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。由于自动驾驶车辆在环岛场景中面临挑战，因此选用环岛作为方法应用与结果展示的案例。首先介绍了基于无迹卡尔曼滤波（UKF）的状态估计方法，并将其应用于环岛场景。为模拟实验生成逼真的环岛交通流，采用了微观交通仿真器SUMO。随后，针对环形交叉口案例，介绍并评估了一种基于变点检测的多策略驾驶行为预测方法。最后，将这些方法相结合，实现了基于UKF与策略预测的车辆轨迹估计，并通过环岛示例进行了验证。