Road network data processing method and system, terminal and storage medium

Abstract

The invention discloses a road network data processing method and system, a terminal and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-processed road network data corresponding to a road network model, so as to extract and obtain the length and shape data of each road section, and determining the datum point density in unit length; if the density of the reference points in the unit length does not meet the density control condition, determining a control curve according to the coordinates of the two-dimensional reference points in the road section shape data, and inserting a target reference point in the road network model according to the coordinates of the two-dimensional reference points and the control curve to obtain a reference point sequence; acquiring a streetscape image matched with the road network model to determine that a target road section with a missing traffic channel exists in the road network model; and for the target road section, generating a supplementary reference point corresponding to the missing traffic channel according to the streetscape image and the reference point sequence, and complementing the missing traffic channel in the road network model according to the supplementary reference point. Therefore, the accuracy of the road network model can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a road network data processing method and system, a terminal and a storage medium. BACKGROUND

[0002] With the development of science and technology, the application of traffic simulation is more and more widely. In the process of realizing traffic simulation, a corresponding road network model needs to be constructed.

[0003] In the prior art, in the construction process of the simulation road network model, the data in the Extensible Markup Language (XML) format is obtained by converting the OpenStreetMap (OSM), and then the construction of the simulation road network model is directly based on the obtained XML data. The problem of the prior art is that in the process of obtaining the XML data based on the OSM conversion, the problem of the loss of the reference point is prone to occur, thereby causing the simulation road network model to have the problem of the sudden change of the road segment line shape, which is not conducive to improving the accuracy of the simulation road network model.

[0004] Therefore, the related technology needs to be improved and developed. SUMMARY

[0005] The main purpose of the present application is to provide a road network data processing method and system, a terminal and a storage medium, which aims to solve the technical problem that the simulation road network model constructed directly based on the XML data is not conducive to improving the accuracy of the model due to the loss of the reference point in the related technology.

[0006] In order to achieve the above purpose, the first aspect of the present application provides a road network data processing method, wherein the road network data processing method comprises: obtaining road network model corresponding to the to-be-processed road network data, extracting the road segment length and road segment shape data corresponding to each road segment from the to-be-processed road network data, wherein the road segment shape data comprises the coordinates of the two-dimensional reference point corresponding to the road segment; determining the reference point density in the unit length corresponding to each road segment according to the road segment length and the road segment shape data; if the reference point density in the unit length does not meet the preset density control condition, determining a control curve according to the coordinates of the two-dimensional reference point in the road segment shape data, inserting a target reference point in the road network model according to the coordinates of the two-dimensional reference point and the control curve, and obtaining a reference point sequence composed of the two-dimensional reference point and the target reference point; obtaining a street view image matched with the road network model; determining a target road segment with a missing traffic channel in the road network model according to the street view image; For the target road segment, according to the street view image and the sequence of reference points, a supplementary reference point corresponding to the missing traffic lane is generated, and the missing traffic lane is completed in the road network model according to the supplementary reference point.

[0007] Optionally, the determination of the reference point density per unit length corresponding to each road segment according to the road segment length and the road segment shape data comprises: determining the number of two-dimensional reference points corresponding to each road segment according to the road segment shape data; For each road segment, the reference point density per unit length corresponding to the road segment is determined according to the ratio of the number of two-dimensional reference points corresponding to the road segment to the road segment length.

[0008] Optionally, the control curve is a Bezier curve. If the reference point density per unit length does not meet the preset density control condition, a control curve is determined according to the coordinates of the two-dimensional reference points in the road segment shape data, a target reference point is inserted in the road network model according to the coordinates of the two-dimensional reference points and the control curve, and a sequence of reference points composed of the two-dimensional reference points and the target reference point is obtained, comprising: constructing a sequence of reference points according to the coordinates of the two-dimensional reference points in the road segment shape data; determining whether the reference point density per unit length is greater than a preset minimum density; If the reference point density per unit length is not greater than the minimum density, two candidate reference points adjacent to each other and having the maximum distance between adjacent points are determined from the sequence of reference points according to the coordinates of the reference points in the sequence of reference points. determining a Bezier curve corresponding to the candidate reference points according to the coordinates of the candidate reference points; inserting a target reference point between the candidate reference points according to the Bezier curve corresponding to the candidate reference points, and updating the sequence of reference points; updating the reference point density per unit length according to the number of reference points in the sequence of reference points, and returning to the step of determining whether the reference point density per unit length is greater than the preset minimum density until the reference point density per unit length is greater than the minimum density.

[0009] Optionally, the determination of the Bezier curve corresponding to the candidate reference points according to the coordinates of the candidate reference points comprises: If the candidate reference points include the first reference point or the last reference point in the sequence of reference points, a second-order Bezier curve corresponding to the candidate reference points is determined according to the coordinates of the candidate reference points. Otherwise, determine a third order Bezier curve corresponding to the candidate reference point according to the coordinates of the candidate reference point.

[0010] Optionally, the method further comprises: Obtaining a preset position parameter value; Determining a target reference point on the Bezier curve according to the position parameter value, and updating the sequence of reference points according to the coordinates of the target reference point.

[0011] Optionally, the missing traffic channel comprises a missing sidewalk. The method further comprises: Performing semantic segmentation on the street view image; Obtaining sidewalk information corresponding to the street view image according to the result of semantic segmentation; Determining whether a missing sidewalk exists in each road segment in the road network model according to the sidewalk information corresponding to the street view image and the traffic channel information in the road network model, and taking a road segment in which a missing sidewalk exists as a target road segment.

[0012] Optionally, the method further comprises: Determining the size of the missing sidewalk in the target road segment according to the sidewalk information corresponding to the street view image, wherein the sidewalk information comprises a sidewalk mask; Generating a supplementary reference point corresponding to the missing sidewalk according to the sequence of reference points and the size of the missing sidewalk, and completing the missing sidewalk in the road network model according to the supplementary reference point.

[0013] The second aspect of the present application provides a road network data processing system, wherein the road network data processing system comprises: A data acquisition module is configured to acquire road network data corresponding to a road network model, and extract road segment length data and road segment shape data corresponding to each road segment from the road network data, wherein the road segment shape data comprises coordinates of two-dimensional reference points corresponding to the road segment. A reference point density determination module is configured to determine the density of reference points in a unit length corresponding to each road segment according to the road segment length data and the road segment shape data. The benchmark point supplement module is configured to: if the benchmark point density in the unit length does not satisfy the preset density control condition, determine a control curve according to the coordinates of the two-dimensional benchmark points in the road section shape data, insert a target benchmark point in the road network model according to the coordinates of the two-dimensional benchmark points and the control curve, and obtain a benchmark point sequence composed of the two-dimensional benchmark points and the target benchmark point; The image acquisition module is configured to acquire a street view image matched with the road network model. The target road section determination module is configured to determine a target road section in which a missing traffic channel exists in the road network model according to the street view image. The traffic channel supplement module is configured to, for the target road section, generate a supplementary benchmark point corresponding to the missing traffic channel according to the street view image and the benchmark point sequence, and supplement the missing traffic channel in the road network model according to the supplementary benchmark point.

[0014] The third aspect of the present application provides a terminal, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program implements the steps of any one of the road network data processing methods when executed by the processor.

[0015] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of any one of the road network data processing methods when executed by a processor.

[0016] As can be seen, in the present application, the road network model corresponding to the to-be-processed road network data is acquired, the road section length and road section shape data corresponding to each road section are extracted from the to-be-processed road network data, wherein the road section shape data comprises the coordinates of two-dimensional benchmark points corresponding to the road section; the benchmark point density in the unit length corresponding to each road section is determined according to the road section length and the road section shape data; if the benchmark point density in the unit length does not satisfy the preset density control condition, a control curve is determined according to the coordinates of the two-dimensional benchmark points in the road section shape data, a target benchmark point is inserted in the road network model according to the coordinates of the two-dimensional benchmark points and the control curve, and a benchmark point sequence composed of the two-dimensional benchmark points and the target benchmark point is obtained; a street view image matched with the road network model is acquired; a target road section in which a missing traffic channel exists in the road network model is determined according to the street view image; for the target road section, a supplementary benchmark point corresponding to the missing traffic channel is generated according to the street view image and the benchmark point sequence, and the missing traffic channel is supplemented in the road network model according to the supplementary benchmark point.

[0017] Thus, for the road network model, when the density of the reference points in the corresponding unit length thereof does not satisfy the preset density control condition, the target reference point is inserted to realize the completion of the reference points of the road network model. Further, the target road segment with the missing traffic channel is determined according to the street view image, and the missing traffic channel in the road network model is completed. Thus, the completion of the reference points and the completion of the missing traffic channel are performed for the road network model, so as to solve the problem of low model accuracy caused by the loss of reference points, and the accuracy of the road network model is improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 is a flowchart of a road network data processing method provided by an embodiment of the present application; Figure 2 is a schematic diagram of a reference point supplement effect provided by an embodiment of the present application; Figure 3 is a schematic diagram of a missing sidewalk supplement effect provided by an embodiment of the present application; Figure 4 is a schematic diagram of the component modules of a road network data processing system provided by an embodiment of the present application; Figure 5 is an internal structure principle block diagram of a terminal provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced without these specific details. In other instances, well-known systems, devices, circuits, and methods have not been described in detail so as not to obscure the description of the present application.

[0021] It should be understood that when used in the specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It should also be understood that the terms used in the specification are for the purpose of describing specific embodiments and do not intend to limit the present application. As used in the specification and the appended claims, the singular forms "a," "an" and "the" are intended to include plural forms unless the context clearly dictates otherwise.

[0023] It should be further understood that the term "and / or" used in the specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0024] As used in the specification and the appended claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to a classification" depending on the context. Similarly, the phrases "if it is determined" or "if it is classified [that a described condition or event]" can be interpreted as meaning "once it is determined" or "in response to a determination" or "once it is classified [that a described condition or event]" or "in response to a classification [that a described condition or event]" depending on the context.

[0025] The technical solutions in the embodiments of the present application are described clearly and completely in the following description of the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0026] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0027] At present, the application of traffic simulation is more and more widely. Traffic simulation is a typical traffic data analysis method, so it is necessary to generate a lane-level traffic simulation model, to develop high-precision simulation road repair technology for the demand of large-scale simulation model geometry and topology optimization, to provide a high-trust, sustainable evolution of the basic road network base for the vehicle-road cooperative control platform.

[0028] The existing simulation road network model relies on XML format data converted based on OSM. In the conversion process, the reference points may be lost, resulting in road segment line mutation, which is difficult to meet the motion and visual requirements of micro-simulation. At the same time, affected by factors such as street view blind area, later expansion and artificial modeling omissions, the simulation road network often has the problem of missing sidewalks, which affects the accuracy of the simulation road network model. The traditional artificial inspection and street view and simulation data comparison method is time-consuming, high-cost and slow to update, and cannot meet the demand of large-scale road network high-frequency iteration. Therefore, a low-cost and high-precision road segment vector automatic repair scheme is urgently needed.

[0029] To solve at least one of the above technical problems, the present application provides a road network data processing method. In the present application, the road network model corresponding to the road network data to be processed is obtained, and the road segment length and road segment shape data corresponding to each road segment are extracted from the road network data to be processed. The road segment shape data includes the coordinates of the two-dimensional reference points corresponding to the road segment. According to the road segment length and the road segment shape data, the reference point density in the unit length of each road segment is determined. If the reference point density in the unit length does not meet the preset density control condition, the control curve is determined according to the coordinates of the two-dimensional reference points in the road segment shape data, the target reference points are inserted into the road network model according to the coordinates of the two-dimensional reference points and the control curve, and the reference point sequence composed of the two-dimensional reference points and the target reference points is obtained. The street view image matching the road network model is obtained. According to the street view image, the target road segment with missing traffic channel in the road network model is determined. For the target road segment, the supplementary reference points corresponding to the missing traffic channel are generated according to the street view image and the reference point sequence, and the missing traffic channel is completed in the road network model according to the supplementary reference points.

[0030] In this way, for the road network model, when the reference point density in the corresponding unit length does not meet the preset density control condition, target reference points are inserted to complete the reference points of the road network model. Further, the target road segment with missing traffic channel is also determined according to the street view image, and the missing traffic channel in the road network model is completed. In this way, the reference points and the missing traffic channel of the road network model are completed, so as to solve the problem of low model accuracy caused by reference point loss, and to improve the accuracy of the road network model.

[0031] As shown in Figure 1 The present application provides a road network data processing method. Specifically, the method includes the following steps: In step S100, the road network model corresponding to the to-be-processed road network data is obtained, and the road segment length and road segment shape data corresponding to each road segment are extracted from the to-be-processed road network data, wherein the road segment shape data includes the coordinates of the two-dimensional reference points corresponding to the road segment. In step S200, the unit length reference point density corresponding to each road segment is determined according to the road segment length and the road segment shape data. In step S300, if the unit length reference point density does not meet the preset density control condition, a control curve is determined according to the coordinates of the two-dimensional reference points in the road segment shape data, target reference points are inserted into the road network model according to the coordinates of the two-dimensional reference points and the control curve, and a reference point sequence composed of the two-dimensional reference points and the target reference points is obtained. In step S400, a street view image matching the road network model is obtained. In step S500, the target road segment with a missing traffic channel in the road network model is determined according to the street view image. In step S600, for the target road segment, the supplementary reference points corresponding to the missing traffic channel are generated according to the street view image and the reference point sequence, and the missing traffic channel is completed in the road network model according to the supplementary reference points.

[0032] The road network model is a simulation road network model generated in advance based on XML data. In the embodiment of the application, the road network model is a corresponding simulation road network model in the city traffic simulation software (SUMO, Simulation of Urban MObility).

[0033] Specifically, the XML format data of the road network model is obtained as the to-be-processed road network data, which includes four types of elements of intersections, road segments, lanes and connection lines. All road segments are extracted from the to-be-processed road network data by Python, and the road segment length and road segment shape data corresponding to each road segment are determined.

[0034] In some specific application scenarios, the attributes and values of the road segment identifier (id), road segment length (length) and road segment shape data (shape) corresponding to each road segment can be recorded. The road segment shape data includes the coordinates of all two-dimensional reference points describing the road segment, which is represented as wherein, represents the total number of two-dimensional reference points corresponding to the road segment, represents the coordinates of the first two-dimensional reference point corresponding to the road segment.

[0035] Specifically, the unit length reference point density corresponding to each road segment is determined according to the road segment length and the road segment shape data, which includes: According to the above road segment shape data, the number of two-dimensional reference points corresponding to each of the above road segments is determined; For each of the above road segments, the reference point density per unit length corresponding to the road segment is determined according to the ratio of the number of two-dimensional reference points corresponding to the road segment to the length of the road segment.

[0036] For a road segment, the ratio of the number of all two-dimensional reference points to the length of the road segment is calculated to obtain the reference point density per unit length, and if the requirement is not met, a new reference point is added.

[0037] Specifically, the length of the road segment is , and the road segment is composed of two-dimensional reference points, and the reference point density per unit length corresponding to the road segment is as shown in the following formula: ; Based on the above formula, the reference point density per unit length corresponding to each road segment can be calculated respectively to determine whether the reference point density per unit length meets the preset density control condition. If not, a new reference point needs to be added for the road segment. In some application scenarios, the target number of reference points can be obtained by multiplying the length of the road segment by the minimum density of the reference points.

[0038] It should be noted that the above control curve is a type of three-dimensional curve that is equivalent to linear basic elements such as straight lines, circular curves, clothoids, and parabolas in two-dimensional design. Specifically, the set three-dimensional curve has good continuity, so that the selection of a new reference point can be performed on the curve.

[0039] Specifically, the above curve meets the following preset requirements: the selected line shape ensures that the slope is continuous at any point within the range, i.e., the slope is continuous; the mathematical model of the selected curve has a unique and determined geometric shape in space; the selected line shape has geometric invariance and does not change in geometric shape with translation or rotation of the reference coordinate system, but only depends on the change in the relative position of each control point; the selected line shape can be adjusted and changed using control methods, and the degree of adjustment can be easily understood by designers through parameters to achieve the desired expected adjustment result; the mathematical model or equation expression of the selected line shape should reduce the difficulty and workload of calculation as much as possible, reduce the number of unknown parameters, avoid the appearance of high-order equations, and enable the computer to process data in a short time.

[0040] In some application scenarios, Hermite spline curve, Catmull-Rom spline curve, or T-spline curve can be selected as the control curve.

[0041] In the embodiments of the present application, the control curve is a Bezier curve; if the density of the reference points in the unit length does not satisfy the preset density control condition, the control curve is determined according to the coordinates of the two-dimensional reference points in the road segment shape data, the target reference point is inserted in the road network model according to the coordinates of the two-dimensional reference points and the control curve, and a reference point sequence composed of the two-dimensional reference points and the target reference point is obtained, including: constructing a reference point sequence according to the coordinates of the two-dimensional reference points in the road segment shape data; judging whether the density of the reference points in the unit length is greater than a preset minimum density; if the density of the reference points in the unit length is not greater than the minimum density, determining two candidate reference points adjacent to each other and having the maximum distance between adjacent points from the reference point sequence according to the coordinates of the reference points in the reference point sequence; determining the Bezier curve corresponding to the candidate reference points according to the coordinates of the candidate reference points; inserting a target reference point between the candidate reference points according to the Bezier curve corresponding to the candidate reference points, and updating the reference point sequence; updating the density of the reference points in the unit length according to the number of reference points in the reference point sequence, and returning to execute the step of judging whether the density of the reference points in the unit length is greater than the preset minimum density until the density of the reference points in the unit length is greater than the minimum density.

[0042] It should be noted that the number of reference points in the reference point sequence reaching the target number of reference points can also be used as a judgment condition for ending the loop, which is not limited here.

[0043] Specifically, the determination of the Bezier curve corresponding to the candidate reference points according to the coordinates of the candidate reference points includes: if the candidate reference points include the first reference point or the last reference point in the reference point sequence, a second-order Bezier curve corresponding to the candidate reference points is determined according to the coordinates of the candidate reference points; otherwise, a third-order Bezier curve corresponding to the candidate reference points is determined according to the coordinates of the candidate reference points.

[0044] Further, the insertion of a target reference point between the candidate reference points according to the Bezier curve corresponding to the candidate reference points and the updating of the reference point sequence include: obtaining a preset position parameter value; determining the target reference point on the Bezier curve according to the position parameter value, and updating the reference point sequence according to the coordinates of the target reference point.

[0045] In practical applications, in order to ensure the smoothness of the connection of the reference points, a Bezier curve is selected as the control curve. Considering that the osculating plane of the fitting curve of the road section should always change in the three-dimensional space, at least four control points are needed to form a control polygon with three edges to ensure that a spatial curve can be formed, so a Bezier curve of three or more orders can be selected.

[0046] The expression of the second-order Bezier curve is as follows: ; Among them, to indicate three sequentially arranged control points required to construct the second-order Bezier curve. indicates the position of a point on the curve, and the value range is [0, 1].

[0047] The expression of the third-order Bezier curve is as follows: ; Among them, to indicate four sequentially arranged control points required to construct the third-order Bezier curve. indicates the position of a point on the curve, and the value range is [0, 1].

[0048] Each order of the Bezier curve function can be described as follows: ; Among them, indicates the control point required to construct the Bezier curve, indicates the polynomial coefficient corresponding to the control point.

[0049] The coefficients of the control points in each order of the Bezier curve function are , The calculation is as follows: ; Among them, indicates the order of the Bezier curve. indicates the control point index, which is an integer from 0 to .

[0050] When a third-order Bezier curve is used, four control points are needed. For the two reference points given in the reference point sequence and ( ), they are respectively taken as the starting control point and the ending control point of the Bezier curve, where represents the number of reference points in the reference point sequence, because the road segment matches the corresponding reference point sequence, and also represents the number of reference points corresponding to the road segment, and both have the same meaning.

[0051] For the intermediate control point , a auxiliary straight line AB is drawn through the point parallel to the vector , and the position of the control point is determined according to a preset ratio in the area between the point and the point on the straight line AB. The preset ratio can be pre-set according to actual needs, or can be determined based on the ratio of the head-to-tail length of the road segment to the length of the road segment. The head-to-tail length of the road segment is specifically the straight line distance between the first reference point and the last reference point in the reference point sequence, and the length of the road segment is specifically the sum of the straight line distances between two consecutive reference points in the reference point sequence. The control point satisfies the following formula: ; wherein is the preset ratio, specifically a customizable parameter between 0 and 1, and the selection of the value of the parameter affects the shape and smoothness of the Bezier curve, and the specific value of the parameter can be set and adjusted according to actual needs.

[0052] For the control point , a auxiliary straight line CD is drawn through the point parallel to the vector , and the position of the control point is determined according to the preset ratio in the area between the point and the point on the straight line CD. The control point satisfies the following formula: ; In the embodiments of the present application, the ratio of the head-to-tail length of the road segment to the length of the road segment is used as the basis for determining the parameter , as shown in the following formula: ; ; ; wherein and represent the horizontal coordinate and the vertical coordinate of the first reference point in the reference point sequence, respectively, and These represent the first and second points in the reference point sequence, respectively. The x and y coordinates of each reference point are used, and other parameters are similarly calculated, which will not be elaborated here.

[0053] After determining the location of the control points, the corresponding control curves can be determined based on the location of the control points, and then the target reference points can be generated based on the control curves.

[0054] Specifically, calculate and find the two candidate reference points in the current reference point sequence that are adjacent and have the largest distance between adjacent points. Based on the Bézier curve parametric equations corresponding to these two candidate reference points, determine the position parameter value to be 0.5 (the specific value can be set and adjusted according to actual needs). Use the coordinates of the point corresponding to this value as the coordinates of the new reference point (i.e., the target reference point) and insert this reference point into the existing reference point sequence.

[0055] Repeat the above process of generating target reference points until a sufficient number of reference points are obtained.

[0056] In this embodiment of the application, the aforementioned missing traffic lanes include missing sidewalks; the aforementioned determination of the target road segments with missing traffic lanes in the aforementioned road network model based on the aforementioned street view images includes: Semantic segmentation is performed on the above street view images; The sidewalk information corresponding to the above street view image is obtained based on the semantic segmentation results; Based on the pedestrian walkway information corresponding to the street view images and the traffic channel information in the road network model, it is determined whether there are missing pedestrian walkways in each road segment of the road network model, and the road segments with missing pedestrian walkways are selected as target road segments.

[0057] Specifically, for the aforementioned target road segment, based on the aforementioned street view image and the aforementioned reference point sequence, supplementary reference points corresponding to the aforementioned missing traffic channels are generated. Based on these supplementary reference points, the missing traffic channels are then completed in the aforementioned road network model, including: Based on the sidewalk information corresponding to the street view image, the size of the missing sidewalk in the target road segment is determined, wherein the sidewalk information includes the sidewalk mask; Based on the above-mentioned reference point sequence and the dimensions of the missing sidewalks, supplementary reference points corresponding to the missing sidewalks are generated, and the missing sidewalks are completed in the above-mentioned road network model based on the supplementary reference points.

[0058] The aforementioned street view images can be obtained in batches using web crawling technology, or through other methods, without specific limitations here.

[0059] In some application scenarios, all street view images are geographically registered, and the street view images are geographically registered to realize registration of the street view images and a road network model. Pixel-level semantic segmentation is performed on the street view images to locate a region of interest (ROI) corresponding to a road region, and a region mask and a local feature are output, which provide input samples for subsequent fine-grained recognition. On the basis of segmentation, a fine-grained classifier is trained to recognize a sidewalk category of the ROI. Specifically, a road image dataset related to a target region is constructed, unified preprocessing and data enhancement are performed, and training / validation / testing sets are proportionally divided to evaluate Top-1 accuracy and robustness. The fine-grained classifier is based on a transfer learning strategy, uses a deep backbone network pre-trained on a large-scale dataset as a basis, combines an attention mechanism, and is fine-tuned to the local road image dataset to enhance sensitivity to texture and morphological features, improve cross-domain feature alignment, and improve recognition accuracy under small samples. The classifier outputs a semantic label, a local confidence score, and a corresponding local visual feature vector of each recognized object. The sidewalk information in the simulation model is compared, and it is determined whether the sidewalk of the road section is missing. For the road section with missing sidewalks, new sidewalk elements are added, and the street view image and the simulation model lane are geometrically registered to determine the size of the sidewalk. Finally, according to the road section reference point and the size of the sidewalk, the sidewalk reference point is calculated along the normal direction of the sub-road section where the road section reference point is located, and the sidewalk reference point is converted into a geometric shape expression conforming to the simulation road network data specification.

[0060] Specifically, in the embodiments of the present application, the corresponding street view image is found according to the longitude and latitude of each reference point of the road section, as the street view RGB image to be processed. For the street view RGB image to be processed, a preset preprocessing operation is performed for preprocessing. Specifically, center / equal scaling is first performed according to the aspect ratio, and then normalized to the ImageNet mean and standard deviation; the image for pixel-level semantic segmentation is uniformly adjusted to 1024x2048 to balance segmentation accuracy and computational overhead; and the ROI cropped image for fine-grained classification is uniformly scaled to 224x224 pixels in subsequent steps to adapt to the input requirements of the classifier. The preprocessing records the geographical reference of the original image to facilitate subsequent coordinate mapping.

[0061] On the basis of analyzing the construction experience of public datasets such as ImageNet, a high-quality road image dataset of the region where the road network model is located and the surrounding area is supplemented. The road dataset contains a total of 5641 images, and the number of images of each type of road is between 120 and 250. The enhanced images are divided into training set and test set according to the ratio of 4:1. The images in the training set are used for full training and iterative optimization of the network, and the test set is used to evaluate the Top-1 accuracy of the final model on the 20-class local road recognition task. In this way, the dataset is constructed based on the above steps. Further, based on the above training set and test set, the training and testing of the semantic segmentation model can be realized until the semantic segmentation model is trained.

[0062] In the embodiment of the application, the semantic segmentation model is a model based on the DeepLabV3+ native architecture. The pixel-level semantic segmentation is performed for key elements in the street view picture, and feature fusion is performed for the semantic segmentation process of the street view elements in the complex scene. The ROI of each type of element is accurately defined to provide input for the downstream recognition and detection task. The Backbone selects the ResNet-101 pre-trained on ImageNet. Specifically, ResNet-101 is selected as the backbone network, and ResNet-101 specifically refers to a residual network (Residual Network) with 101 layers. ResNet-101 effectively solves the problems of gradient disappearance and gradient explosion in the training of deep neural networks by introducing a residual learning mechanism and a skip connection, so that the network can be more effectively trained to a deeper layer, thereby learning more complex feature representations. In addition, the pre-trained model of ResNet-101 on a large-scale dataset can be migrated to a specific task, accelerating the training process and improving the performance on a small dataset.

[0063] The network corresponding to the semantic segmentation model first performs a series of downsampling and dilated convolution operations on the input RGB street view image to obtain rich high-level semantic features. Then, five parallel features (dilation rates = 1, 6, 2, 18, 24) are generated through the Atrous Spatial Pyramid Pooling (ASPP) module, and then reduced and fused through 1x1 convolution. After being fused with shallow features in the decoder (Decoder), it is upsampled to the original image size through bilinear interpolation, and the probability distribution of each pixel of each class is output.

[0064] The loss function is pixel-level cross-entropy (Cross-Entropy), and the learning rate decay strategy is adopted. The optimizer adopts SGD+Momentum (momentum = 0.9, weight_decay = 1e-4).

[0065] When training and testing the semantic segmentation model, the DeepLabV3+ pre-training weight is loaded, the single image full resolution forward inference is performed, the classification pixel probability map is obtained, and post-processing such as DenseCRF and connected domain filtering is performed to refine the edge, eliminate noise, and improve the coherence of the segmentation boundary. The segmentation effect is monitored by mIoU and pixel accuracy (PA). In this way, the post-processing and evaluation of the semantic segmentation model are realized.

[0066] After completing the DeepLabV3+ based semantic segmentation and locating the ROI of the road elements, these regions are segmented, and the semantic labels, region masks, and local features are output to provide input samples for subsequent fine-grained recognition.

[0067] Further, a lightweight lane category detection model is trained to output the attribute labels and spatial positions corresponding to each element. The identification base network of the model is determined, different architectures of deep convolutional neural networks DenseNet, Inception, ResNeXt, and MobileNet are selected, the full network is trained on the image training dataset, and the trained model is used to infer the test dataset. The above model effects are evaluated by the recognition accuracy Top1-ACC, and the pre-trained model is fine-tuned and predicted using the self-built dataset to compare the recognition effects of the network model migrated on ImageNet. The full network is trained on the road training set, and after iteration, the trained lane category detection model is obtained. The lane category detection model is used to infer the test set, and the ResNet50 with the highest Top1-ACC accuracy is obtained as the backbone network of lane recognition. In this way, the training of the fine-grained classifier is realized. Further, in the use process, the sidewalk information in the street view image can be recognized based on the trained model.

[0068] Compared with the ImageNet million image dataset, the training effect of the network parameters of the local dataset may be limited by the size of the sample amount, so the transfer learning fine-tuning is performed.

[0069] The structure and parameters of the pre-trained network model on the large dataset are migrated to the local, and the local dataset is used for fine-tuning. In the embodiment of the application, the fine-tuning method is to freeze all parameters except the last layer, only learn the characteristics of the local dataset on the parameters of the fully connected layer at the last layer, and fine-tune according to the back propagation. In this way, the transfer learning and fine-tuning of the model are realized, and the accuracy of the model is further improved.

[0070] On the basis of the basic network model, an attention mechanism is added to improve the recognition accuracy. Training and testing are carried out on the dataset, and the "channel first and then space" convolution attention module (CBAM, Convolutional Block Attention Module) is adopted, which fully considers the combination of channel domain attention and spatial domain attention, and increases the average value pooling and convolution operation. The optimized network is used to select the most matched material candidate.

[0071] For each lane ROI, two types of feature vectors are extracted: one from the intermediate layer of the semantic segmentation network; the other from the penultimate layer of the fine-grained classifier. The extracted original floating-point vectors are L2 normalized and subjected to principal component analysis (PCA) to reduce storage and retrieval overhead; at the same time, a set of simple color / texture statistics are generated as auxiliary features. All features are written in a unified format to the JSON metadata field corresponding to the ROI, and are simultaneously written into the vector database for efficient approximate nearest neighbor search.

[0072] Further, the features related to the sidewalk in the above features are taken as the sidewalk information corresponding to the street view image. The above street view image is lane registered with the road network model, and the missing sidewalk is obtained by comparing with the road network model, and the size of the missing sidewalk is calculated according to the proportional relationship between the lane in the road network model and the lane in the street view image.

[0073] Specifically, in the simulation lane contained in the simulation section in the road network model, a simulation lane with complete geometry and reliable attribute is selected as the reference lane in the registered section. The pixel-level mask of the reference lane in the street view image obtained based on semantic segmentation is obtained as the sidewalk mask, and the pixel width of the mask cross section is measured (unit: pixel). The real design width of the reference lane (unit: meter) is recorded in the XML data of the road network model, and the ground sampling distance coefficient is established. Then, for the missing sidewalk marked as missing in the detection stage, the corresponding pixel-level mask is extracted in the same image, and the pixel width (unit: pixel) is measured. The real width of the missing sidewalk can be obtained by the following proportional mapping: ; In this way, the real width of the missing sidewalk can be calculated, which is the size of the missing sidewalk. Further, according to the existing reference points in the reference point sequence, the size of the missing sidewalk is combined to calculate the supplementary reference points corresponding to the sidewalk, thereby realizing the sidewalk completion.

[0074] Specifically, for each reference point in the reference point sequence , a direction vector of the next adjacent reference point is calculated, as shown in the following formula: ; wherein, represents a direction vector formed by the reference point and the adjacent reference point , and respectively represent the horizontal coordinate and the vertical coordinate of the reference point , and respectively represent the horizontal coordinate and the vertical coordinate of the reference point .

[0075] For each reference point , a normal vector (i.e. a vector after rotating 90° clockwise) perpendicular to is calculated, as shown in the following formula: ; wherein, represents a normal vector perpendicular to in the clockwise direction; represents the component of in the direction of , represents the component of in the direction of .

[0076] For each reference point , the corresponding sidewalk supplementary reference point is calculated according to the following formula: ; wherein, represents the total width of the motorway of the current target section, represents the real width of the missing sidewalk.

[0077] In this way, based on the original reference points and the newly generated supplementary reference points, the missing sidewalk can be completed, thereby improving the accuracy of the road network model.

[0078] Figure 2 is a reference point supplement effect schematic diagram provided by an embodiment of the present application, Figure 2 wherein the red area represents an intersection, the black area represents a motorway, and the gray area represents a sidewalk, and the motorway and the sidewalk together constitute a section. Specifically, Figure 2 in which the right-turn ramp is identified as a section that needs to be supplemented with reference points,Figure 2 In the embodiment, the supplement effect is embodied in that the number of reference points of the road section is increased from 6 to 14. Figure 3 FIG. 3 is a schematic diagram of a missing sidewalk supplement effect according to the embodiment of the present application, Figure 3 In the embodiment, the supplement effect is embodied in that the gray sidewalk is added.

[0079] In the embodiment, the reference point supplement of the curved road section and the supplement of the missing sidewalk are implemented based on the road network data processing method. Specifically, for a road section in which the reference point density in a unit length does not satisfy a preset density control condition, a new reference point is inserted according to the Bezier curve between the continuous reference points thereof until the reference point density in the unit length satisfies the preset density control condition. When supplementing the missing sidewalk, the sidewalk information in the street view picture corresponding to the road section is extracted by using an image semantic segmentation model, and is compared with the sidewalk information of the road section in the simulation model to determine whether the sidewalk of the road section is missing. For the road section with the missing sidewalk, the sidewalk size is determined by geometric registration between the street view picture and the lane in the simulation model. Finally, the sidewalk reference points are calculated according to the reference points of the road section and the sidewalk size, so as to implement the supplement of the sidewalk.

[0080] In this way, for the road network model, when the reference point density in the corresponding unit length thereof does not satisfy the preset density control condition, the target reference point is inserted to implement the supplement of the reference points of the road network model. Further, the target road section with the missing traffic channel is determined according to the street view image, and the missing traffic channel in the road network model is supplemented. In this way, the supplement of the reference points and the supplement of the missing traffic channel are implemented for the road network model, so as to solve the problem of low model accuracy caused by the missing reference points, and to be beneficial to improving the accuracy of the road network model.

[0081] Specifically, the road sections of the SUMO simulation road network are taken as the research objects, whether the processing is needed is determined by calculating the reference point density. After determining that the processing is needed, the slope of the Bezier curve on both sides of the reference point is taken as the target, an auxiliary straight line that passes through the reference point and is parallel to the direction of the line connecting the reference points before and after the reference point is made, the control points are selected on the auxiliary straight line according to the proportion, the cubic Bezier curves between the continuous reference points of the curved road section are respectively constructed, and the new reference points are repeatedly added between the reference points with the largest interval according to the constructed Bezier curves. Further, the corresponding street view picture is crawled according to the latitude and longitude of the existing reference points, the sidewalk information is extracted by using the image semantic segmentation model, and is compared with the sidewalk information of the road section in the simulation model to determine whether the sidewalk of the road section is missing. For the road section with the missing sidewalk, the sidewalk element is added, and the sidewalk size is determined by geometric registration between the street view picture and the lane in the simulation model. Finally, the sidewalk reference points are calculated according to the reference points of the road section and the sidewalk size along the normal direction of the sub-road section in which the reference points of the road section are located, and are converted into the geometric shape expression conforming to the simulation road network data specification.

[0082] In view of the problems of missing road section reference points and missing sidewalks in the SUMO simulation model in the conversion process, the Bezier curve interpolation strategy and the street view image semantic segmentation technology are used to realize the completeness and continuity of the road section in geometry and topology, solve the problem of large manual intervention, and improve the accuracy of automatic repair and the efficiency of updating of the road section model.

[0083] As shown in Figure 4 Corresponding to the road network data processing method, the embodiment of the application further provides a road network data processing system, which comprises: A data acquisition module 410 is configured to acquire road network model corresponding to the to-be-processed road network data, and extract the road section length and road section shape data corresponding to each road section from the to-be-processed road network data, wherein the road section shape data comprises the coordinates of the two-dimensional reference points corresponding to the road section. A reference point density determination module 420 is configured to determine the reference point density per unit length corresponding to each road section according to the road section length and the road section shape data. A reference point supplement module 430 is configured to, if the reference point density per unit length does not meet the preset density control condition, determine a control curve according to the coordinates of the two-dimensional reference points in the road section shape data, insert a target reference point in the road network model according to the coordinates of the two-dimensional reference points and the control curve, and obtain a reference point sequence composed of the two-dimensional reference points and the target reference point. An image acquisition module 440 is configured to acquire a street view image matched with the road network model. A target road section determination module 450 is configured to determine a target road section with missing traffic channels in the road network model according to the street view image. A traffic channel completion module 460 is configured to, for the target road section, generate a supplementary reference point corresponding to the missing traffic channel according to the street view image and the reference point sequence, and complete the missing traffic channel in the road network model according to the supplementary reference point.

[0084] In this way, for the road network model, when the reference point density per unit length corresponding to the road network model does not meet the preset density control condition, a target reference point is inserted to complete the reference points of the road network model. Further, the target road section with missing traffic channels is determined according to the street view image, and the missing traffic channels in the road network model are completed. In this way, the reference points and the missing traffic channels of the road network model are completed to solve the problem of low model accuracy caused by missing reference points, which is beneficial to improve the accuracy of the road network model.

[0085] It should be noted that the specific structure and implementation of the road network data processing system and each module or unit thereof can refer to the corresponding description in the above method embodiments, and will not be repeated here.

[0086] It should be noted that the division of each module of the road network data processing system is not unique and is not specifically limited here.

[0087] Based on the above embodiments, the present application also provides a terminal, the principle block diagram of which can be as shown in Figure 5 The terminal includes a processor, a memory, a network interface and a display screen connected through a system bus. The processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the terminal is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the steps of any road network data processing method described above. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen.

[0088] Those skilled in the art can understand that Figure 5 The principle block diagram shown in the above embodiments is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal to which the scheme of the present application is applied. The specific terminal can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0089] In one embodiment, a terminal is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to implement the steps of any road network data processing method provided by the embodiments of the present application.

[0090] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any road network data processing method provided by the embodiments of the present application.

[0091] It should be understood that the serial numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is taken as an example for description, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific names of each functional unit and module are only for convenient distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the apparatus can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0093] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in a certain embodiment can be referred to the related description of other embodiments.

[0094] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or in combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different ways to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0095] In the embodiments provided in the present application, it should be understood that the disclosed system / terminal device and method can be implemented in other ways. For example, the above-described system / terminal device embodiments are only schematic, for example, the division of the above modules or units is only a logical function division, and actual implementation can be in another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0096] The above integrated modules / units, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above embodiment methods can also be completed by a computer program instructing related hardware, and the above computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the above computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, and software distribution medium, etc. It should be noted that the content contained in the above computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0097] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A road network data processing method, characterized in that, The method includes: Obtain the road network data to be processed corresponding to the road network model, and extract the road segment length and road segment shape data corresponding to each road segment from the road network data to be processed, wherein the road segment shape data includes the coordinates of the two-dimensional reference point corresponding to the road segment; Based on the road segment length and road segment shape data, determine the density of reference points per unit length for each road segment; If the density of reference points within the unit length does not meet the preset density control conditions, then a control curve is determined based on the coordinates of the two-dimensional reference points in the road segment shape data. Based on the coordinates of the two-dimensional reference points and the control curve, a target reference point is inserted into the road network model, and a reference point sequence composed of the two-dimensional reference points and the target reference point is obtained. Obtain street view images that match the road network model; Based on the street view image, identify the target road segments in the road network model that have missing traffic channels; For the target road segment, supplementary reference points are generated based on the street view image and the reference point sequence, and the missing traffic channels are completed in the road network model based on the supplementary reference points.

2. The road network data processing method according to claim 1, characterized in that, The step of determining the density of reference points per unit length for each road segment based on the road segment length and road segment shape data includes: The number of two-dimensional reference points corresponding to each road segment is determined based on the road segment shape data. For each road segment, the density of reference points per unit length of the road segment is determined based on the ratio of the number of two-dimensional reference points corresponding to the road segment to the length of the road segment.

3. The road network data processing method according to claim 1, characterized in that, The control curve is a Bézier curve; If the density of reference points within the unit length does not meet the preset density control conditions, a control curve is determined based on the coordinates of the two-dimensional reference points in the road segment shape data. Based on the coordinates of the two-dimensional reference points and the control curve, target reference points are inserted into the road network model, and a reference point sequence consisting of the two-dimensional reference points and the target reference points is obtained, including: Based on the coordinates of the two-dimensional reference points in the road segment shape data, a reference point sequence is constructed; Determine whether the density of reference points within the unit length is greater than a preset minimum density; If the density of reference points within the unit length is not greater than the minimum density, then based on the coordinates of each reference point in the reference point sequence, determine two candidate reference points that are adjacent to each other and have the largest distance between adjacent points from the reference point sequence. Determine the Bézier curve corresponding to the candidate reference point based on the coordinates of the candidate reference point; Based on the Bézier curves corresponding to the candidate reference points, a target reference point is inserted between the candidate reference points, and the reference point sequence is updated; The reference point density within a unit length is updated based on the number of reference points in the reference point sequence, and the step of determining whether the reference point density within a unit length is greater than the preset minimum density is re-executed until the reference point density within a unit length is greater than the minimum density.

4. The road network data processing method according to claim 3, characterized in that, Determining the Bézier curve corresponding to the candidate reference point based on the coordinates of the candidate reference point includes: If the candidate reference point includes the first or last reference point in the reference point sequence, then the second-order Bézier curve corresponding to the candidate reference point is determined based on the coordinates of the candidate reference point. Otherwise, the third-order Bézier curve corresponding to the candidate reference point is determined based on the coordinates of the candidate reference point.

5. The road network data processing method according to claim 3, characterized in that, The step of inserting a target reference point between the candidate reference points based on the Bézier curve corresponding to the candidate reference point and updating the reference point sequence includes: Get the preset position parameter values; The target reference point on the Bézier curve is determined based on the value of the position parameter, and the reference point sequence is updated based on the coordinates of the target reference point.

6. The road network data processing method according to claim 1, characterized in that, The missing traffic access includes missing sidewalks; The step of determining the target road segment in the road network model that has missing traffic channels based on the street view image includes: Perform semantic segmentation on the street view image; The sidewalk information corresponding to the street view image is obtained based on the semantic segmentation results; Based on the sidewalk information corresponding to the street view image and the traffic channel information in the road network model, it is determined whether there are missing sidewalks in each road segment of the road network model, and the road segment with missing sidewalks is selected as the target road segment.

7. The road network data processing method according to claim 6, characterized in that, For the target road segment, generating supplementary reference points corresponding to the missing traffic lanes based on the street view image and the reference point sequence, and completing the missing traffic lanes in the road network model based on the supplementary reference points, includes: Based on the sidewalk information corresponding to the street view image, the size of the missing sidewalk in the target road segment is determined, wherein the sidewalk information includes a sidewalk mask; Based on the reference point sequence and the dimensions of the missing sidewalk, supplementary reference points corresponding to the missing sidewalk are generated, and the missing sidewalk is completed in the road network model based on the supplementary reference points.

8. A road network data processing system, characterized in that, The system includes: The data acquisition module is used to acquire the road network data to be processed corresponding to the road network model, and extract the road segment length and road segment shape data corresponding to each road segment from the road network data to be processed, wherein the road segment shape data includes the coordinates of the two-dimensional reference point corresponding to the road segment; The benchmark density determination module is used to determine the benchmark density per unit length for each road segment based on the road segment length and road segment shape data. The benchmark point supplementation module is used to determine a control curve based on the coordinates of the two-dimensional benchmark points in the road segment shape data if the benchmark point density within the unit length does not meet the preset density control conditions. Based on the coordinates of the two-dimensional benchmark points and the control curve, the module inserts a target benchmark point into the road network model and obtains a benchmark point sequence composed of the two-dimensional benchmark points and the target benchmark point. An image acquisition module is used to acquire street view images that match the road network model; The target road segment determination module is used to determine, based on the street view image, the target road segments in the road network model that have missing traffic channels; The traffic corridor completion module is used to generate supplementary reference points corresponding to the missing traffic corridors based on the street view image and the reference point sequence for the target road segment, and to complete the missing traffic corridors in the road network model based on the supplementary reference points.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the steps of the road network data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the road network data processing method as described in any one of claims 1 to 7.

Images