面向车路协同孪生仿真测试的多尺度滤波同步方法

为提升车路协同孪生仿真测试系统的同步性能，明确了孪生主体的运行机理，分析了影响系统同步性能的干扰因素，建立了孪生状态同步映射模型; 针对孪生状态采样的时钟异步问题，设计了时钟误差估计策略，修正了孪生仿真测试系统的量测时间偏差; 在此基础上，结合卡尔曼滤波原理，引入多尺度滤波器更新机制，建立了考虑同步采样误差的量测噪声模型，提出了多尺度滤波同步优化方法; 最后，在搭建的孪生仿真测试原型系统中，选取NGSIM数据集的车辆轨迹开展试验。研究结果表明:在不同车辆速度条件下，提出的多尺度滤波同步优化方法能够保持良好的同步性能; 在横向坐标同步方面，平均绝对误差小于1 mm，99.5%的绝对误差控制在8 mm以内; 在纵向坐标同步方面，平均绝对误差小于9 mm，99.5%的绝对误差控制在38 mm以内; 在速度同步方面，平均绝对误差小于2.8 cm·s-1，99.5%的绝对误差控制在24 cm·s-1以内; 在偏航角同步方面，平均绝对误差小于1.1×10-3 rad，99.5%的绝对误差控制在1.1×10-2 rad以内; 与航迹推算方法相比，提出的方法能够在横向坐标、纵向坐标、速度和偏航角方面平均提升30.0%的同步精度，能够有效解决孪生主体的状态异步问题，可保障车路协同孪生仿真测试系统的实时同步与精准运行。 

Multi-scale filtering synchronization method for vehicle-infrastructure cooperative twin-simulation testing

To enhance the synchronization performance of the vehicle-infrastructure cooperative twin-simulation testing system, the operation mechanism of twin objects was clarified. Then the interference factors affecting the synchronization performance of the system were analyzed to establish the synchronous mapping model for the twin state. In view of the asynchronous clock problem in twin state sampling, a clock error estimation strategy was designed to correct the measurement time deviation of the twin-simulation testing system. On this basis, a multi-scale filtering updating mechanism was introduced by combining the principle of the Kalman filtering. Furthermore, a measurement noise model considering the synchronization sampling errors was established, and the multi-scale filtering synchronization optimization method was proposed. Finally, the vehicle trajectories from the NGSIM dataset were selected to carry out experiments in a constructed prototype system of twin-simulation testing. Research results show that the synchronization performance can be well maintained by the proposed multi-scale filtering synchronization optimization method under different vehicle speeds. In terms of synchronizing the lateral coordinate, the mean absolute error (MAE) is less than 1 mm, and 99.5% of absolute error (AE) can be controlled to within 8 mm. In terms of synchronizing the longitudinal coordinate, the MAE is less than 9 mm, and 99.5% of AE can be controlled to within 38 mm. In terms of synchronizing the speed, the MAE is less than 2.8 cm·s-1, and 99.5% of AE can be controlled to within 24 cm·s-1. In terms of synchronizing the yaw angle, the MAE is less than 1.1×10-3 rad, and 99.5% of AE can be controlled to within 1.1×10-2 rad. Compared with the dead reckoning method, the proposed method can improve the synchronization accuracy by an average of 30.0% in terms of lateral coordinate, longitudinal coordinate, speed, and yaw angle, solve the asynchronous state problem for twin objects effectively, and guarantee the real-time synchronization and accurate operation of the vehicle-infrastructure cooperative twin-simulation testing system. 3 tabs, 10 figs, 31 refs.