Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

中文翻译：

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卡尔曼滤波器和运动模型在车辆状态估计和路径预测中的比较评价

车辆状态估计和路径预测通常涉及卡尔曼滤波器和运动模型，是智能驾驶的关键任务。在车辆状态估计中，很少讨论无迹卡尔曼滤波器（UKF）和扩展卡尔曼滤波器（EKF）在精度和效率方面的比较性能评估。本文致力于对结合不同运动模型的 UKF 和 EKF 的性能进行实证评估，并研究模型的属性和路径预测中的影响因素。已经进行了广泛的现实世界实验，结果表明 EKF 和 UKF 在状态估计方面具有大致相同的准确度；但是，EKF 通常比 UKF 快；最快的过滤器比最慢的过滤器快 2⋅6 倍。路径预测实验表明，速度估计和使用的运动模型会影响路径预测；模型越真实地反映车辆的驾驶状态，其预测就越可靠。