NGSIM车辆轨迹重构

NGSIM Vehicle Trajectory Reconstruction

下一代交通仿真(next generation simulation,NGSIM)车辆轨迹数据存在异常值和测量误差,为了使其准确可用,在建模之前需要对NGSIM车辆轨迹进行重构.建立了两步车辆轨迹数据重构算法:1)通过小波分析和物理约束界限值识别两类异常值,并分别采用拉格朗日5次和3次多项式插值对异常值进行重新估计;2)在保证信号能量比的前提下,根据卡尔曼滤波算法对车辆轨迹进行滤波去噪.通过对NGSIM车辆轨迹数据库I-80中的样本轨迹进行重构,速度曲线和加速度曲线以及Jerk分析表明该轨迹重构算法使模型建立更加精确.之后,将该算法应用于整个数据库中,加速度分布图表明轨迹重构效果良好.

Many outliers and measurement errors exist in the next generation simulation (NGSIM) data. To make the vehicle trajectories more precise and usable for researching, they should be reconstructed before establishing certain models. In this paper, a two-step model was developed: 1) The two patterns of outliers were identified by wavelet analysis and physical restricts and modified by 5th-degree and 3th- degree Lagrange polynomial interpolation, respectively;2) The Kalman filter, taking signal energy into account, was conducted to filter the noises in NGSIM data. The performance of the two-step model was supported by the speed curve, acceleration curve, and jerk analysis from the NGSIM database I-80. Finally, this method was implemented to reconstruct all the vehicle trajectories in NGSIM data, and the acceleration distribution indicated that the performance was very good.