Abstract: Aiming at the problem of noise in acquiring cooperative adaptive cruise control (CACC) vehicle state data (including displacement, speed, acceleration, etc.) through vehicle-to-vehicle and vehicle-to-road communication in V2X environment, a method to improve the accuracy of CACC vehicle driving data based on improved Kalman filtering algorithm was proposed, considering the change of velocity azimuth angle of the vehicles when driving on the curve. According to the geometric relationship of the CACC vehicle driving on the curve, by taking into account the changes of vehicle driving displacement, speed and other driving states at the previous time and the current time, the optimal estimation of vehicle driving state was made for the driving data after improved Kalman filtering, so as to improve the accuracy of CACC vehicle driving state data. The improved Kalman filtering algorithm was established through the joint simulation platform of CarSim and Simulink to simulate and verify the data obtained by CACC vehicles during the course of driving on the curve. The simulation results show that the improved Kalman filtering algorithm makes the accuracy of vehicle driving displacement MSE increase by 81.04% and RMSE increase by 56.96%, which is closer to the expected value and has better accuracy.

Key words: vehicle engineering; intelligent transportation; CACC; improved Kalman filtering; data processing; V2X

摘要： 针对V2X环境下通过车车、车路通信获取的CACC车辆行驶状态数据（包括位移、速度和加速度等）存在噪声的问题，考虑车辆在弯道行驶过程中速度方位角的变化，提出了一种改进卡尔曼滤波算法的CACC车辆行驶数据精度提高方法。该方法根据CACC车辆在弯道行驶的几何关系，通过考虑上一时刻和当前时刻车辆行驶位移、速度等行驶状态的变化，将其行驶数据经过改进卡尔曼滤波后对车辆行驶状态做出最优估计，进而提高CACC车辆行驶状态数据精度。通过CarSim和Simulink联合仿真平台建立改进卡尔曼滤波算法对CACC车辆在弯道行驶过程中获取数据的仿真和验证。仿真结果表明，改进的卡尔曼滤波算法使车辆行驶位移的精确度MSE提高了81.04%，RMSE提高了56.96%，更接近期望值，具有更好的准确性。

关键词: 车辆工程；智能交通；协同自适应巡航控制；改进卡尔曼滤波；数据处理；V2X