  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 future
uncertainties in advance, we study a proactive robust adaptive control
architecture for autonomous vehicles' lane-keeping control problems. The data
center generates a prior environmental uncertainty estimate by combining
weather forecasts and measurements from anonymous vehicles through a
spatio-temporal filter. The prior estimate contributes 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.
