Local traffic flow real-time interactive generation method for driving simulation

Abstract

The application relates to a local traffic flow real-time interaction generation method for driving simulation, which comprises the following steps: S1, using a UAV sensor to adopt road traffic flow information, and extracting vehicle information in a shot road section range; S2, recording detailed road information; S3, establishing a complete road model in driving simulation software according to road linear data and road information management files; S4, designing or configuring a traffic flow generator in the driving simulation software; S5, establishing a vehicle model, and providing a car following / lane changing model and a trajectory planning model for the traffic flow generated vehicle; S6, adding specific global coordinates or stake numbers in the road editing tool; and S7, realizing real-time interaction through a driving database, a road information management file and a driving simulation traffic flow generator. The application can significantly improve the technical effect of a driving simulator, make driving simulation more real and accurate, and provide more valuable tools and data for traffic management research and decision-making.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and more specifically, to a method for real-time interactive generation of local traffic flow for driving simulation. Background Technology

[0002] Researching real-time interactive traffic flow generation methods has profound significance. It can not only improve the realism of driving simulations, providing drivers with a more realistic training environment, but also help traffic engineers and urban planners optimize traffic system design and predict and respond to various traffic flow changes. Furthermore, this method plays a crucial role in the development of autonomous driving technology, providing richer and more complex training and testing environments for autonomous driving systems. Finally, by simulating and studying different traffic flow conditions, we can better understand the causes of traffic accidents and identify effective measures to improve road traffic safety. In summary, researching real-time interactive traffic flow generation methods is of great value for improving quality of life and social development.

[0003] Traditional real-time global traffic flow generation has several significant drawbacks. First, it is highly complex, involving a wider road network and more intersections, leading to increased model and algorithm complexity. Computational costs also rise accordingly, potentially requiring more computing resources and hardware support. Second, global models have a more urgent need for large amounts of real-time data, potentially involving more sensors and data acquisition devices, further increasing system complexity and cost. Third, real-time performance presents numerous challenges, as traffic conditions can change drastically in a short period. Data consistency, privacy, and security issues also pose challenges, and user acceptance may be affected by public concerns about privacy and surveillance. Finally, global traffic flow systems lack flexibility and struggle to adapt to real-time changes in specific areas. Based on the demands for real-time performance and flexibility, local real-time traffic flow generation technology has attracted considerable attention. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a real-time interactive generation method for local traffic flow in driving simulation. Compared with traditional global traffic flow, it improves the accuracy, efficiency, flexibility and real-time performance of driving simulation, making it more realistic and accurate, and providing more valuable tools and data for traffic management research and decision-making.

[0005] The technical solution adopted by this invention to solve its technical problem is: to construct a real-time interactive generation method for local traffic flow in driving simulation, comprising the following steps:

[0006] S1. Using drone sensors to obtain road traffic flow information, the video data captured by the drone is converted into structured driving data through multi-source data fusion, and vehicle information within the captured road segment is extracted.

[0007] S2. Establish different driving databases based on different road segments, and create road information management files based on real roads to record detailed road information;

[0008] S3. Based on the road linearity data and road information management files, establish a complete road model in the driving simulation software, while ensuring that the changes in the number of lanes, lane lengths, and the accurate locations of abrupt changes in the model are consistent with the road information management files.

[0009] S4. Design or configure a traffic flow generator in driving simulation software to generate traffic flow based on road information management documents and driving databases;

[0010] S5. Establish a vehicle model and equip the vehicles generated by traffic flow with a car-following / lane-changing model and a trajectory planning model. During segment transitions, the trajectory planning model is used to achieve smooth vehicle replacement.

[0011] S6. In the driving simulation software, based on the created road network, add specific global coordinates or station numbers to it using the road editing tool. Define the traffic flow generation range based on the road station numbers and the real-time coordinates of the vehicle, and set the size and shape of the traffic flow generation area.

[0012] S7. Through real-time interaction with the vehicle database, road information management files, and driving simulation traffic flow generator, vehicles can be generated / deleted in real time within a specified range.

[0013] According to the above scheme, in step S1, extracting vehicle information within the shooting section specifically involves: selecting nodes on the road according to road attributes, using drones of the same model for aerial photography, using drone flight planning software to set the flight path and altitude, shooting high-definition video, preprocessing the obtained video to remove noise, filling missing values ​​through data cleaning and data interpolation, removing irrelevant or unclear parts, performing image processing analysis, using image processing software or algorithms to identify and track vehicles on the road, and finally using a programming language to write a script to convert the extracted data into structured driving data.

[0014] According to the above scheme, the vehicle information extracted within the shooting section includes location, vehicle length, vehicle width, vehicle speed, and acceleration.

[0015] According to the above scheme, in step S2, the data is classified according to road attributes, geographical location, and traffic density factors, and a driving database table and a road information management file are designed. The driving database includes vehicle ID, coordinates, speed, acceleration, and following vehicles. The road information management file includes road information such as the number of road lanes, segment length, lane width, ramps, and change point locations.

[0016] According to the above scheme, in step S4, designing or configuring the traffic flow generator in the driving simulation software specifically involves: using programming tools to write a traffic flow control plugin for the driving simulation software; firstly, importing the vehicle database and road information management file into the workspace; writing a function to query the current road in real time; executing an SQL query; querying the connection between the vehicle database and the road information management file; and returning traffic data based on the provided road information; then setting a function to call the traffic flow data; and generating traffic flow on the road in real time through the traffic flow generator of the driving simulation software, while continuously calling the segment vehicle database. Before calling, the optimal segment matching is performed according to the vehicle status at the end of the old segment to ensure that the vehicles in the old segment replace the vehicles in the new segment in a shorter time, ensuring that the traffic flow can transition smoothly and naturally at abrupt changes.

[0017] According to the above scheme, in step S5, the vehicles generating traffic flow are equipped with a car-following / lane-changing model and a trajectory planning model. Specifically, the car-following / lane-changing model is integrated to control the behavior of the vehicles. The real-time coordinates, speed, acceleration, and turning angle of the simulated vehicles are transmitted to the car-following / lane-changing model for coordinate back-calculation and position correction. The speed, acceleration, and position are updated in real time. The new speed is calculated based on the car-following model, vehicle spacing, and speed difference. The lane is updated based on the lane-changing decision. Based on trajectory planning, the driving information in the driving database is reread and applied after the simulated vehicles are disturbed. Under real data, it is ensured that the vehicles in the surrounding real traffic flow react to the behavior of the simulated vehicles to maintain a safe distance and coordination with the simulated driving vehicles.

[0018] According to the above scheme, the vehicle model established includes simulated driving vehicles and traffic flow vehicles.

[0019] According to the above scheme, in step S7, the local generation logic is determined in the traffic flow control plugin of the driving simulation software written in step S4, the position of the simulated vehicle is monitored in real time, and other vehicles are generated or deleted based on this position, so that the vehicles are only generated within a range of 1000m in front of and 500m behind the simulated vehicle. At the same time, dynamic adjustments are made so that when the simulated vehicle moves, the range of generated vehicles also moves accordingly.

[0020] The method for real-time interactive generation of local traffic flow for driving simulation according to the present invention has the following beneficial effects:

[0021] 1. This invention can realize real-time interactive generation of local traffic flow in a driving simulation environment. It can smoothly call up the collected real data and freely define the generation range. In addition, the generated vehicles are equipped with corresponding intelligent control models, which improves the realism and lifelikeness of driving simulation.

[0022] 2. In this invention, users can customize traffic scenarios according to specific needs, and adjust traffic flow density, vehicle type, road conditions, etc., to meet different training objectives or research needs, greatly improving the flexibility and customizability of the driving simulator.

[0023] 3. The traffic flow generated by this invention can be selected globally or locally, which improves the efficiency and flexibility of driving simulation. It can also set more realistic scenarios to better study driving behavior and help develop intelligent connected systems, thus improving the innovation and forward-looking nature of driving simulation.

[0024] 4. Through traffic flow control, this invention allows some intelligent connected systems and products to be tested and simulated in driving simulators. This not only allows drivers to experience future intelligent connected vehicles and smart transportation systems, but also reduces the experimental costs of new intelligent products.

[0025] 5. This invention helps promote the development and application of driving simulation, and provides strong support for research and education on intelligent vehicles and smart transportation. Attached Figure Description

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0027] Figure 1 This is a data acquisition diagram of the real-time interactive generation method for local traffic flow in driving simulation according to the present invention;

[0028] Figure 2 This is a schematic diagram of the vehicle database file for the real-time interactive generation method of local traffic flow for driving simulation according to the present invention.

[0029] Figure 3 This is a schematic diagram of the road information management file of the real-time interactive generation method for local traffic flow in driving simulation according to the present invention;

[0030] Figure 4 This is the first real-time interactive graph of the local traffic flow real-time interactive generation method for driving simulation of the present invention;

[0031] Figure 5 This is the second real-time interactive graph of the local traffic flow real-time interactive generation method for driving simulation of the present invention;

[0032] Figure 6 This is a transition flowchart of the first trajectory planning segment of the real-time interactive generation method for local traffic flow in driving simulation of the present invention.

[0033] Figure 7 This is a transition flowchart of the second trajectory planning segment of the real-time interactive generation method for local traffic flow in driving simulation of the present invention;

[0034] Figure 8 This is a flowchart of the method for real-time interactive generation of local traffic flow for driving simulation according to the present invention. Detailed Implementation

[0035] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] like Figure 1-8 As shown, the real-time interactive generation method for local traffic flow in driving simulation according to the present invention includes the following steps:

[0037] S1. Using sensors such as drones to collect road traffic flow information, the video data captured by the drones is converted into structured driving data through multi-source data fusion, and vehicle information within the captured road segment is extracted. The vehicle information includes location, vehicle length, vehicle width, vehicle speed, acceleration, etc.

[0038] Specifically, the process involves selecting nodes on the road according to its attributes, including each road scenario and lane change point. Aerial photography is conducted using drones of the same model, ensuring coverage of the required monitored road area. Drone flight planning software is used to set the flight path and altitude, capturing high-definition video to ensure clear visibility of road traffic flow. The obtained video is preprocessed to remove noise, and data cleaning, data interpolation to fill missing values, and removal of irrelevant or unclear parts are performed before image processing and analysis. Image processing software or algorithms (such as OpenCV) are used to identify and track vehicles on the road. Finally, a script is written using a programming language (such as Python) to convert the extracted data into structured driving data. The collected information includes... Figure 1 .

[0039] S2. Establish different driving databases for different road segments, including vehicle ID, coordinates, speed, acceleration, following vehicles, etc., and establish road information management files based on real roads to record detailed road information, especially changes in the number of lanes and the location of abrupt change points, including road information such as the number of road lanes, segment length, lane width, ramps, and abrupt change point locations.

[0040] Specifically, the data is categorized based on factors such as road attributes (mainly the number of lanes), geographical location, and traffic density (low, medium, high). A driving database table and a road information management file are designed. The driving database includes vehicle ID, coordinates, speed, acceleration, and following vehicles. The road information management file provides detailed information, particularly changes in the number of lanes and the location of abrupt changes, including road information such as the number of lanes, segment length, lane width, ramps, and abrupt change locations. Both are designed in the same format, such as using Excel spreadsheets. Figure 2-3 As shown.

[0041] S3. Based on the road linearity data and road information management files, establish a complete road model in the driving simulation software, while ensuring that the changes in the number of lanes, lane lengths, and the accurate locations of abrupt changes in the model are consistent with the road information management files.

[0042] S4. Design or configure a traffic flow generator in driving simulation software to generate traffic flow based on road information management documents and driving databases.

[0043] Specifically, the process involves using programming tools to develop a traffic flow control plugin for driving simulation software. First, import the vehicle database and road information management file into the workspace. Then, write a function that can query the current road in real time, execute an SQL query, connect the vehicle database and road information management file, and return traffic data based on the provided road information. Next, set up a function to call the traffic flow data. The driving simulation software's traffic flow generator then generates traffic flow on the road in real time, continuously calling the segment vehicle database. Before each call, the optimal segment is matched based on the vehicle status at the end of the old segment, ensuring that vehicles from the old segment can replace vehicles in the new segment in a short time, guaranteeing a smooth and natural transition of traffic flow at abrupt changes.

[0044] S5. Establish vehicle models, including simulated driving vehicles and traffic flow vehicles. Equip vehicles generated by traffic flow with car-following / lane-changing models and trajectory planning models. During segment transitions, the trajectory planning model is used to achieve smooth vehicle replacement.

[0045] Specifically, this involves integrating a car-following / lane-changing model to control vehicle behavior. By transmitting the simulated vehicle's real-time coordinates, speed, acceleration, and turning angle to the car-following / lane-changing model, the model performs coordinate back-calculation to correct the vehicle's position and updates speed, acceleration, and position in real time. New speeds are calculated based on the car-following model, vehicle spacing, and speed differences. Lane changes are updated based on lane-changing decisions. This ensures that vehicles in the surrounding real traffic flow can react to the simulated vehicle's behavior, maintaining a safe distance and coordination with the simulated vehicle, achieving the desired effect. Figure 4-5 As shown.

[0046] Simultaneously, a trajectory planning model should be provided for the vehicles. Time points should be marked in each data segment to ensure they correspond to the same actual time. An ID should be assigned to each vehicle in each segment, and its lane and time point status (position, speed, acceleration, turning angle, etc.) should be recorded. For vehicles ending each segment, the corresponding vehicle state at the same time point and in the same lane in the next segment should be determined as the target state. A trajectory planning model should be designed to calculate the path from the current state (the state at the end of the old segment) to the target state (the state at the same time point in the new segment). The trajectory planning should consider time synchronization; that is, the calculated path should allow the vehicle to reach the target state within a specified time (not too long, otherwise the optimal segment should be reselected). The trajectory planning model should be used to calculate the optimal path from the current state to the target state for each vehicle. Simultaneously, the vehicle's driving parameters should be adjusted to match the target state (position, speed, acceleration, turning angle, etc.) when entering a new segment. This ensures that as the vehicle approaches the end of the old segment, it targets vehicles in the same lane of the new segment, reaches a similar state within a short time, and automatically switches to the new segment's data. This ensures that the data switch is not only continuous and natural in behavior but also strictly synchronized in time, achieving the desired effect. Figure 6 As shown.

[0047] S6. In the driving simulation software, based on the created road network, add specific global coordinates or station numbers using the road editing tool. Use the driving simulation software's path planning tool to define the traffic flow generation range based on the road station numbers and the vehicle's real-time coordinates, setting the size and shape of the traffic flow generation area.

[0048] S7. Through real-time interaction with the vehicle database, road information management files, and driving simulation traffic flow generator, vehicles can be generated / deleted in real time within a specified range.

[0049] Specifically, in step S4, the traffic flow control plugin of the driving simulation software determines the local generation logic, monitors the position of the simulated vehicles in real time, and generates or deletes other vehicles based on these positions, ensuring that vehicles are generated only within a certain range (1000m in front and 500m behind) around the simulated vehicle (i.e., the user-controlled vehicle or the main observation object). Simultaneously, dynamic adjustments are made so that when the simulated vehicle moves, the range of generated vehicles also moves accordingly.

[0050] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for real-time interactive generation of local traffic flow for driving simulation, characterized in that, Includes the following steps: S1. Using drone sensors to obtain road traffic flow information, the video data captured by the drone is converted into structured driving data through multi-source data fusion, and vehicle information within the captured road segment is extracted. S2. Establish different driving databases based on different road segments, and create road information management files based on real roads to record detailed road information; S3. Based on the road linearity data and road information management files, establish a complete road model in the driving simulation software, while ensuring that the changes in the number of lanes, lane lengths, and the accurate locations of abrupt changes in the model are consistent with the road information management files. S4. Design or configure a traffic flow generator in driving simulation software to generate traffic flow based on road information management documents and driving databases; S5. Establish a vehicle model and equip the vehicles generated by traffic flow with a car-following / lane-changing model and a trajectory planning model. During segment transitions, the trajectory planning model is used to achieve smooth vehicle replacement. S6. In the driving simulation software, based on the created road network, add specific global coordinates or station numbers to it using the road editing tool. Define the traffic flow generation range based on the road station numbers and the real-time coordinates of the vehicle, and set the size and shape of the traffic flow generation area. S7. Through real-time interaction with the vehicle database, road information management files, and driving simulation traffic flow generator, vehicles can be generated / deleted in real time within a specified range.

2. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 1, characterized in that, In step S1, vehicle information within the shooting section is extracted, specifically as follows: nodes are selected on the road according to road attributes, aerial photography is conducted using drones of the same model, drone flight planning software is used to set the flight path and altitude, high-definition video is captured, the obtained video is preprocessed to remove noise, missing values ​​are filled through data cleaning and data interpolation, irrelevant or unclear parts are removed, and image processing analysis is performed, image processing software or algorithms are used to identify and track vehicles on the road, and finally, a script is written using a programming language to convert the extracted data into structured driving data.

3. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 2, characterized in that, The vehicle information extracted within the shooting section includes location, vehicle length, vehicle width, vehicle speed, and acceleration.

4. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 1, characterized in that, In step S2, the data is classified according to road attributes, geographical location, and traffic density factors, and a driving database table and a road information management file are designed. The driving database includes vehicle ID, coordinates, speed, acceleration, and following vehicles. The road information management file includes road information such as the number of road lanes, segment length, lane width, ramps, and abrupt change point locations.

5. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 1, characterized in that, In step S4, the traffic flow generator in the driving simulation software is designed or configured. Specifically, a traffic flow control plugin for the driving simulation software is written using a programming tool. First, the driving database and road information management file are imported into the workspace. A function is written to query the current road in real time, execute an SQL query, query the connection between the driving database and the road information management file, and return traffic data based on the provided road information. Then, a function is set to call the traffic flow data. The traffic flow generator of the driving simulation software generates traffic flow on the road in real time and continuously calls the segment driving database. Before calling, the optimal segment matching is performed according to the vehicle status at the end of the old segment to ensure that the vehicles in the old segment replace the vehicles in the new segment in a shorter time, ensuring that the traffic flow can transition smoothly and naturally at abrupt changes.

6. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 1, characterized in that, In step S5, the vehicles generating traffic flow are equipped with a car-following / lane-changing model and a trajectory planning model. Specifically, the car-following / lane-changing model of the vehicle is integrated to control the behavior of the vehicle. The real-time coordinates, speed, acceleration, and turning angle of the simulated vehicle are transmitted to the car-following / lane-changing model for coordinate back-calculation and position correction. The speed, acceleration, and position are updated in real time. The new speed is calculated based on the car-following model, vehicle spacing, and speed difference; Based on lane-changing decisions, lanes are updated; based on trajectory planning, driving information from the driving database is reread and applied after simulated vehicle disturbances; under real data, it is ensured that vehicles in the surrounding real traffic flow react to the behavior of simulated vehicles to maintain a safe distance and coordination with the simulated driving vehicle.

7. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 6, characterized in that, The established vehicle model includes simulated driving vehicles and traffic flow vehicles.

8. The method for real-time interactive generation of local traffic flow for driving simulation according to claim 1, characterized in that, In step S7, the local generation logic is determined in the traffic flow control plugin of the driving simulation software written in step S4. The position of the simulated vehicle is monitored in real time, and other vehicles are generated or deleted based on this position, so that the vehicles are only generated within a range of 1000m in front of and 500m behind the simulated vehicle. At the same time, dynamic adjustments are made so that when the simulated vehicle moves, the range of generated vehicles also moves accordingly.

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