the first lane group and the center line of the second lane group that are adjacent but not connected from the center lines of the matched lane groups, and may further include steps 45101-45102:
and 45101, obtaining the traffic quantity of the central lines of two adjacent lane groups in the central lines of the lane groups based on the data of the lane groups and the vehicle driving track data.
The number of passes in the embodiments of the present disclosure may be understood as the number of vehicle travel tracks.
In the embodiment of the disclosure, the computer device may acquire a large amount of vehicle driving track data of the lanes according to the need, and match the acquired vehicle driving track data with the data of the lane groups to obtain the number of vehicle driving tracks matched with the data of each lane group, so as to determine the traffic number of the center line of each lane group.
Step 45102, determining the center line of the first lane group and the center line of the second lane group based on the traffic number of the center lines of the adjacent two lane groups among the center lines of the lane groups.
In the embodiment of the disclosure, after obtaining the traffic numbers of the centerlines of the adjacent two lane groups among the centerlines of the lane groups, the computer device may determine the centerlines of the first lane group and the second lane group based on the traffic numbers of the centerlines of the adjacent two lane groups among the centerlines of the lane groups.
In some embodiments, the center line of the front lane group having the number of passes greater than the first threshold may be screened as the center line of the first lane group based on the number of passes of the center line of the front lane group among the center lines of the adjacent two lane groups; and screening the center line of the rear lane group with the ratio larger than a second threshold value as the center line of the second lane group based on the ratio of the traffic number of the center line of the rear lane group to the traffic number of the center line of the front lane group in the center lines of the two adjacent lane groups.
Step 452, determining a fusion area based on the center line of the first lane set and the center line of the second lane set.
In the embodiments of the present disclosure, a fusion area may be understood as an area where there may be a connection relationship between the center line of the first lane group and the center line of the second lane group. After obtaining the center line of the first lane group and the center line of the second lane group, the computer device determines a blend area of the center line of the first lane group and the center line of the second lane group.
In some embodiments, the computer device may determine the blend area based on an ending point of the centerline of the first lane group and a starting point of the centerline of the second lane group. Specifically, a center point between the end point of the center line of the first lane group and the start point of the center line of the second lane group, and a distance between the end point of the center line of the first lane group and the start point of the center line of the second lane group can be obtained according to the end point of the center line of the first lane group and the start point of the center line of the second lane group, and a circle with the center point as the center and the distance as the diameter is used as a fusion area.
Step 453, a fusion point is determined from the fusion region.
In the disclosed embodiments, a fusion point may be understood as a point that may connect a centerline of a first lane group and a centerline of a second lane group. After determining the blend area of the center line of the first lane group and the center line of the second lane group, the computer device may determine a blend point of the center line of the first lane group and the center line of the second lane group from the blend area.
Step 454, connecting the center line of the first lane group with the center line of the second lane group based on the merging point.
In an embodiment of the disclosure, after determining the merging point of the center line of the first vehicle lane group and the center line of the second vehicle lane group, the computer device may connect the merging point with the center line of the first vehicle lane group and the center line of the second vehicle lane group, respectively, and further connect the center line of the first vehicle lane group and the center line of the second vehicle lane group.
In some embodiments, the computer device may obtain an ending point of the centerline of the first lane group and a starting point of the centerline of the second lane group after determining the merging point, and connect the ending point of the centerline of the first lane group and the starting point of the centerline of the second lane group.
Step 460, generating navigation road network data based on the center line of the lane.
The content of the embodiments of the present disclosure may refer to the above step 130, and will not be described herein.
According to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data, and the data of the lane line of the lane is included in the data of the lane; dividing the data of the lanes to obtain data of at least one lane group of the lanes; determining a center line of the lane group based on data of lane lines in the data of the lane group; matching at least one vehicle driving track data of the lane with data of a lane group to obtain a lane group matched with any vehicle driving track data; connecting the center lines of the matched lane groups to obtain the center lines of the lanes; the navigation road network data is generated based on the center line of the lane, and the center line of the lane group in the lane can be connected based on the vehicle running track data of the lane, so that the generated navigation road network data is more accurate.
Fig. 5 is a schematic structural diagram of a navigation road network data generating apparatus according to an embodiment of the present disclosure, where the apparatus may be understood as part of functional modules of the above-mentioned computer device. As shown in fig. 5, the navigation road network data generating apparatus 500 includes:
the obtaining module 510 is configured to obtain data of a lane from the high-precision road network data, where the data of the lane includes data of a lane line of the lane;
a determining module 520 for determining a center line of the lane based on the data of the lane line;
a generating module 530 for generating navigation road network data based on the center line of the lane.
According to the embodiment of the disclosure, the data of the lane is obtained from the high-precision road network data through the obtaining module, and the data of the lane comprises the data of the lane line of the lane; the determining module determines the center line of the lane based on the data of the lane line; the generation module generates navigation road network data based on the center line of the lane, can directly acquire the data of the lane from the high-precision road network data, then generates the center line of the lane based on the data of the lane, further generates the navigation road network data, does not need to map the navigation road network data, saves mapping resources, and can quickly and accurately generate the navigation road network data.
Optionally, the determining module 520 includes:
the segmentation submodule is used for segmenting the data of the lanes to obtain data of at least one lane group of the lanes;
the determining submodule is used for determining the center line of the lane group based on the data of the lane lines in the data of the lane group;
the first generation sub-module is used for connecting the central lines of at least one lane group and generating the central lines of lanes.
Optionally, the determining submodule includes:
a first determination unit configured to determine a boundary of the lane group based on position data of points on lane lines in the lane group;
and a second determining unit for determining the center line of the lane group based on the boundary of the lane group.
Optionally, the first determining unit includes:
the dividing subunit is used for carrying out space division on the boundary of the lane group along the direction perpendicular to the lane lines to obtain a preset number of polygons;
and the generation subunit is used for sequentially connecting the centroids of each polygon in the preset number of polygons to generate the center line of the lane group.
Optionally, the first generating sub-module includes:
a third determining unit configured to determine an arrangement order of at least one lane group in lanes;
the first connecting unit is used for sequentially connecting the central lines of at least one lane group according to the arrangement sequence.
Optionally, the first generating sub-module includes:
and the second connecting unit is used for sequentially connecting the central lines of at least one lane group according to the lane direction.
Optionally, the first generating sub-module includes:
the matching unit is used for matching at least one vehicle running track data of the lane with the data of the lane group to obtain the lane group matched with any vehicle running track data;
and the third connecting unit is used for connecting the center lines of the matched lane groups to obtain the center lines of the lanes.
Optionally, the third connection unit includes:
an acquisition subunit, configured to acquire a center line of a first lane group and a center line of a second lane group that are adjacent but not connected from center lines of the matched lane groups;
a first determination subunit configured to determine a fusion area based on a center line of the first lane group and a center line of the second lane group;
a second determining subunit configured to determine a fusion point from the fusion region;
and the connecting subunit is used for connecting the first vehicle lane group center line and the second vehicle lane group center line based on the fusion point.
Optionally, the acquiring subunit includes:
the obtaining component is used for obtaining the passing quantity of the central lines of two adjacent lane groups in the central lines of the lane groups based on the data of the lane groups and the vehicle driving track data;
a determining component for determining a center line of the first lane group and a center line of the second lane group based on the number of passes of the center lines of the adjacent two lane groups.
Optionally, the generating module 530 includes:
the connecting sub-module is used for connecting the central line of the lane with the central lines of other lanes in the route where the lane is located to obtain the central line of the route;
and the second generation sub-module is used for generating navigation road network data based on the central line of the route.
The navigation road network data generating device provided in this embodiment can execute the method described in any one of the above embodiments, and the execution mode and the beneficial effects thereof are similar, and are not described herein again.
The embodiments of the present disclosure provide a computer readable storage medium, in which a computer program is stored, where when the computer program is executed by a processor, the computer program causes the processor to implement the above method, and the implementation manner and beneficial effects of the method are similar, and are not repeated herein.
The computer readable storage media described above can employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer programs described above may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The navigation road network data generation method is characterized by comprising the following steps of:
acquiring data of a lane from high-precision road network data, wherein the data of the lane comprises data of lane lines of the lane;
determining a center line of the lane based on the data of the lane lines;
and generating navigation road network data based on the center line of the lane.
2. The method of claim 1, wherein the determining the center line of the lane based on the data of the lane lines comprises:
dividing the data of the lanes to obtain data of at least one lane group of the lanes;
determining a center line of the lane group based on data of lane lines in the data of the lane group;
and connecting the central lines of the at least one lane group to generate the central line of the lane.
3. The method of claim 2, wherein the data of the lane lines includes position data of points on the lane lines, and wherein the determining the center line of the lane group based on the data of the lane lines in the data of the lane group includes:
determining a boundary of the lane group based on position data of points on lane lines in the lane group;
a centerline of the lane group is determined based on the boundaries of the lane group.
4. The method of claim 3, wherein the determining a center line of the lane group based on the boundary of the lane group comprises:
space-dividing the boundary of the lane group along the direction perpendicular to the lane lines to obtain a preset number of polygons;
and sequentially connecting the centroids of each polygon in the preset number of polygons to generate the center line of the lane group.
5. The method of claim 2, wherein the connecting the centerlines of the at least one lane group to generate the centerlines of the lanes comprises:
determining an arrangement order of the at least one lane group in the lanes;
and sequentially connecting the central lines of the at least one lane group according to the arrangement sequence to generate the central lines of the lanes.
6. The method of claim 2, wherein the connecting the centerlines of the at least one lane group to generate the centerlines of the lanes comprises:
matching at least one vehicle driving track data of the lanes with the data of the lane groups to obtain a lane group matched with any vehicle driving track data;
and connecting the central lines of the matched lane groups to obtain the central lines of the lanes.
7. The method of any of claims 1-6, wherein generating navigation road network data based on a centerline of the lane comprises:
connecting the center line of the lane with the center lines of other lanes in the route where the lane is located to obtain the center line of the route;
and generating navigation road network data based on the central line of the route.
8. A navigation road network data generating device, the device comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring data of a lane from high-precision road network data, and the data of a lane line of the lane are included in the data of the lane;
the determining module is used for determining the center line of the lane based on the data of the lane line;
and the generation module is used for generating navigation road network data based on the central line of the lane.
9. A computer device, comprising:
a memory and a processor, wherein the memory has stored therein a computer program which, when executed by the processor, implements the navigation road network data generating method according to any of claims 1-7.
10. A computer readable storage medium, characterized in that the storage medium has stored therein a computer program which, when executed by a processor, implements the navigation road network data generating method according to any of claims 1-7.
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