Research review on simulation and test of mixed traffic swarm in vehicle-infrastructure cooperative environment
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摘要: 归纳了车路协同及其仿真测试技术的发展历程，并结合典型仿真结果探讨了萌芽期、起步期、发展期阶段下的仿真需求、经典方法与技术瓶颈；在此基础上，提出了基于交通主体建模、群体行为仿真、测试结果分析的3层新型虚实交互仿真测试架构；针对混合交通主体仿真需求构建了异构交通主体模型，解析了混合交通运行机理，以此作为仿真系统底层模型支撑；结合设计的虚实交互仿真测试架构，突破了混合交通群体智能场景生成技术，提出了混合交通群体智能仿真方法；在此基础上，选取交叉口和路段典型交通场景，开展了不同群体智能决策控制方法的仿真试验，以验证所提方法的效能；最后，总结了车路协同的未来发展方向和相关建议。研究结果表明：相比于传统仿真测试方法，提出的虚实交互仿真测试方法的系统仿真粒度从500 ms减小到100 ms以内，仿真规模从9个节点和500个交通主体提升到150个节点和2 000个交通主体，仿真场景数量由36个扩展到98个，实现了异构交通主体渗透率0~100%动态可调，有效提高了车路协同混合交通仿真测试的效率、规模和覆盖度；目前新型混合交通环境下车路协同仿真测试需求快速朝着群体化、智能化、规模化演变，开展基于虚实交互和运行环境数据模拟的车路协同群体智能仿真测试方法技术研究，将有力推动下一代智能交通系统的发展。Abstract: The developments of vehicle-infrastructure cooperation and corresponding simulation and test technologies were summarized, and the simulation requirements, classical methods, and technical bottlenecks in the rudiment, infancy, and developing stages were discussed with a focus on the typical simulation results. A new three-layer virtual-real interactive simulation and test framework was proposed based on the traffic subject modeling, swarm behavior simulation, and test result analysis. According to the simulation requirements of mixed traffic subjects, a model for the heterogeneous traffic subjects was constructed, and the operation mechanism of mixed traffic was analyzed to serve as the underlying model support for the simulation system. With the designed virtual-real interactive simulation and test framework, breakthroughs were accomplished in the scenario generation technology for the mixed traffic swarm intelligence, and a simulation method for the mixed traffic swarm intelligence was put forward. Then, simulation tests of decision-making and control methods for different swarm intelligences were carried out in the selected typical traffic scenarios, such as intersections and road sections, to verify the effectiveness of the proposed method. Finally, the future development directions of vehicle-infrastructure cooperation and corresponding suggestions were summarized. Research results show that show that compared with the traditional simulation and test method, the proposed virtual-real interactive simulation and test method reduces the system's simulation granularity from 500 ms to less than 100 ms, the simulation scale increases from 9 nodes and 500 traffic subjects to 150 nodes and 2 000 traffic subjects, and the number of simulated scenarios enhances from 36 to 98. The dynamic adjustment within a range of 0-100% penetration rate of heterogeneous traffic subjects is achieved, and the efficiency, scale, and coverage of the vehicle-infrastructure cooperative simulation and test of mixed traffic are effectively improved. The requirements of vehicle-infrastructure cooperative simulation and test in the new mixed traffic environment are rapidly evolving towards the larger swarm, higher intelligence, and larger scale. Carrying out research on the method and technology for the simulation and test on the vehicle-infrastructure cooperative swarm intelligence based on the virtual-real interaction and operating environment data simulation will effectively promote the development of the next generation of the intelligent traffic system.
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图 1 车路协同群体智能仿真测试发展路线
Figure 1. Development route of vehicle-infrastructure cooperative swarm intelligence simulation and test
图 3 车路协同视景一体化仿真架构
Figure 3. Visual integrated simulation framework of vehicle-infrastructure cooperation
图 6 车路协同视景仿真系统结构
Figure 6. Structure of vehicle-infrastructure cooperative visual simulation system
图 7 典型车路协同应用场景下效能测试架构
Figure 7. Performance test framework in typical vehicle-infrastructure cooperative application scenarios
图 8 不同CV渗透率下预警成功统计结果
Figure 8. Statistical results of early warning success under different penetration rates of CV
图 9 不同车距定位误差下最小车距统计结果
Figure 9. Statistical results of minimum vehicle distance under different vehicle distance positioning errors
图 10 车路协同群体智能仿真测试技术框架
Figure 10. Vehicle-infrastructure cooperative swarm intelligence simulation and test technology framework
图 11 异构交通群体智能行为仿真框架
Figure 11. Behavior simulation framework for heterogeneous traffic swarm intelligence
图 14 虚实交互的大规模交通仿真平台框架
Figure 14. Framework of large-scale traffic simulation platform based on virtual-real interaction
图 18 仿真测试需求与场景案例关系
Figure 18. Relationship between simulation test requirements and scenario cases
图 20 多交叉口区域协同控制示意
Figure 20. Illustration of multi-intersection area cooperative control
图 22 混合交通群体智能协同行为仿真系统
Figure 22. Cooperative behavior simulation system for mixed traffic swarm intelligence
图 24 信号控制周期仿真和通行效率验证
Figure 24. Signal control cycle simulation and traffic efficiency verification
图 25 分步式超车入队车辆的行驶速度
Figure 25. Driving speeds of step-by-step overtaking vehicle entering queue
图 30 不同场景的延时和吞吐量仿真结果
Figure 30. Simulation results of delays and throughputs in different scenarios
图 31 不同CAV渗透率时车辆延迟和停车次数对比
Figure 31. Comparison of vehicle delays and parking times under different penetralion rates of CAV
1. Development routes of vehicle–infrastructure cooperative swarm intelligence simulation and test
7. Performance test framework in typical vehicle–infrastructure cooperative application scenarios
8. Statistical results of early warning success under different penetration rates of CVs
9. Statistical results of minimum vehicle distance under different vehicle-distance positioning errors
10. Simulation and test technology framework for vehicle–infrastructure cooperative swarm intelligence
14. Framework of large-scale traffic simulation platform based on virtual–real interaction
31. Comparison of vehicle delays and stop times under different penetration rates of CAVs
表 1 车路协同仿真测试需求与特征演化
Table 1. Simulation and test requirements and feature evolution of vehicle-infrastructure cooperation
仿真阶段 仿真手段 仿真对象 典型测试方法/架构 特征 萌芽期 虚拟仿真 交通个体/ 宏观交通流 黑盒测试/ 元胞自动机 低智化个体化 起步期 视景一体化仿真 小规模群体 高层体系架构 智能化分布式 发展期 虚实交互 大规模群体 硬件在环/ 实车在环 群智化规模化 
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表 2 起步期部分车路协同典型应用场景
Table 2. Some typical application scenarios of vehicle-infrastructure cooperation in initial stage
控制模式 场景名称 通信 分类 单车控制 绿波车速引导 V2I 效率 长直路段车路通讯 V2I 服务 限速预警 V2I 安全 多车协同 前向碰撞预警 V2V 安全 盲区预警/变道预警 V2V 安全 协作式变道 V2V 效率
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表 3 异构交通主体模型特征对比
Table 3. Characteristics comparison of heterogeneous traffic subject models
类型 模型输入 反馈形式 模型输出 HDV模型 车头间距、本车/前车速度 非线性 加速度 AV模型 车头间距、本车/前车速度 线性 加速度 CV模型 队列车辆位置、速度、加速度、时延 线性 加速度变化量 CAV模型 车头间距、前车速度、本车速度/加速度 线性 加速度/速度
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表 4 群体混合场景示例
Table 4. Examples of swarm mixed scenarios
场景功能分类 场景功能特征 仿真构建需求 协作式交叉口通行 信号控制交叉口车辆常规通行 安全/效率 信号控制交叉口处理异常停车 安全/效率 交叉口信号自适应控制 效率 交叉口动态车道管理 效率/管理 无信号控制交叉口组织通信异构车辆常规通行 安全/效率 协作式城市快速路匝道控制 城市快速路单匝道控制 安全/效率 城市快速路主线多匝道协同控制 安全/效率 城市快速路与高速公路结合部控制 安全/效率 协作式城市路段控制 城市常规路段组织车辆编队 安全/效率 常规路段组织紧急车辆优先通行 安全/效率
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1. Simulation and test requirements and feature evolution of vehicle–infrastructure cooperation
2. Some typical application scenarios of vehicle–infrastructure cooperation in the rudiment stage
3. Characteristics comparison of heterogeneous traffic subject models
4. Examples of swarm mixed scenarios
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