This paper presents methods for vehicle state estimation and prediction for autonomous driving. A roundabout is chosen to apply the methods and illustrate the results as autonomous vehicles have difficulty in handling roundabouts. State estimation based on the unscented Kalman filter (UKF) is introduced first with application to a roundabout. The microscopic traffic simulator SUMO is used to generate realistic traffic in the roundabout for the simulation experiments. Change point detection based driving behavior prediction using a multi policy approach is then introduced and evaluated for the round intersection example. Finally, these methods are combined for vehicle trajectory estimation based on UKF and policy prediction and demonstrated using the roundabout example.

翻译：本文提出了用于自动驾驶的车辆状态估计与预测方法。鉴于自动驾驶车辆在环形交叉口处理中面临挑战，本文选取环形交叉口作为方法应用场景并展示结果。首先介绍基于无迹卡尔曼滤波（UKF）的状态估计方法，并将其应用于环形交叉口场景。采用微观交通仿真软件SUMO生成环形交叉口中的真实交通流用于仿真实验。随后针对环形交叉口实例，引入基于多策略的变点检测驾驶行为预测方法并进行评估。最后，结合基于UKF的车辆轨迹估计与策略预测方法，通过环形交叉口实例验证了组合方法的有效性。