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Road condition is an important environmental factor for autonomous vehiclecontrol. A dramatic change in the road condition from the nominal status is asource of uncertainty that can lead to a system failure. Once the vehicleencounters an uncertain environment, such as hitting an ice patch, it is toolate to reduce the speed, and the vehicle can lose control. To cope with futureuncertainties in advance, we study a proactive robust adaptive controlarchitecture for autonomous vehicles' lane-keeping control problems. The datacenter generates a prior environmental uncertainty estimate by combiningweather forecasts and measurements from anonymous vehicles through aspatio-temporal filter. The prior estimate contributes to designing a robustheading controller and nominal longitudinal velocity for proactive adaptationto each new abnormal condition. Then the control parameters are updated basedon posterior information fusion with on-board measurements.

Robust Vehicle Lane Keeping Control with Networked Proactive Adaptation