Road condition is an important environmental factor for autonomous vehicle control. A dramatic change in the road condition from the nominal status is a source of uncertainty that can lead to a system failure. Once the vehicle encounters an uncertain environment, such as hitting an ice patch, it is too late to reduce the speed, and the vehicle can lose control. To cope with unforeseen uncertainties in advance, we study a proactive robust adaptive control architecture for autonomous vehicles' lane-keeping control problems. In the proposed framework, the data center generates a prior environmental uncertainty estimate with a quantified uncertainty by combining weather forecasts and measurements from anonymous vehicles through a spatio-temporal filter. The prior estimate and quantified uncertainty contribute to designing a robust heading controller and nominal longitudinal velocity for proactive adaptation to each new abnormal condition. Then the control parameters are updated based on posterior information fusion with on-board measurements.

道路状况是自动驾驶车辆控制的重要环境因素。道路状况与名义状态的巨大变化是不确定性的来源，可能导致系统故障。一旦车辆遇到不确定的环境，例如撞上冰块，再减速就来不及了，车辆可能会失去控制。为了提前应对不可预见的不确定性，我们研究了一种用于自动驾驶车辆车道保持控制问题的主动鲁棒自适应控制架构。在所提出的框架中，数据中心通过时空过滤器结合天气预报和匿名车辆的测量结果，生成具有量化不确定性的事先环境不确定性估计。先前的估计和量化的不确定性有助于设计稳健的航向控制器和标称纵向速度，以主动适应每种新的异常情况。然后，基于后验信息与机载测量的融合来更新控制参数。