A microcosmic traffic simulation data map engine adaptation conversion method and system

Abstract

The application discloses a kind of microcosmic traffic simulation data's map engine adaptation conversion method and system, it is related to traffic simulation technical field.The method includes: extracting road section information and lane information from simulation road network file;According to road section information, judge road section nature, according to road section information and road section nature, construct independent attribute set for each road;Based on driving lane line shape and lane width, generate driving lane surface file for each road, the independent attribute set constructed is used as the attribute of corresponding driving lane surface file;Parallel offset is carried out to driving lane line shape, form emergency lane surface file;Driving lane surface file and emergency lane surface file are carried out gap correction;According to independent attribute set, establish style rule library, judge the required lane type, realize automatic marking line adaptation according to lane type, generate lane line file;Lane line file, corrected driving lane surface file and emergency lane surface file are loaded to geographic information service software.

Description

Technical Field

[0001] This invention relates to the field of traffic simulation technology, and in particular to a map engine adaptation and conversion method and system for microscopic traffic simulation data. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the in-depth development of technologies such as smart highways and digital twins, using microscopic traffic simulation (such as VISSIM and SUMO) to model, analyze, and evaluate highways has become an important method. Map engines have been widely used due to their superior display capabilities.

[0004] Currently, loading microscopic traffic simulation data into map engines still presents several challenges: the data structure and graphical representation of simulation road network files (such as .inpx, .net, and .xml) are incompatible with the common geospatial data formats and layer management mechanisms required by map engines. Simulated lanes are logically connected, while maps require graphical surfaces with visual attributes. Directly converting from the simulation road network simplifies the entire road segment into a single line, failing to accurately reproduce lane-level details such as lane width and emergency lanes, as well as lane markings that reflect actual road conditions. Furthermore, the massive amount of vehicle trajectory data output from simulations causes map engine lag when directly transmitted and rendered. The lack of effective trajectory compression and status coding mechanisms prevents smooth, real-time display of large-scale vehicle dynamics within limited network bandwidth and fails to convey driving behaviors such as sudden braking or lane changes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a map engine adaptation and conversion method and system for microscopic traffic simulation data, thereby overcoming the problems of data format incompatibility, loss of detail, and low rendering efficiency between microscopic traffic simulation data and map engine visualization in existing technologies.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a map engine adaptation and conversion method for microscopic traffic simulation data is provided, including: Extract road segment and lane information from the simulated road network file; lane information includes lane alignment and lane width. Determine the nature of a road segment based on its information, and construct an independent attribute set for each road based on the road segment information and its nature. For each road, a lane surface file is generated based on the lane alignment and lane width, and the constructed independent attribute set is used as the attribute of the corresponding lane surface file; the lane alignment is parallel offset to form an emergency lane surface file; gap correction is performed on the lane surface file and the emergency lane surface file; A style rule library is established based on an independent attribute set, the required lane type is determined, and automatic lane marking adaptation is achieved based on the lane type to generate lane line files. Load the lane line files, the corrected driving lane surface files, and the emergency lane surface files into the geographic information service software.

[0007] Secondly, a map engine adaptation and conversion system for microscopic traffic simulation data is provided, including: The road basic information extraction module is used to extract road segment information and lane information from the simulated road network file; lane information includes lane alignment and lane width; The road segment nature determination module is used to determine the nature of a road segment based on road segment information, and to construct an independent attribute set for each road based on the road segment information and road segment nature. The lane surface generation and correction module is used to generate lane surface files for each road based on the lane alignment and lane width, and use the constructed independent attribute set as the attributes of the corresponding lane surface files; it performs parallel offset on the lane alignment to form emergency lane surface files; and it performs gap correction on the lane surface files and emergency lane surface files. The lane marking adaptation and generation module is used to establish a style rule library based on an independent attribute set, determine the required lane type, and automatically adapt lane markings according to the lane type to generate lane line files. The geographic information service loading module is configured to load lane line files, corrected driving lane surface files, and emergency lane surface files into the geographic information service software.

[0008] Thirdly, an electronic device is also provided, comprising: Memory, used for non-transitory storage of computer-readable instructions; and Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0009] Fourthly, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements the method described in the first aspect above.

[0010] The above technical solution has the following advantages or beneficial effects: (1) This invention realizes end-to-end automated conversion from microscopic simulated road network to map engine layer. By identifying road segments through road segment information and combining curvature adaptive correction and gap correction algorithms, lane line files, driving lane surface files and emergency lane surface files are accurately generated, realistically restoring lane-level details and solving the problem that roads are easily simplified to a single line type and details are lost when loading microscopic traffic simulation data into the map engine.

[0011] (2) This invention proposes a speed-direction dual threshold adaptive keyframe extraction algorithm, which effectively compresses massive trajectory data; at the same time, it encodes the driving behavior of vehicles into compact status codes, which significantly reduces network transmission pressure, ensures smooth and real-time rendering of large-scale vehicle dynamics, and solves the problem of map engine lag caused by the large amount of simulation data.

[0012] (3) The present invention adopts a modular architecture design, which facilitates system integration and expansion. At the same time, it supports user-defined adjustments in multiple aspects such as lane width, curvature correction, and lane marking style, which can flexibly adapt to different simulation scenarios and map display requirements, significantly improving the versatility and practicality of the system. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0014] Figure 1 This is a flowchart of a map engine adaptation and conversion method for microscopic traffic simulation data in a specific embodiment of the present invention. Detailed Implementation

[0015] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0016] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] In this embodiment of the invention, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of this invention, "multiple" refers to two or more.

[0018] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0019] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0020] Example 1 like Figure 1 As shown, this embodiment provides a map engine adaptation and conversion method for microscopic traffic simulation data, including: S1: Extract road segment information and lane information from the simulated road network file; lane information includes lane alignment and lane width; S2: Determine the nature of a road segment based on the road segment information, and construct an independent attribute set for each road based on the road segment information and road segment nature; S3: Generate a lane surface file for each road based on the lane alignment and the lane width, and use the constructed independent attribute set as the attribute of the corresponding lane surface file; perform parallel offset on the lane alignment to form an emergency lane surface file; perform gap correction on the lane surface file and the emergency lane surface file; S4: Establish a style rule library based on independent attribute sets, determine the required lane type, implement automated lane marking adaptation based on lane type, and generate lane line files; S5: Load the lane line files, the corrected driving lane surface files, and the emergency lane surface files into the geographic information service software.

[0021] The specific steps of S1 are as follows: S1.1: Extract basic road information from the simulated road network file to form structured data.

[0022] In some embodiments, the simulated road network file includes VISSIM's .inpx and SUMO's .net.xml.

[0023] S1.2: The extracted basic road information is divided into road segment information and lane information. The road segment information includes road segment ID, speed limit, number of lanes, curvature, road segment length, and lane connection relationship; the lane information includes lane alignment and lane width.

[0024] The specific steps of S2 are as follows: S2.1: Using speed limits, number of lanes, curvature, and road segment length from the road segment information as the criterion layer, determining the road segment nature as the target layer, and mainline and ramps as the solution layers, the Analytic Hierarchy Process (AHP) is used for judgment. The road segment nature refers to determining whether the road segment is a mainline or a ramp. The purpose of this step is to prevent errors in judgment caused by the existence of roads with complex characteristics, such as collector-distributor roads or highway connecting roads, which could affect subsequent conversions.

[0025] In some embodiments, the specific judgment process using the Analytic Hierarchy Process (AHP) is as follows: First, a criterion layer judgment matrix is ​​constructed. Experts are then convened to score the pairwise importance of speed limit, number of lanes, curvature, and road segment length within the criterion layer. Next, the judgment matrix is ​​normalized and information weights are calculated. Finally, the consistency ratio is calculated to verify the rationality of the judgment results, thus completing the construction of the AHP method. When making a judgment, the speed limit, number of lanes, curvature, and road segment length from the road segment information are input. Combined with the information weights, the scores of each scheme can be calculated, thereby determining the nature of the road segment.

[0026] S2.2: Merge the determined road segment properties with the corresponding road segment information to construct an independent attribute set for each road.

[0027] The specific steps for S3 are as follows: S3.1: Generate lane surface files. Generate lane surface files for each road based on lane alignment and lane width, and use the constructed independent attribute set as the attributes of the corresponding lane surface files.

[0028] In this embodiment, the specific steps for generating a lane surface file for each road based on the lane alignment and lane width are as follows: the lane alignment located at the center line of the lane is offset to the left and right sides respectively, and the coordinate sequence of any one of the two offset lines is reversed and combined with the coordinate sequence of the other line to construct the lane surface file.

[0029] S3.2: Perform a parallel offset on the driving lane alignment to create an emergency lane surface file. The specific steps are as follows: S3.2.1: Based on the lane width attribute and common emergency lane widths, calculate the basic offset of the emergency lane. The specific formula is as follows: (1); In the formula, d is the basic parallel offset of the emergency lane relative to the centerline of the driving lane. The overall width of the driving lane. The emergency lane width is set to 3m in this embodiment, but can be customized by the user.

[0030] S3.2.2: For irregularly shaped driving roads with transition curves, small-radius bends, and lane widening, adaptive curvature correction is performed. This step can solve the problem of accuracy distortion after irregular alignment deviation in existing technologies.

[0031] The formula for adaptive correction of irregular linear curvature is: (2); In the formula, This is the final offset after curvature correction. This is the curvature correction factor (default 0.02, can be customized by the user). The curvature is calculated based on the lane alignment information.

[0032] The method for calculating the centerline coordinates of the emergency lane after correction for irregularly shaped driving roads with transition curves, small-radius bends, and lane widening is as follows: (3); (4); In the formula, Let the coordinates be any point on the center line of the driving lane. The tangent direction angle at that point. These are the coordinates of the centerline point of the corresponding emergency lane.

[0033] S3.2.3: Perform a parallel offset on the driving lane alignment to create an emergency lane surface file.

[0034] S3.3: Due to the parallel offset in step S3.2, there may be a small gap between the driving lane and the emergency lane. Therefore, it is necessary to correct the gap between the driving lane surface file and the emergency lane surface file.

[0035] In this embodiment, a surface alignment correction algorithm is designed to combine the curvature continuity constraint of irregular lines to perform gradient correction on the gap width, avoiding line deformation after correction, and achieving seamless connection between the driving lane surface file and the emergency lane surface file. The specific steps are as follows: S3.3.1: Calculate the gap width: (5); In the formula, For any point on the driving lane surface near the threshold boundary of the emergency lane surface, For the emergency lane surface boundary relative to The closest point The time distance between the two points is the gap width.

[0036] S3.3.2: Calculate the gradient correction offset: (6); In the formula, Let be the unit normal vector pointing from the boundary of the driving lane area to the boundary of the emergency lane area. Let the normal vector be the magnitude. As a correction factor, the default value in this embodiment is 0.2, and users can adjust it as needed.

[0037] S3.3.3: Correct the emergency lane boundary coordinates based on the gradient correction offset, and calculate the corrected emergency lane boundary coordinates: (7); (8); In the formula, From the coordinates of two points and Calculated.

[0038] The specific steps of S4 are as follows: S4.1: Establish a style rule library based on independent attribute sets to determine the required lane type.

[0039] In this embodiment, the style library rule base consists of the overall lane edge line, lane internal markings, and merging / diverging point markings, etc. The specific rules of the style library rule base are as follows: Overall lane boundary rules: If the road segment is a main line and there is no merging or diverging relationship, the outermost boundary of the driving lane shall be a solid line with a default width of 0.15m; if the road segment is a ramp, both sides of the driving lane shall be solid lines.

[0040] Defines the dividing lines between lanes traveling in the same direction. When the road segment is a main line, it is adapted as a dashed line (i.e., dashed lines inside the driving lane), with a default line width of 0.15m. The lengths of the dashed line segments and interval segments are configured according to the 69-line rule. When the road segment is a ramp and is a single lane, no internal dashed lines are generated; if the ramp has multiple lanes traveling in the same direction, the internal lane dividing lines are also adapted as dashed lines, and the parameters can be the same as those for the main line.

[0041] Merging and diverging lane marking rules. Merging and diverging zones are identified by lane connection relationships: if the nature of a preceding or succeeding connecting road segment changes (e.g., from a main road to a ramp), then that location is determined to be a merging or diverging zone; within merging and diverging zones, the lane marking type at the intersection boundary is adapted to short dashed lines, and the spacing and length of the dashed lines can be user-defined parameters to distinguish them from regular internal dashed lines.

[0042] S4.2: The generated lane line files specifically include solid line surface files, dashed line surface files, and short dashed line surface files. The generation process for each is as follows: S4.2.1: For lane edge lines, in sections without merging or diverging lanes, the lane width information is used to offset the lane edge line to the roadside position, and a solid line surface file is formed by combining it with the commonly used solid line width. Simultaneously, for irregular line shapes, the same curvature adaptive correction as in S3 is used to avoid edge line distortion. In this embodiment, the commonly used solid line width is 0.15 meters.

[0043] S4.2.2: For dashed lines inside the driving lane, offset the driving lane alignment to between driving lanes and break them according to the 69 line rule, and combine them with the commonly used dashed line width to form a dashed line surface file.

[0044] S4.2.3: For merging and diverging points, identify the positional relationship between the mainline and the ramp, generate short dashed line surface files at the connection points, and the spacing between the dashed lines can be defined according to user needs. Correct the lane edge lines and ramp surface information. The specific process is as follows: S4.2.3.1: First, determine the positional relationship between the lane edge line and the ramp surface file. If there is no overlapping area, no processing is required. If there is an intersecting area, proceed with the steps in S4.2.3.2. Specifically, since the lane edge line overlaps to some extent with the ramp surface at the merging and diverging points, it is necessary to determine the positional relationship between the lane edge line and the ramp surface file.

[0045] S4.2.3.2: Extract the lane edge lines of the intersecting parts, break them according to the set rules, and delete the original edge lines to form a corrected lane surface file. In this embodiment, the set rules adopt the 69-line principle, which users can customize according to their needs.

[0046] S4.2.3.3: Use the lane edge lines to cut the ramp surface, extract the ramp surface region with the smallest area, delete the solid and dashed lines of the ramp lanes on this surface region, and delete the ramp surface region.

[0047] The specific steps of S5 are as follows: The lane line files, gap-corrected driving lane surface files, and emergency lane surface files are loaded into geographic information service software, such as GeoServer. In this embodiment, to ensure map engine loading efficiency, a two-level layer system of base layer and detail layer is constructed: the base layer contains driving lane and emergency lane surfaces, and the detail layer contains various road markings; layer styles are configured, and a tile caching mechanism is enabled.

[0048] Because the vehicle trajectory data output from the simulation is massive, direct transmission and rendering would cause map engine lag. Therefore, in some embodiments, the map engine adaptation and conversion method for the microscopic traffic simulation data further includes the following steps: S6: Obtain vehicle information for each simulation step; S7: Calculate the rate of change of speed and angle of change of direction of each vehicle based on the vehicle information, and determine the driving status of each vehicle based on the vehicle information, rate of change of speed and angle of change of direction. S8: Based on the driving status, speed change rate and direction change angle of each vehicle, the keyframe extraction algorithm is used to extract trajectory keyframes; S9: Encode the vehicle information and specific driving behavior patterns in the frames corresponding to the extracted trajectory keyframes into status codes. S10: After converting the status code, push it to the map engine for rendering.

[0049] The specific steps of S6 are as follows: The vehicle information for each simulation step can be obtained in real time through the API interface of the simulation software, such as the COM interface of VISSIM and the TraCI of SUMO. The vehicle information includes vehicle ID, coordinates, speed, acceleration and heading angle.

[0050] The specific steps for S7 are as follows: S7.1: Calculate the rate of change of speed for each vehicle. The specific formula is as follows: (9); In the formula, Let be the speed of the vehicle at the j-th simulation step. For the velocity of the (j-1)th simulation step, This is the simulated step size.

[0051] S7.2: Calculate the change angle of direction for each vehicle. The specific formula is as follows: (10); In the formula, Let j be the simulated heading angle of the vehicle. Let be the (j-1)th simulated heading angle of the vehicle.

[0052] S7.3: Based on the vehicle information obtained from S6, including the vehicle's speed change rate and direction change angle, it identifies four vehicle driving states. The specific rules are as follows: 1) High-speed constant speed state: and 2) Low-speed congestion: 3) Changing lanes: ;4) Normal driving: Not meeting any of the above conditions.

[0053] The specific steps for S8 are as follows: S8.1: The adaptive threshold for the velocity-direction dual-threshold adaptive keyframe extraction algorithm is as follows: (11); (12); In the formula, and are the average values of the speed change rate and the direction change angle in the last 10 simulation steps, and the number of simulation steps can also be adjusted according to requirements; is the speed adjustment threshold. In this embodiment, 2 is taken for high speed, 1 is taken for low speed / lane change, and 1.5 is taken for normal driving; is the speed adjustment threshold. In this embodiment, 1.5 is taken for high speed, 1 can be taken for low speed / lane change, and 1.2 can be taken for normal driving. At the same time, for the curve with trajectory curvature reduce and by 20% to avoid trajectory distortion on the curve.

[0054] S8.2: Adopt the speed-direction double-threshold adaptive key frame extraction algorithm to extract the key frames of the trajectory, and only transmit the key frames of the trajectory to the map engine, that is, transmit the points where the trajectory form changes significantly. The specific steps are as follows: When or , it is determined that the vehicle coordinates at the jth simulation step are key frames; at the same time, set a differentiated forced extraction interval in combination with the driving state: when the number of steps without key frame extraction continuously exceeds the preset interval, force the current step to be a key frame. Among them, the preset interval in the high-speed constant-speed state is 10 simulation steps, the preset interval in the low-speed congestion or lane change state is 3 simulation steps, and the preset interval in the normal driving state is 5 simulation steps to avoid trajectory loss.

[0055] The specific steps of S9 are as follows: Combine the basic state of each vehicle under the key frame of the trajectory with the driving behavior mode and encode it into a compact state code to indicate whether the vehicle is in specific driving behavior modes such as emergency braking, lane change, congestion following, etc., so as to reduce the data transmission volume. Adopt the extended encoding method, which consists of a basic segment and an extended segment. Only the basic segment is transmitted when there is no special time scenario, and the extended segment is added in special scenarios. The following is a single example: 1) Basic segment (6-bit binary): ① High 2 bits: Speed level encoding: 00 (stationary, v = 0), 01 (low speed, 0 < v ≤ 10 km / h), 10 (medium speed, 10 < v ≤ 100 km / h), 11 (high speed, v > 100 km / h); ② Middle 2 bits: Basic driving behavior encoding: 00 (normal driving), 01 (emergency braking), 10 (rapid acceleration), 11 (congestion following); ③ Low 2 bits: Vehicle type encoding: 00 (small truck), 01 (small passenger car), 10 (large passenger car), 11 (medium and large trucks and above); Extended segment (2-bit binary): Add driving behavior status as needed, such as 00 (overtaking), 01 (yielding), 10 (driving on a slope), 11 (sharp turn). In S10, the status code is specifically encoded into a JSON string and then pushed to the map engine.

[0056] Example 2 This embodiment provides a map engine adaptation and conversion system for microscopic traffic simulation data, including: The road basic information extraction module is used to extract road segment information and lane information from the simulated road network file; lane information includes lane alignment and lane width; The road segment nature determination module is used to determine the nature of a road segment based on road segment information, and to construct an independent attribute set for each road based on the road segment information and road segment nature. The lane surface generation and correction module is used to generate lane surface files for each road based on the lane alignment and lane width, and use the constructed independent attribute set as the attributes of the corresponding lane surface files; it performs parallel offset on the lane alignment to form emergency lane surface files; and it performs gap correction on the lane surface files and emergency lane surface files. The lane marking adaptation and generation module is used to establish a style rule library based on an independent attribute set, determine the required lane type, and automatically adapt lane markings according to the lane type to generate lane line files. The geographic information service loading module is configured to load lane line files, corrected driving lane surface files, and emergency lane surface files into the geographic information service software.

[0057] It should be noted that the aforementioned road basic information extraction module, road segment nature judgment module, lane surface generation and correction module, lane marking adaptation generation module, and geographic information service loading module correspond to steps S1 to S5 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should be noted that the aforementioned modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0058] In some embodiments, the system further includes: a vehicle information acquisition module, a driving state recognition module, a key frame extraction module, and a state encoding module, each corresponding to steps S6 to S10 in Embodiment 1.

[0059] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0060] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0061] Example 3 This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0062] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0063] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0064] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0065] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0066] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0067] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0068] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the method as described in Embodiment 1 of the present invention.

[0069] The detailed steps are the same as those provided in Example 1, and will not be repeated here.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A map engine adaptation and conversion method for microscopic traffic simulation data, characterized in that, include: Extract road segment and lane information from the simulated road network file; The lane information includes the lane alignment and lane width; The road segment nature is determined based on the road segment information, and an independent attribute set is constructed for each road based on the road segment information and the road segment nature; The process of determining the nature of a road segment based on the road segment information specifically involves using speed limit, number of lanes, curvature, and road segment length as the criterion layer, determining the nature of the road segment as the target layer, and mainline and ramp as the scheme layer, and then using the analytic hierarchy process (AHP) to make the determination. Here, the nature of the road segment refers to determining whether the segment is a mainline or a ramp. Based on the lane alignment and lane width, a lane surface file is generated for each road, and the constructed independent attribute set is used as the attribute of the corresponding lane surface file; the lane alignment is parallel offset to form an emergency lane surface file; Perform gap correction on the driving lane surface files and emergency lane surface files; A style rule library is established based on the independent attribute set, the required lane type is determined, and automatic lane marking adaptation is achieved based on the lane type to generate lane line files. The style rule library consists of the overall edge line of the driving lane, the internal markings of the driving lane, and the markings at the merging and diverging points; Load the lane line files, the corrected driving lane surface files, and the emergency lane surface files into the geographic information service software; The process of parallel offsetting the lane alignment to form an emergency lane surface file specifically involves: The offset is calculated based on the lane width attribute and common emergency lane widths; the specific formula is as follows: ； Where d is the basic parallel offset of the emergency lane relative to the centerline of the driving lane. The overall width of the driving lane. For emergency lane width; For irregularly shaped driving roads with transition curves, small-radius bends, and lane widening, curvature adaptive correction is performed; the formula for irregularly shaped curvature adaptive correction is: ； in, This is the final offset after curvature correction. This is the curvature correction factor. For curvature; The lane alignment is offset parallel to create an emergency lane surface file; The process of correcting gaps in the driving lane surface file and the emergency lane surface file specifically involves: calculating the gap width; calculating the gradient correction offset based on the gap width; and correcting the emergency lane boundary coordinates based on the gradient correction offset. The formula for calculating the gap width is: ； In the formula, For any point on the driving lane surface near the threshold boundary of the emergency lane surface, For the emergency lane surface boundary relative to The closest point The distance between the two points is the gap width; The formula for calculating the gradient correction offset is as follows: ； In the formula, Let be the unit normal vector pointing from the boundary of the driving lane area to the boundary of the emergency lane area. Let the normal vector be the magnitude. As a correction factor; The formula for correcting the emergency lane boundary coordinates based on the gradient correction offset and calculating the corrected emergency lane boundary coordinates is as follows: ； ； In the formula, From the coordinates of two points and Calculated.

2. The map engine adaptation and conversion method for microscopic traffic simulation data as described in claim 1, characterized in that, Also includes: Obtain vehicle information for each simulation step; Calculate the rate of change of speed and angle of change of direction for each vehicle based on the vehicle information, and determine the driving status of each vehicle based on the vehicle information, rate of change of speed, and angle of change of direction. Based on the driving status, speed change rate, and direction change angle of each vehicle, the trajectory keyframes are extracted using a keyframe extraction algorithm. The vehicle information in the corresponding frame of the extracted trajectory keyframe is encoded into a status code along with the specific driving behavior pattern. The status code is converted and then pushed to the map engine for rendering.

3. The map engine adaptation and conversion method for microscopic traffic simulation data as described in claim 1, characterized in that, The road segment information includes road segment ID, speed limit, number of lanes, curvature, road segment length, and lane connection relationships.

4. The map engine adaptation and conversion method for microscopic traffic simulation data as described in claim 1, characterized in that, The lane line files specifically include solid line surface files, dashed line surface files, and short dashed line surface files.

5. A map engine adaptation and conversion system for microscopic traffic simulation data, characterized in that, include: The road basic information extraction module is used to extract road segment information and lane information from the simulated road network file; The lane information includes the lane alignment and lane width; The road segment nature determination module is used to determine the road segment nature based on the road segment information, and to construct an independent attribute set for each road based on the road segment information and the road segment nature; The process of determining the nature of a road segment based on the road segment information specifically involves using speed limit, number of lanes, curvature, and road segment length as the criterion layer, determining the nature of the road segment as the target layer, and mainline and ramp as the scheme layer, and then using the analytic hierarchy process (AHP) to make the determination. Here, the nature of the road segment refers to determining whether the segment is a mainline or a ramp. The lane surface generation and correction module is used to generate lane surface files for each road based on the lane alignment and the lane width, and to use the constructed independent attribute set as the attributes of the corresponding lane surface files; and to perform parallel offset on the lane alignment to form an emergency lane surface file. Perform gap correction on the driving lane surface files and emergency lane surface files; The lane marking adaptation and generation module is used to establish a style rule library based on the independent attribute set, determine the required lane type, realize automated lane marking adaptation based on the lane type, and generate lane line files. The style rule library consists of the overall edge line of the driving lane, the internal markings of the driving lane, and the markings at the merging and diverging points; The geographic information service loading module is configured to load lane line files, corrected driving lane surface files, and emergency lane surface files into the geographic information service software. The process of parallel offsetting the lane alignment to form an emergency lane surface file specifically involves: The offset is calculated based on the lane width attribute and common emergency lane widths; the specific formula is as follows: ； Where d is the basic parallel offset of the emergency lane relative to the centerline of the driving lane. The overall width of the driving lane. For emergency lane width; For irregularly shaped driving roads with transition curves, small-radius bends, and lane widening, curvature adaptive correction is performed; the formula for irregularly shaped curvature adaptive correction is: ； in, This is the final offset after curvature correction. This is the curvature correction factor. For curvature; The lane alignment is offset parallel to create an emergency lane surface file; The process of correcting gaps in the driving lane surface file and the emergency lane surface file specifically involves: calculating the gap width; calculating the gradient correction offset based on the gap width; and correcting the emergency lane boundary coordinates based on the gradient correction offset. The formula for calculating the gap width is: ； In the formula, For any point on the driving lane surface near the threshold boundary of the emergency lane surface, For the emergency lane surface boundary relative to The closest point The distance between the two points is the gap width; The formula for calculating the gradient correction offset is as follows: ； In the formula, Let be the unit normal vector pointing from the boundary of the driving lane area to the boundary of the emergency lane area. Let the normal vector be the magnitude. As a correction factor; The formula for correcting the emergency lane boundary coordinates based on the gradient correction offset and calculating the corrected emergency lane boundary coordinates is as follows: ； ； In the formula, From the coordinates of two points and Calculated.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the map engine adaptation and conversion method for microscopic traffic simulation data as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the map engine adaptation and conversion method for microscopic traffic simulation data as described in any one of claims 1-4.

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