A method and system for automatic high-precision map processing based on the Qt framework

Abstract

This invention provides a high-precision map automatic processing method and system based on the Qt framework, comprising: Step 1: cropping the road feature class in the high-precision map; Step 2: generating intersection area routes based on the cropped road feature class using an algorithm, and simulating the drivable routes of vehicles at the intersection; Step 3: performing upstream and downstream associations of roads based on the cropped road feature class; Step 4: modifying road boundaries based on the cropped road feature class. With this invention, users only need to select a file to automatically obtain the results, reducing the cost of high-precision map production, improving the accuracy of map information, and reducing the time required for map production processes.

Description

Technical Field

[0001] This invention relates to the field of map building technology, and more specifically, to a high-precision automatic map processing method and system based on the Qt framework. Background Technology

[0002] High-precision maps are maps with higher accuracy and richer content compared to ordinary maps. The data sources for high-precision maps are typically point clouds and images. Due to limitations in sensor accuracy and the obstruction of various real-world obstacles, directly generated maps are often unsuitable for direct application in products, thus requiring map building. In map building, many processes are time-consuming, labor-intensive, and prone to errors when handled manually; therefore, creating specialized automated high-precision map processing tools is essential.

[0003] This invention primarily describes a high-precision map automatic processing tool based on QGIS. QGIS is a user-friendly desktop geographic information system based on Qt, running on platforms such as Linux, Unix, Mac OSX, and Windows. It provides data display, editing, and analysis functions. Qt is a cross-platform C++ graphical user interface application development framework. It is an object-oriented framework that uses a meta-object compiler and macros. Qt is easily extensible and allows for true component programming. Qt supports multiple platforms, including but not limited to Windows, Linux, and MacOS systems.

[0004] Patent document CN112132952A (application number: CN202010833665.6) discloses a method for constructing a 3D mesh map based on a partitioning framework, applied to an intelligent driving system. The method involves determining the spatial range and hierarchy of the 3D mesh map based on the GeoSOT spatial partitioning framework according to the intelligent driving system; using the OctoMap octree algorithm to divide each level of the 3D mesh map into sub-mesh levels; and encoding each sub-mesh level to obtain the 3D mesh map. However, this patent cannot meet the needs of this invention, nor can it solve the existing technical problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a high-precision automatic map processing method and system based on the Qt framework.

[0006] In a first aspect, the present invention provides a high-precision map automatic processing method based on the Qt framework, comprising:

[0007] Step 1: In the high-definition map, perform cropping on the road feature class;

[0008] Step 2: Based on the cropped road feature class, generate the intersection area route using an algorithm and simulate the drivable routes of vehicles at the intersection;

[0009] Step 3: Based on the trimmed road feature class, perform upstream and downstream road associations;

[0010] Step 4: Modify the road boundaries based on the clipped road feature class.

[0011] Preferably, step 1 includes:

[0012] Step 1.1: Build a spatial index for the break_lines feature class;

[0013] Step 1.2: Traverse the road element class and obtain each road element;

[0014] Step 1.3: Find the break lines that intersect with each road element and obtain the set of intersection points formed by these intersection points;

[0015] Step 1.4: Traverse the nodes of the road element, determine the process from the starting point to the ending point of the road in the intersection point set, generate new line segments with these intersection points as the endpoints, and generate line segments in a loop with the endpoint as the starting point of the next line segment. When the road endpoint is reached, the loop ends.

[0016] Step 1.5: Update the generated line segments to the original layer and delete all line segments in the original layer to complete the clipping process.

[0017] Preferably, step 2 includes:

[0018] Step 2.1: Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points;

[0019] Step 2.2: Traverse the first and last points and determine whether there are any overlapping points. Find the points without overlapping points as intersection points and calculate the drivable routes.

[0020] Step 2.3: Using the DBSCAN algorithm, configure appropriate parameters to classify and group all intersection points;

[0021] Step 2.4: For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Before constructing the intersection road, determine whether two roads belong to the same direction. Lanes entering or exiting in the same direction do not constitute a drivable road.

[0022] Preferably, step 3 includes:

[0023] Step 3.1: Build an index for the road feature class;

[0024] Step 3.2: Traverse the road feature class and find the list of road features near each road feature;

[0025] Step 3.3: Traverse the list and determine whether it has an upstream or downstream relationship with the road element;

[0026] Step 3.4: Record the id field of the road element that forms an upstream and downstream relationship, and assign it to the upstream and downstream id fields of the road element.

[0027] Preferably, step 4 includes:

[0028] Step 4.1: Create a temporary point layer representing the point_types field of the boundary layer;

[0029] Step 4.2: Select the corresponding type in the type list, select the part that needs to be modified, change the selected point set to the selected value, and record the ID of the modified boundary element at this time;

[0030] Step 4.3: After making the modifications, save the attributes and update the modified points_type in the boundary layer.

[0031] Secondly, the present invention provides a high-precision automatic map processing system based on the Qt framework, comprising:

[0032] Module M1: In high-precision maps, it performs clipping processing on road feature classes;

[0033] Module M2: Based on the cropped road feature class, it generates intersection area routes through algorithms and simulates the drivable routes of vehicles at the intersection;

[0034] Module M3: Based on the cropped road feature class, perform upstream and downstream road associations;

[0035] Module M4: Modifies road boundaries based on the clipped road feature class.

[0036] Preferably, the module M1 includes:

[0037] Module M1.1: Constructs a spatial index for the break_lines feature class;

[0038] Module M1.2: Traverse the road feature class and obtain each road feature;

[0039] Module M1.3: Find the break lines that intersect with each road feature and obtain the set of intersection points formed by these intersection points;

[0040] Module M1.4: Traverses the nodes of the road element, determines the road start point to the road end point in the intersection point set, generates new line segments with these intersection points as the end points, and generates line segments in a loop with the end point as the start point of the next line segment. When the road end point is reached, the loop ends.

[0041] Module M1.5: Updates the generated line segments to the original layer and deletes all line segments in the original layer, completing the trimming process.

[0042] Preferably, the module M2 includes:

[0043] Module M2.1: Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points;

[0044] Module M2.2: Traverse the first and last points, determine whether there are any overlapping points, find the points without overlapping points as intersection points, and calculate the drivable routes;

[0045] Module M2.3: Utilizes the DBSCAN algorithm, with appropriate parameters configured, to classify and group all intersection points;

[0046] Module M2.4: For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Before constructing the intersection road, determine whether two roads belong to the same direction. Lanes entering and exiting in the same direction do not constitute a drivable road.

[0047] Preferably, the module M3 includes:

[0048] Module M3.1: Builds an index for road feature classes;

[0049] Module M3.2: Traverse the road feature class and find the list of road features near each road feature;

[0050] Module M3.3: Traverse the list and determine whether it has an upstream or downstream relationship with the road element;

[0051] Module M3.4: Records the id field of road elements that form upstream and downstream relationships, and assigns it to the upstream and downstream id fields of the road elements.

[0052] Preferably, the module M4 includes:

[0053] Module M4.1: Creates a temporary point layer representing the point_types field of the boundary layer;

[0054] Module M4.2: Select the corresponding type in the type list, select the part that needs to be modified, and change the selected point set to the selected value. At this time, record the ID of the modified boundary element.

[0055] Module M4.3: After modification, save the attributes and update the modified points_type to the boundary layer.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention illustrates the advantages of automation tools in the production process of high-precision maps by describing several classic tools. Users only need to select a file to automatically process and obtain the results, which reduces the cost of high-precision map production, improves the accuracy of map information, and reduces the time of map production process. Attached Figure Description

[0058] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0059] Figure 1 This is a diagram of the automated processing architecture for high-precision maps.

[0060] Figure 2 This is a structural diagram of a high-precision map automated processing tool.

[0061] Figure 3 This is a schematic diagram of the road element class before clipping;

[0062] Figure 4 This is a diagram showing the cropped road element class.

[0063] Figure 5 Flowchart for implementing clipping of road element classes;

[0064] Figure 6 This is a schematic diagram of a crossroads, representing a road element.

[0065] Figure 7 A schematic diagram simulating drivable routes at road intersections is generated.

[0066] Figure 8 Generate a road mobility flowchart for the road feature class;

[0067] Figure 9 This is a graph showing the relationship between the road element and its upstream and downstream attributes.

[0068] Figure 10 Add an upstream and downstream relationship diagram for the road element;

[0069] Figure 11 This is a diagram showing the upstream and downstream connections of the road.

[0070] Figure 12 This is a schematic diagram of the boundary in its normal state.

[0071] Figure 13 A diagram illustrating the boundary type;

[0072] Figure 14 A diagram illustrating the modification of the boundary category;

[0073] Figure 15 A screenshot showing the tool for modifying boundary points_type and its usage. Detailed Implementation

[0074] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0075] Example 1:

[0076] This invention provides a high-precision map automatic processing method based on the Qt framework, which reduces the cost of high-precision map production, improves the accuracy of map information, and reduces the time of map production process.

[0077] This invention provides a high-precision automatic map processing method based on the Qt framework, such as... Figure 1 As shown, it includes:

[0078] Step 1: In the high-definition map, perform cropping on the road feature class;

[0079] In one embodiment, step 1 includes:

[0080] Step 1.1: Build a spatial index for the break_lines feature class;

[0081] Step 1.2: Traverse the road element class and obtain each road element;

[0082] Step 1.3: Find the break lines that intersect with each road element and obtain the set of intersection points formed by these intersection points;

[0083] Step 1.4: Traverse the nodes of the road element, determine the process from the starting point to the ending point of the road in the intersection point set, generate new line segments with these intersection points as the endpoints, and generate line segments in a loop with the endpoint as the starting point of the next line segment. When the road endpoint is reached, the loop ends.

[0084] Step 1.5: Update the generated line segments to the original layer and delete all line segments in the original layer to complete the clipping process.

[0085] Step 2: Based on the cropped road feature class, generate the intersection area route using an algorithm and simulate the drivable routes of vehicles at the intersection;

[0086] In one embodiment, step 2 includes:

[0087] Step 2.1: Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points;

[0088] Step 2.2: Traverse the first and last points and determine whether there are any overlapping points. Find the points without overlapping points as intersection points and calculate the drivable routes.

[0089] Step 2.3: Using the DBSCAN algorithm, configure appropriate parameters to classify and group all intersection points;

[0090] Step 2.4: For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Before constructing the intersection road, determine whether two roads belong to the same direction. Lanes entering or exiting in the same direction do not constitute a drivable road.

[0091] Step 3: Based on the trimmed road feature class, perform upstream and downstream road associations;

[0092] In one embodiment, step 3 includes:

[0093] Step 3.1: Build an index for the road feature class;

[0094] Step 3.2: Traverse the road feature class and find the list of road features near each road feature;

[0095] Step 3.3: Traverse the list and determine whether it has an upstream or downstream relationship with the road element;

[0096] Step 3.4: Record the id field of the road element that forms an upstream and downstream relationship, and assign it to the upstream and downstream id fields of the road element.

[0097] Step 4: Modify the road boundaries based on the clipped road feature class.

[0098] In one embodiment, step 4 includes:

[0099] Step 4.1: Create a temporary point layer representing the point_types field of the boundary layer;

[0100] Step 4.2: Select the corresponding type in the type list, select the part that needs to be modified, change the selected point set to the selected value, and record the ID of the modified boundary element at this time;

[0101] Step 4.3: After making the modifications, save the attributes and update the modified points_type in the boundary layer.

[0102] Example 2:

[0103] The present invention also provides a high-precision map automatic processing system based on the Qt framework. The high-precision map automatic processing system based on the Qt framework can be implemented by executing the process steps of the high-precision map automatic processing method based on the Qt framework. That is, those skilled in the art can understand the high-precision map automatic processing method based on the Qt framework as a preferred embodiment of the high-precision map automatic processing system based on the Qt framework.

[0104] The high-precision map automatic processing system based on the Qt framework provided by the present invention includes: module M1: performing cropping processing on road feature classes in the high-precision map; module M2: generating intersection area routes based on the cropped road feature classes through an algorithm, and simulating the drivable routes of vehicles at the intersection; module M3: performing upstream and downstream association of roads based on the cropped road feature classes; and module M4: modifying road boundaries based on the cropped road feature classes.

[0105] Module M1 includes: Module M1.1: Constructing a spatial index for the break_lines feature class; Module M1.2: Traversing the road feature class and obtaining each road feature; Module M1.3: Finding the break_lines that intersect with each road feature and obtaining the intersection point set formed by these intersection points; Module M1.4: Traversing the nodes of the road feature, determining the process from the road start point to the road end point in the intersection point set, generating new line segments with these intersection points as the end points, and cyclically generating line segments with the end point as the start point of the next line segment. When the road end point is reached, the loop ends; Module M1.5: Updating the generated line segments to the original layer and deleting all line segments in the original layer, completing the clipping process.

[0106] Module M2 includes: Module M2.1: Traverses the road feature class, obtains the first and last points of each feature, and establishes an index for these points; Module M2.2: Traverses the first and last points, determines whether there are overlapping points, finds points without overlapping points as intersection points, and calculates drivable routes; Module M2.3: Uses the DBSCAN algorithm, configures appropriate parameters, and classifies and groups all intersection points; Module M2.4: For points in each group, obtains road features, and connects the starting points according to the direction of the road features to generate drivable roads at intersections. Before constructing intersection roads, it is determined whether two roads belong to the same direction. Lanes entering and exiting in the same direction do not constitute drivable roads.

[0107] The module M3 includes: Module M3.1: constructing an index for the road feature class; Module M3.2: traversing the road feature class and finding a list of road features near each road feature; Module M3.3: traversing the list and determining whether it forms an upstream or downstream relationship with the road feature; Module M3.4: recording the id field of the road feature that forms an upstream or downstream relationship and assigning it to the upstream or downstream id field of the road feature.

[0108] Module M4 includes: Module M4.1: Creates a temporary point layer representing the point_types field of the boundary based on the boundary layer; Module M4.2: Selects the corresponding type in the type, selects the part to be modified, changes the selected point set to the selected value, and records the ID of the modified boundary feature; Module M4.3: Saves the attributes after modification and updates the modified points_type to the boundary layer.

[0109] Example 3:

[0110] like Figure 1 This tool is based on QGIS and features customized development for high-precision map processing and analysis, building upon QGIS's existing data display and preview capabilities. The main GUI is implemented using QT for Python and supports multiple high-precision map data formats, including but not limited to .shp and .geojson. Its primary purpose is to facilitate the rapid generation of high-precision maps and reduce manual processing.

[0111] This tool requires the following versions: QT5, Python3, and QGIS3.16.

[0112] like Figure 2High-precision map automated processing tools cover a wide range of aspects and multiple tools. This article focuses on describing four of them: automated road segment clipping, road drivable route simulation, road upstream and downstream correlation, and auxiliary semi-automated road boundary line modification.

[0113] The first three are fully automatic tools, requiring only the user to select the corresponding layer to use them. The road boundary line modification tool assists users in quickly modifying boundary line types that are difficult to modify manually.

[0114] (1) Road segment trimming

[0115] In high-precision maps, road segment layers (hereinafter referred to as road feature classes) are stored as line features. Initially, the road feature class is often generated without considering attribute meaning; it's simply a long line feature. However, in actual traffic network topology, a long road segment usually needs to be broken up. Each road segment has a corresponding name. For example, two road segments with different names are spatially connected. Even if a road segment has the same name, its appearance can differ due to the influx of other roads. Therefore, in some special cases, breaking up the road segment is necessary.

[0116] interrupt before and after Figure 3 and Figure 4 As shown. The Road feature class is broken at its intersection with the Break feature class, generating new road features. The specific process is as follows: Figure 5 .

[0117] Specific implementation steps:

[0118] 1. Build a spatial index for the clipping layer (hereinafter referred to as the break_lines feature class);

[0119] 2. Traverse the clipped layer (hereinafter referred to as the road feature class) and obtain each road feature;

[0120] 3. Find the break lines that intersect with each road element and obtain the set of intersection points formed by these intersection points;

[0121] 4. Traverse the nodes of the road element, determine the process from the starting point to the ending point of the road in the intersection point set, generate new line segments with these intersection points as the endpoints, and then generate line segments in a loop with the endpoints as the starting points of the next line segment. When the road endpoint is reached, the loop ends.

[0122] 5. Update the generated line segments to the original layer and delete all line segments in the original layer. This completes the trimming process.

[0123] (2) Simulation of drivable routes at intersections

[0124] High-definition maps, especially those depicting intersections, often struggle to accurately represent the actual conditions of intersections, whether represented by long or short line segments. Figure 6 The possible routes for vehicles need to be determined based on actual conditions, including left and right turns, and also lane classifications, such as the distinction between motor vehicle lanes and non-motor vehicle lanes. Therefore, routes in intersection areas often need to be generated using algorithms to simulate the possible routes for vehicles at the intersection. Figure 7 .

[0125] like Figure 8 To generate a feasible road flowchart for the road element class, the specific implementation steps are as follows:

[0126] 1. Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points.

[0127] 2. Traverse the first and last points and determine whether there are any overlapping points. Find the points without overlapping points. These points can be considered as intersection points, and the drivable routes need to be calculated.

[0128] 3. Using the DBScan algorithm, configure appropriate parameters to classify and group all intersection points.

[0129] For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Note that lanes entering or exiting in the same direction are not considered drivable roads. Therefore, before constructing the intersection road, determine whether two roads belong to the same direction.

[0130] (3) Road upstream and downstream connections

[0131] In the Road feature class, road segments that are related upstream and downstream should not only have topological connections, but also reflect these connections in their attributes. Therefore, each road feature has `up_ids` and `down_ids` attributes, which reflect the upstream and downstream relationships of the road feature. Furthermore, when merging different data sets, the relationships between adjacent road attributes still need to be processed. Therefore, automated processing of upstream and downstream road relationships is a necessary feature.

[0132] Furthermore, different layers may also form upstream and downstream relationships. This function can also be used to establish upstream and downstream associations for subsequent queries of upstream and downstream line features, such as... Figure 9 and Figure 10 The specific implementation steps are as follows: Figure 11 :

[0133] 1. Build an index for the road feature class;

[0134] 2. Traverse the road feature class and find the list of road features near each road feature;

[0135] 3. Traverse the list and determine whether it has an upstream or downstream relationship with the road element;

[0136] 4. Record the id field of the road element that forms an upstream and downstream relationship, and assign it to the upstream and downstream id fields of the road element.

[0137] (4) Road boundary modification

[0138] Road boundaries are a complex issue; typically, road boundaries are defined by curbs, such as... Figure 12 However, due to parking and other road obstructions near road boundaries, point cloud maps have extremely limited understanding of this data. Furthermore, determining the yellow / white lines and dashed / solid lines between different roads is complex, requiring manual modification. Traditional manual modification is time-consuming, labor-intensive, and sometimes impossible (a boundary line may have different types in different locations). Therefore, this tool generates a temporary point layer based on the `points_type` attribute of the road boundary layer (hereinafter referred to as the boundary feature class). Users can easily determine the type of the boundary in this part based on the rendering of the temporary point layer. The tool also provides a quick category modification function; by simply selecting the type and selecting points, the type of these points can be modified, and the changed line feature index is recorded. When saving the changes, a new `points_type` is generated based on the changed line feature index and the corresponding point set in the temporary point layer, and the `points_type` field value of this part of the boundary feature is modified.

[0139] After saving, reopen the generated points_type layer preview to see the temporary layer that was created at this time. Figure 13 As shown. During the subsequent vehicle driving simulation, the boundary line of this section of the road will be a light gray solid line, as shown. Figure 14 .

[0140] like Figure 15 This section describes the tool for modifying the boundary points_type and its usage. The specific process is as follows:

[0141] 1. Create a temporary point layer based on the boundary layer, representing the point_types field of the boundary;

[0142] 2. Select the corresponding type in the type list, and select the part that needs to be modified. The selected point set will then be changed to the selected value. At this time, record the ID of the modified boundary element.

[0143] 3. After making the modifications, save the attributes, and the modified points_type will be updated in the boundary layer.

[0144] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0145] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A high-precision map automatic processing method based on the Qt framework, characterized in that, include: Step 1: In the high-definition map, perform cropping on the road feature class; Step 2: Based on the cropped road feature class, generate the intersection area route using an algorithm and simulate the drivable routes of vehicles at the intersection; Step 3: Based on the trimmed road feature class, perform upstream and downstream road associations; Step 4: Modify the road boundaries based on the clipped road feature class; Step 1 includes: Step 1.1: Build a spatial index for the break_lines feature class; Step 1.2: Traverse the road element class and obtain each road element; Step 1.3: Find the break lines that intersect with each road element and obtain the set of intersection points formed by these intersection points; Step 1.4: Traverse the nodes of the road element, determine the process from the road start point to the road end point in the intersection point set, generate new line segments with these intersection points as the end points, and repeat the process of generating line segments with the end point as the start point of the next line segment. When the road end point is reached, the loop ends. Step 1.5: Update the generated line segments to the original layer and delete all line segments in the original layer to complete the clipping process; Step 2 includes: Step 2.1: Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points; Step 2.2: Traverse the first and last points and determine whether there are any overlapping points. Find the points without overlapping points as intersection points and calculate the drivable routes. Step 2.3: Using the DBSCAN algorithm, configure appropriate parameters to classify and group all intersection points; Step 2.4: For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Before constructing the intersection road, determine whether two roads belong to the same direction. Lanes entering and exiting in the same direction do not constitute a drivable road. Step 4 includes: Step 4.1: Create a temporary point layer representing the point_types field of the boundary layer; Step 4.2: Select the corresponding type in the type list, select the part that needs to be modified, change the selected point set to the selected value, and record the ID of the modified boundary element at this time; Step 4.3: After making the modifications, save the attributes and update the modified points_type in the boundary layer.

2. The high-precision map automatic processing method based on the Qt framework according to claim 1, characterized in that, Step 3 includes: Step 3.1: Build an index for the road feature class; Step 3.2: Traverse the road feature class and find the list of road features near each road feature; Step 3.3: Traverse the list and determine whether it has an upstream or downstream relationship with the road element; Step 3.4: Record the id field of the road element that forms an upstream and downstream relationship, and assign it to the upstream and downstream id fields of the road element.

3. A high-precision map automatic processing system based on the Qt framework, characterized in that, include: Module M1: In high-precision maps, it performs clipping processing on road feature classes; Module M2: Based on the cropped road feature class, it generates intersection area routes through algorithms and simulates the drivable routes of vehicles at the intersection; Module M3: Based on the cropped road feature class, perform upstream and downstream road associations; Module M4: Modifies road boundaries based on the clipped road feature class; The module M1 includes: Module M1.1: Constructs a spatial index for the break_lines feature class; Module M1.2: Traverse the road feature class and obtain each road feature; Module M1.3: Find the break lines that intersect with each road feature and obtain the set of intersection points formed by these intersection points; Module M1.4: Traverses the nodes of the road element, determines the process from the road start point to the road end point in the intersection point set, generates new line segments with these intersection points as the end points, and generates line segments in a loop with the end point as the start point of the next line segment. When the road end point is reached, the loop ends. Module M1.5: Updates the generated line segments to the original layer and deletes all line segments in the original layer, completing the clipping process; The module M2 includes: Module M2.1: Traverse the road feature class, obtain the first and last points of each feature, and create an index for these points; Module M2.2: Traverse the first and last points, determine whether there are any overlapping points, find the points without overlapping points as intersection points, and calculate the drivable routes; Module M2.3: Utilizes the DBSCAN algorithm, with appropriate parameters configured, to classify and group all intersection points; Module M2.4: For each group of points, obtain the road element, and connect the starting points according to the direction of the road element to generate a drivable road at the intersection. Before constructing the intersection road, determine whether two roads belong to the same direction. Lanes entering and exiting in the same direction do not constitute a drivable road. The module M4 includes: Module M4.1: Creates a temporary point layer representing the point_types field of the boundary layer; Module M4.2: Select the corresponding type in the type list, select the part that needs to be modified, and change the selected point set to the selected value. At this time, record the ID of the modified boundary element. Module M4.3: After modification, save the attributes and update the modified points_type to the boundary layer.

4. The high-precision map automatic processing system based on the Qt framework according to claim 3, characterized in that, The module M3 includes: Module M3.1: Builds an index for road feature classes; Module M3.2: Traverse the road feature class and find the list of road features near each road feature; Module M3.3: Traverse the list and determine whether it has an upstream or downstream relationship with the road element; Module M3.4: Records the id field of road elements that form upstream and downstream relationships, and assigns it to the upstream and downstream id fields of the road elements.

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