Vehicle-road cooperation information interaction simulation system and method
By integrating edge computing and multimodal data fusion into the vehicle-road cooperative information interaction simulation system, the limitations of vehicle-road cooperative system performance evaluation are solved, efficient and accurate simulation evaluation and multi-dimensional analysis are achieved, and the simulation and optimization of complex traffic scenarios are supported.
Patent Information
- Application Number
- CN202510854237.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-24
- Publication Date
- 2025-09-23
AI Technical Summary
When evaluating the performance of vehicle-road cooperative systems, existing technologies have high field testing costs, long cycles, and are greatly affected by environmental factors. It is difficult to fully cover all scenarios and conditions, resulting in limited accuracy and comprehensiveness of the evaluation results.
A vehicle-road cooperative information interaction simulation system was developed, including a console module, an edge computing acceleration unit, an OPNET data exchange sub-module, and a simulation result analysis module. Through FPGA chips, dynamic adaptive sampling frequency adjustment, multimodal data fusion, reinforcement learning routing algorithm, and digital twin mapping were implemented. It supports real-time data acquisition, processing, and protocol configuration, and combines blockchain evidence storage to achieve efficient and accurate simulation evaluation.
It achieves efficient and accurate simulation of vehicle-road collaborative information interaction, reduces end-to-end latency, improves simulation efficiency and flexibility, provides multi-dimensional analysis reports, and ensures the traceability and reliability of the simulation process.
Smart Images
Figure CN120692175A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of intelligent transportation technology, and specifically relates to a vehicle-road collaborative information interaction simulation system and method. Background Art
[0002] With the rapid advancement of urbanization worldwide, cities are transforming with each passing day. However, the resulting traffic problems are becoming increasingly prominent, becoming a major challenge hindering sustainable urban development. Traffic congestion not only increases travel times but also exacerbates air pollution and energy consumption. Frequent traffic accidents pose a serious threat to people's lives and property. Faced with this complex and pressing set of challenges, the development of intelligent transportation technology is seen as a key solution.
[0003] Vehicle-Infrastructure Collaboration (V2X), a shining star in the intelligent transportation technology ecosystem, opens up new avenues for improving the overall safety, efficiency, and energy conservation and emissions reduction capabilities of the road traffic system by enabling real-time, efficient, and accurate information exchange between vehicles and road infrastructure. Leveraging advanced communications, the Internet of Things, and big data analytics, this technology enables vehicles to access critical data such as road conditions, traffic signal status, and forward obstacle warnings in real time, enabling them to make smarter and safer driving decisions. Furthermore, road infrastructure can dynamically adjust signal timing and issue traffic guidance information based on the vehicle's real-time location and driving needs, further optimizing traffic flow.
[0004] However, despite the promising prospects of vehicle-infrastructure collaboration technology, comprehensive performance evaluation and optimization are essential before actual deployment. This not only affects the maturity and reliability of the technology but also directly impacts the actual operational effectiveness and socioeconomic benefits of future intelligent transportation systems. Traditional performance evaluation methods, such as field testing, while able to intuitively reflect the actual application effects of the technology, are limited by high costs, long cycles, and significant environmental influences. Especially in complex urban traffic environments, field testing often fails to fully cover all possible scenarios and conditions, limiting the accuracy and comprehensiveness of the evaluation results.
[0005] Therefore, it is particularly important to develop a simulation system and method that can simulate information interaction in a vehicle-road collaborative environment. Summary of the Invention
[0006] The purpose of the present invention is to provide a vehicle-road collaborative information interaction simulation system and method to solve the problems raised in the above background technology.
[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a vehicle-road cooperative information interaction simulation system, the system comprising a console module and a vehicle-road cooperative information interaction network simulation module; The console module includes an external data receiving submodule, an OPNET data exchange submodule, an edge computing acceleration unit, and a data information display submodule; The edge computing acceleration unit uses an FPGA chip to implement parallel processing of data streams, enabling the external data receiving submodule to dynamically and adaptively adjust the sampling frequency, automatically optimizing the data collection granularity based on the real-time traffic flow density (e.g., automatically increasing the sampling frequency to 200Hz when the vehicle density is >80 vehicles / km). The external data receiving submodule is connected to the external vehicle-road cooperative traffic simulation system through the HLA / RTI interface to realize the collection of real-time vehicle-road cooperative traffic simulation data. The OPNET data exchange submodule uses the HLA_interface interface provided by OPNET to complete the generation of user simulation data packets and the generation and transmission of control data for the vehicle-road cooperative information interaction network simulation module. The OPNET data exchange submodule integrates an intelligent routing algorithm based on reinforcement learning, dynamically selects the optimal data transmission path according to the network load status, and reduces the end-to-end delay by more than 30%. The data information display submodule provides an intuitive visual interface for displaying external data reception and OPNET simulation control information. The vehicle-road cooperative information interaction network simulation module consists of a data conversion module, a digital twin mapping submodule, a network and protocol configuration module, and a simulation result analysis module; The digital twin mapping submodule establishes a two-way real-time mapping between the physical road network and the virtual simulation environment through the digital twin engine, supporting the simultaneous injection of sudden traffic events (such as traffic accidents and temporary control) in the real world into the simulation system; The data conversion module serves as a simulation data adapter, responsible for converting the data collected by the console module into a data format recognizable by OPNET; the network and protocol configuration module is responsible for configuring the network topology structure of the mobile vehicle nodes in the vehicle-road collaborative simulation road network, and realizing the configuration of the vehicle-road collaborative information interaction protocol by modifying the manet_station node model; the simulation result analysis module deeply processes and analyzes the data generated during the simulation process to comprehensively evaluate the performance indicators of the vehicle-road collaborative information interaction protocol.
[0008] Preferably, the external data receiving submodule adopts a multimodal data fusion engine, supports protocol parsing and conversion of the V2X communication protocol stack (including DSRC, C-V2X, and LTE-V2X), and has a built-in traffic flow prediction model based on the Transformer architecture, which can perform real-time interpolation and completion of missing data; The external data receiving submodule of the console module has powerful data preprocessing capabilities, and can conduct efficient and accurate data interaction with the external vehicle-road cooperative traffic simulation system in accordance with the preset data format and protocol, ensuring the real-time and accuracy of the data; at the same time, the submodule also supports the access of multiple data sources to adapt to traffic simulation scenarios of different complexities; it also includes an adaptive data cleaning mechanism, which realizes the quality assessment of multi-source data through a lightweight federated learning framework, automatically filters noise data (filtering is triggered when the signal-to-noise ratio is <5dB), and supports data version rollback coordination with the traffic cloud control platform.
[0009] Preferably, the data conversion module of the vehicle-infrastructure cooperative information interaction network simulation module not only has a data format conversion function, but also has a built-in data verification mechanism to strictly verify the data during the data conversion process to ensure data integrity and correctness. In addition, the module also supports data compression and decompression functions to improve the efficiency of data transmission and storage. The data conversion module integrates dynamic protocol adaptation middleware, adopts the SDN (software-defined network) architecture to achieve protocol-independent data encapsulation, and supports automatic recognition of more than 10 mainstream vehicle-infrastructure cooperative protocols (such as ETSI ITS-G5 and IEEE 1609.x series). It also includes a lightweight blockchain-based data storage submodule that hashes key simulation parameters (such as vehicle trajectory and traffic light status) and uploads them to the chain to ensure the traceability of the simulation process. The data compression module adopts an improved LZ4-Huffman hybrid algorithm and optimizes the dictionary construction strategy based on the characteristics of vehicle network time series data, achieving an average compression ratio of 4.2:1.
[0010] Preferably, the method comprises the following steps: Step 1: The console module's external data receiving submodule comprehensively collects external V2X traffic simulation data, including vehicle status information, road condition information, and traffic flow information. It then uses the isolation forest algorithm to identify abnormal traffic data in real time (such as vehicle trajectories with speed changes exceeding 30 km / h) and triggers the simulation system's sandbox isolation environment for verification. Step 2: Use the OPNET data exchange submodule of the console module to efficiently and accurately transmit the collected data to the vehicle-road cooperative information interaction network simulation module; Step 3: In the vehicle-road cooperative information interaction network simulation module, the data conversion module first converts the received data into a data format recognizable by OPNET. Then, the data is verified to ensure its integrity and correctness. This data verification process uses a dual-mode redundancy check mechanism, combined with CRC32 cyclic redundancy check and Reed-Solomon error correction code, to achieve 99.999% data integrity assurance. Step 4: Use the network and protocol configuration module to flexibly configure the network topology of mobile vehicle nodes in the V2I simulation network and the V2I information exchange protocol to adapt to different traffic scenarios and needs. This module incorporates intent-driven networking (IDN) technology, supports configuring complex scenarios (such as 'configuring V2I communication priority in heavy rain') through natural language commands, and automatically generates the corresponding MANET routing protocol parameters. Step 5: Start the simulation process to accurately simulate the real-time information exchange between the vehicle and road infrastructure, including data transmission, reception, and processing, and record key data for subsequent analysis. The simulation process also includes a hardware-in-the-loop (HIL) test interface, which uses the dSPACE real-time simulation system to achieve closed-loop interaction with the actual on-board terminal (OBU), supporting collaborative control verification with millisecond-level latency.
[0011] Step 6: The simulation result analysis module deeply processes and analyzes the data generated during the simulation process to comprehensively evaluate the performance indicators of the vehicle-road cooperative information interaction protocol. At the same time, it provides a visual display function for the simulation results so that users can more intuitively understand the simulation effect. The performance indicator evaluation includes the digital twin quality indicator (DQI), which constructs an evaluation system from three dimensions: spatiotemporal consistency, behavioral realism, and protocol compliance, and outputs a multi-dimensional analysis report including heat maps, timing diagrams, and 3D scene playback.
[0012] Preferably, in step one, the external data receiving submodule conducts real-time and efficient data interaction with the external vehicle-road cooperative traffic simulation system through the HLA / RTI interface to ensure the real-time and accuracy of the data; at the same time, the submodule also supports preprocessing and filtering of the collected data to improve data quality; the abnormal traffic data detection adopts a federated learning architecture, and each traffic simulation node trains a lightweight LSTM anomaly detection model locally, only exchanging model gradient parameters, to achieve global abnormal pattern recognition while ensuring data privacy.
[0013] Preferably, in step three, the data conversion module is not only responsible for the conversion of the data format, but also has a built-in data verification mechanism to strictly verify the data during the data conversion process to ensure the integrity and correctness of the data; in addition, the module also supports encryption processing of the converted data according to actual needs to ensure the security of data transmission.
[0014] Preferably, in step four, the network and protocol configuration module has a dynamic adjustment function, which can adjust the configuration parameters of the network topology and the vehicle-road cooperative information interaction protocol in real time according to the changes in the network topology and the changes in the vehicle-road cooperative information interaction requirements that occur during the simulation process, so as to ensure the accuracy and reliability of the simulation.
[0015] Preferably, in step five, the simulation process includes not only the simulation of vehicle driving behavior (such as following behavior, lane changing behavior, etc.), but also the simulation of traffic signal control and the simulation of road infrastructure status; by comprehensively considering multiple factors, a more realistic and comprehensive traffic simulation is achieved.
[0016] Preferably, in step six, the simulation result analysis module not only provides data processing and analysis functions for the simulation results, but also supports visual display of the simulation results in various forms such as charts and animations; at the same time, the module also supports comparative analysis of the simulation results with preset performance indicators to evaluate the actual performance of the vehicle-road collaborative information interaction protocol.
[0017] Preferably, the method also includes step seven: optimizing and improving the vehicle-road cooperative information interaction protocol based on the evaluation results of the simulation result analysis module; then re-simulating and verifying whether the performance of the optimized protocol is improved; and continuously iterating the optimization and improvement process until the optimal performance is achieved.
[0018] The beneficial effects of the present invention are as follows: 1. This invention implements protocol parsing, data cleaning, and real-time interpolation and completion functions for the V2X communication protocol stack by integrating a multimodal data fusion engine and a traffic flow prediction model based on the Transformer architecture. The external data receiving submodule uses an edge computing acceleration unit driven by an FPGA chip, supports dynamic adaptive sampling frequency adjustment, and can automatically optimize the data collection granularity according to the real-time traffic flow density to ensure the real-time and accuracy of data collection. At the same time, combined with the abnormal data detection mechanism of the isolation forest algorithm, the system can identify and isolate abnormal traffic data in real time to avoid contaminating the simulation environment, providing a high-quality data foundation for subsequent simulation analysis.
[0019] 2. The present invention significantly improves the simulation efficiency and flexibility of the vehicle-road cooperative information interaction network by integrating an intelligent routing algorithm based on reinforcement learning and a dynamic protocol adaptation middleware. The OPNET data exchange submodule can dynamically select the optimal data transmission path according to the network load status, reducing the end-to-end delay by more than 30%; the data conversion module has a built-in dual-mode redundancy check mechanism (CRC32+Reed-Solomon) and an improved LZ4-Huffman hybrid compression algorithm, which ensures data integrity while achieving an average compression ratio of 4.2:1. In addition, the system supports automatic identification of more than 10 mainstream vehicle-road cooperative protocols, and implements protocol-independent data encapsulation through the SDN architecture, which greatly reduces the configuration complexity in multi-protocol scenarios.
[0020] 3. The present invention realizes the two-way real-time mapping between the physical road network and the virtual simulation environment by constructing a digital twin mapping engine and a hardware-in-the-loop (HIL) test interface, and supports the synchronous injection of sudden traffic events in the real world into the simulation system. The simulation process introduces intent-driven network (IDN) technology, and users can configure complex scenarios through natural language instructions, and the system automatically generates the corresponding MANET routing protocol parameters. The simulation result analysis module constructs a digital twin quality index (DQI) evaluation system from three dimensions: spatiotemporal consistency, behavioral authenticity, and protocol compliance. It outputs a multi-dimensional analysis report including heat maps, timing diagrams, and 3D scene playback, and intuitively displays the simulation effect through a visual interface. Combined with the lightweight data storage sub-module of the blockchain, the system can hash key simulation parameters on the chain to ensure the traceability of the simulation process, providing reliable support for the research and development and verification of vehicle-road collaborative technology. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 This is a diagram of the vehicle-road cooperative information interaction simulation system of the present invention; Figure 2 This is a flow chart of the vehicle-road collaborative information interaction simulation method of the present invention. DETAILED DESCRIPTION
[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0023] like Figures 1 to 2 As shown, an embodiment of the present invention provides a vehicle-road cooperative information interaction simulation system, which mainly includes the following modules: Console Module: External data receiving submodule: Real-time collection of external vehicle-road collaborative traffic simulation data is achieved through the HLA / RTI (High-Level Architecture / Runtime Infrastructure) interface, including vehicle status (such as speed, position, direction, etc.), road conditions (such as number of lanes, traffic signal status, etc.), traffic flow and other information.
[0024] OPNET data exchange submodule: Utilizes OPNET's built-in HLA_interface interface to complete the generation of user simulation data packets and the generation and transmission of control data for the vehicle-road cooperative information interaction network simulation module, ensuring the real-time and accuracy of the data.
[0025] Data information display submodule: provides an intuitive visual interface to realize the visual display of external data reception and OPNET simulation control information, making it easier for users to monitor and analyze the simulation process.
[0026] Vehicle-road cooperative information interaction network simulation module: Data conversion module: As a simulation data adapter, it converts the data collected by the console module into a data format recognizable by OPNET to ensure data compatibility and interoperability.
[0027] Network and protocol configuration module: realizes the configuration of the network topology structure of mobile vehicle nodes in the vehicle-road collaborative simulation road network, including operations such as adding, deleting, and connecting nodes; at the same time, by modifying the manet_station node model, it realizes the configuration of the vehicle-road collaborative information interaction protocol, such as V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle) in the V2X (Vehicle-to-Everything) communication protocol.
[0028] Simulation result analysis module: processes and analyzes the data generated during the simulation process to evaluate the performance indicators of the vehicle-road cooperative information interaction protocol, such as communication delay, packet loss rate, system stability, traffic flow changes, road safety improvement, etc.
[0029] In addition, the system also includes a traffic flow generation module, a vehicle behavior simulation module, etc., which are used to generate diverse traffic scenarios and simulate vehicle driving behaviors.
[0030] Simulation method: Vehicle behavior model Following model: This model uses a combined vehicle low-speed following model based on the intelligent driver model (IDM) and radial basis function neural network (RBFNN), as well as a following model that combines a long short-term memory neural network (LSTM) data-driven model constructed using an adaptive Kalman filter algorithm with an IDM theory-driven model to simulate the vehicle's driving behavior in the following situation, including operations such as acceleration, deceleration, and maintaining distance.
[0031] Lane-changing model: Utilizes lane-changing models such as the Gipps model or the MITSIM model to simulate the lane-changing behavior of vehicles on multi-lane roads, taking into account the generation of lane-changing intentions, the detection of lane-changing conditions, and the implementation of lane-changing actions.
[0032] Trajectory smoothing: A polynomial smoothing algorithm is used to smooth the vehicle trajectory, reducing deviations caused by coordinate jitter and offset, and improving trajectory accuracy and reliability.
[0033] Communication protocol simulation: Communication protocol selection: Based on the actual needs of the intelligent traffic vehicle-road cooperative system, select appropriate communication protocols, such as TCP / IP, UDP, etc., and simulate the data transmission process in different network environments, including wired networks, wireless networks (such as Wi-Fi, cellular networks, etc.), and hybrid networks.
[0034] Data transmission simulation: simulates the sending, receiving, and retransmission processes of data packets, taking into account the impact of factors such as network delay and packet loss rate on data transmission.
[0035] Data encryption and decryption: Use advanced encryption algorithms to encrypt transmitted data to ensure the confidentiality of data during transmission; at the same time, simulate the data decryption process to verify the effectiveness and reliability of the encryption algorithm.
[0036] Traffic flow model: Traffic flow generation: Using open-source traffic simulation software such as SUMO (Simulation of Urban Mobility), we import real-world traffic flow data and combine it with a random generation algorithm to generate a variety of traffic scenarios, including morning and evening rush hours and special weather conditions.
[0037] Lane selection and lane changing: A car-following model and a lane-changing model are used to simulate vehicle behavior in different lanes, taking into account factors such as lane selection strategy, the generation of lane-changing intentions, and the detection of lane-changing conditions.
[0038] Traffic signal control: Simulate traffic signal control strategies, including fixed timing control and adaptive control, and evaluate the impact of different control strategies on traffic flow.
[0039] Multi-scenario analysis: Scenario construction: Construct a variety of virtual traffic scenarios based on different traffic conditions and constraints (such as traffic volume, road conditions, weather conditions, etc.).
[0040] Simulation execution: In each scenario, the simulation process is started to simulate the real-time information interaction between vehicles and road infrastructure, and key data (such as vehicle driving trajectory, communication delay time, traffic density, etc.) is recorded.
[0041] Result evaluation: The simulation result analysis module is used to process and analyze the data generated during the simulation process to evaluate the effects of different vehicle-road collaboration strategies, including traffic flow management effects, road safety improvements, and traffic efficiency improvements.
[0042] Implementation Method Step 1: System Construction Choose a suitable programming language and database (such as Python and MongoDB) to build the basic framework of the system.
[0043] According to the system composition and simulation method, the functions of each module are designed and implemented, including the console module, vehicle-road cooperative information interaction network simulation module, traffic flow generation module, vehicle behavior simulation module, etc.
[0044] Step 2: Data collection and processing The external data receiving submodule is used to collect real-time vehicle-road cooperative traffic simulation data, including vehicle status, road conditions, traffic flow and other information.
[0045] Preprocess the collected data, including data cleaning, denoising, normalization and other operations, to improve the quality and reliability of the data.
[0046] The preprocessed data is converted into a data format recognizable by OPNET through the data conversion module to provide data support for the subsequent simulation process.
[0047] Step 3: Simulation process Configure the network topology structure of mobile vehicle nodes and the vehicle-road cooperative information interaction protocol in the network and protocol configuration module.
[0048] Start the traffic flow generation module to generate diverse traffic scenarios.
[0049] Start the vehicle behavior simulation module to simulate the vehicle's driving behavior in different scenarios.
[0050] Start the simulation process to simulate the real-time information interaction between vehicles and road infrastructure and record key data.
[0051] Step 4: Result Analysis and Optimization The simulation result analysis module is used to process and analyze the data generated during the simulation process to evaluate the effectiveness of different vehicle-road collaboration strategies.
[0052] Based on the analysis results, bottlenecks and problem points are identified and optimization suggestions are put forward, such as adjusting traffic signal control strategies and optimizing vehicle driving paths.
[0053] Optimize and improve existing problems, and re-simulate and verify until the best solution is reached.
[0054] Step 5: Application and promotion Applying this system in actual traffic scenarios will provide strong support for traffic flow management, road safety improvement and traffic efficiency improvement.
[0055] Cooperate with relevant industries and institutions to promote the application of this system and promote the development and progress of intelligent transportation technology.
[0056] Continuously collect user feedback, continuously improve and optimize the system, and enhance the system's applicability and reliability.
[0057] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.
[0058] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A vehicle-road cooperative information interaction simulation system, characterized by: The system includes a console module and a vehicle-road cooperative information interaction network simulation module; The console module includes an external data receiving submodule, an OPNET data exchange submodule, an edge computing acceleration unit, and a data information display submodule; The edge computing acceleration unit uses FPGA chips to implement parallel processing of data streams, enabling the external data receiving submodule to have a dynamic adaptive sampling frequency adjustment function and automatically optimize the data collection granularity according to the real-time traffic flow density; The external data receiving submodule connects to the external V2X traffic simulation system via the HLA / RTI interface to collect real-time V2X traffic simulation data. The OPNET data exchange submodule utilizes OPNET's built-in HLA_interface to generate user simulation data packets and control data for the V2X information interaction network simulation module. The OPNET data exchange submodule integrates an intelligent routing algorithm based on reinforcement learning to dynamically select the optimal data transmission path based on network load status, reducing end-to-end latency by over 30%. The data information display submodule provides an intuitive visual interface for displaying external data reception and OPNET simulation control information; The vehicle-road cooperative information interaction network simulation module consists of a data conversion module, a digital twin mapping submodule, a network and protocol configuration module, and a simulation result analysis module; The digital twin mapping submodule establishes a two-way real-time mapping between the physical road network and the virtual simulation environment through the digital twin engine, supporting the simultaneous injection of sudden traffic events in the real world into the simulation system; The data conversion module acts as a simulation data adapter and is responsible for converting the data collected by the console module into a data format that can be recognized by OPNET; The network and protocol configuration module is responsible for configuring the network topology of mobile vehicle nodes in the vehicle-road cooperative simulation network, and implementing the configuration of the vehicle-road cooperative information interaction protocol by modifying the manet_station node model; The simulation result analysis module deeply processes and analyzes the data generated during the simulation process to comprehensively evaluate the performance indicators of the vehicle-road collaborative information interaction protocol.
2. The vehicle-road cooperative information interaction simulation system according to claim 1, characterized in that: The external data receiving submodule uses a multimodal data fusion engine, supports protocol parsing and conversion of the V2X communication protocol stack, and has a built-in traffic flow prediction model based on the Transformer architecture, which can perform real-time interpolation and completion of missing data. The external data receiving submodule of the console module has powerful data preprocessing capabilities, and can conduct efficient and accurate data interaction with the external vehicle-road cooperative traffic simulation system in accordance with the preset data format and protocol, ensuring the real-time and accuracy of the data; at the same time, the submodule also supports the access of multiple data sources to adapt to traffic simulation scenarios of different complexities; it also includes an adaptive data cleaning mechanism, which realizes the quality assessment of multi-source data through a lightweight federated learning framework, automatically filters noise data, and supports data version rollback coordination with the traffic cloud control platform.
3. The vehicle-road cooperative information interaction simulation system according to claim 1, characterized in that: The data conversion module of the vehicle-road cooperative information interaction network simulation module not only has the data format conversion function, but also has a built-in data verification mechanism to strictly verify the data during the data conversion process to ensure the integrity and correctness of the data; in addition, the module also supports data compression and decompression functions to improve the efficiency of data transmission and storage; the data conversion module integrates dynamic protocol adaptation middleware, adopts SDN architecture to achieve protocol-independent data encapsulation, and supports automatic identification of more than 10 mainstream vehicle-road cooperative protocols; It also includes a lightweight data evidence submodule based on blockchain, which hashes key simulation parameters on the chain to ensure the traceability of the simulation process. The data compression module adopts an improved LZ4-Huffman hybrid algorithm and optimizes the dictionary construction strategy based on the characteristics of vehicle network time series data, achieving an average compression ratio of 4.2:
1.
4. A vehicle-road collaborative information interaction simulation method, characterized by: The method comprises the following steps: Step 1: The console module's external data receiving submodule comprehensively collects external V2X traffic simulation data, including vehicle status information, road condition information, and traffic flow information. It uses the isolation forest algorithm to identify abnormal traffic data in real time and triggers the simulation system's sandbox isolation environment for verification. Step 2: Use the OPNET data exchange submodule of the console module to efficiently and accurately transmit the collected data to the vehicle-road cooperative information interaction network simulation module; Step 3: In the vehicle-road cooperative information interaction network simulation module, the data conversion module first converts the received data into a data format recognizable by OPNET. Then, the data is verified to ensure its integrity and correctness. This data verification process uses a dual-mode redundancy check mechanism, combined with CRC32 cyclic redundancy check and Reed-Solomon error correction code, to achieve 99.999% data integrity assurance. Step 4: Use the network and protocol configuration module to flexibly configure the network topology of mobile vehicle nodes in the V2X simulation network and the V2X information interaction protocol to adapt to different traffic scenarios and needs. The network and protocol configuration module introduces intent-driven networking (IDN) technology, supports configuring complex scenarios through natural language commands, and automatically generates corresponding MANET routing protocol parameters. Step 5: Start the simulation process to accurately simulate the real-time information exchange between the vehicle and road infrastructure, including data transmission, reception, and processing, and record key data for subsequent analysis. The simulation process also includes a hardware-in-the-loop (HIL) test interface, which uses the dSPACE real-time simulation system to achieve closed-loop interaction with the actual on-board terminal (OBU), supporting collaborative control verification with millisecond-level latency. Step 6: The simulation result analysis module deeply processes and analyzes the data generated during the simulation process to comprehensively evaluate the performance indicators of the vehicle-road cooperative information interaction protocol. At the same time, it provides a visual display function for the simulation results so that users can more intuitively understand the simulation effect. The performance indicator evaluation includes the digital twin quality indicator (DQI), which constructs an evaluation system from three dimensions: spatiotemporal consistency, behavioral realism, and protocol compliance, and outputs a multi-dimensional analysis report including heat maps, timing diagrams, and 3D scene playback.
5. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: In step one, the external data receiving submodule conducts real-time and efficient data interaction with the external vehicle-road cooperative traffic simulation system through the HLA / RTI interface to ensure the real-time and accuracy of the data; at the same time, the submodule also supports preprocessing and filtering of the collected data to improve data quality; the abnormal traffic data detection adopts a federated learning architecture, and each traffic simulation node locally trains a lightweight LSTM anomaly detection model and only exchanges model gradient parameters to achieve global abnormal pattern recognition while ensuring data privacy.
6. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: In step three, the data conversion module is not only responsible for the conversion of the data format, but also has a built-in data verification mechanism to strictly verify the data during the data conversion process to ensure the integrity and correctness of the data; in addition, the module also supports encryption of the converted data according to actual needs to ensure the security of data transmission.
7. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: In step four, the network and protocol configuration module has a dynamic adjustment function, which can adjust the configuration parameters of the network topology and vehicle-road cooperative information interaction protocol in real time according to the changes in the network topology and the changes in the vehicle-road cooperative information interaction requirements that occur during the simulation process to ensure the accuracy and reliability of the simulation.
8. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: In step five, the simulation process includes not only the simulation of vehicle driving behavior, but also the simulation of traffic signal control and the simulation of road infrastructure status; by comprehensively considering multiple factors, a more realistic and comprehensive traffic simulation is achieved.
9. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: In step six, the simulation result analysis module not only provides data processing and analysis functions for the simulation results, but also supports visual display of the simulation results in various forms; at the same time, the module also supports comparative analysis of the simulation results with preset performance indicators to evaluate the actual performance of the vehicle-road cooperative information interaction protocol.
10. The vehicle-road cooperative information interaction simulation method according to claim 4, characterized in that: The method further includes step seven: optimizing and improving the vehicle-road cooperative information interaction protocol based on the evaluation results of the simulation result analysis module; and then re-simulating and verifying whether the performance of the optimized protocol is improved; Through continuous iterative optimization and improvement process until the best performance is achieved.