An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, i.e. physics-based and manoeuvre-based methods. Using a physics-based methodology, this paper proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the current vehicle states based on sensing module outputs. Then a Kalman Predictor with actual lane keeping control law is used to predict steering actions and vehicle states in the future. A lane departure assessment module evaluates the probabilistic distribution of vehicle corner positions and decides whether to initiate a human takeover request. The prediction algorithm is capable to describe the stochastic characteristics of future vehicle pose, which is preliminarily proved in simulated tests. Finally, the on-road tests at speeds of 15 to 50 km/h further show that the pro-posed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.
翻译:自动驾驶系统应有能力监督自身性能,必要时请人驾驶员接管。在车道保持假设中,预测车辆未来轨迹是实现安全和可信赖的驾驶自动化的关键。以往关于车辆轨迹预测的研究主要分为两类,即物理和机动方法。本文件采用物理方法,建议采用基于闭路车辆动态模型的车道离开预测算法。我们使用扩大的Kalman过滤器,根据感应模块输出量估算现有车辆状态。然后,使用具有实际车道保持控制法的Kalman预测器,预测未来的车道控制状态。航道离开评估模块评估车辆角位置的概率分布,决定是否提出人手接管请求。预测算法能够描述未来车辆姿势的随机特征,这在模拟测试中已得到初步证明。最后,以15至50公里/小时的速度进行的公路测试进一步表明,代用的方法可以准确预测车辆未来轨迹。它可能作为对车道离开风险评估的可行解决办法,用于自动车道保持功能。