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Autonomous race cars require perception, estimation, planning, and controlmodules which work together asynchronously while driving at the limit of avehicle's handling capability. A fundamental challenge encountered in designingthese software components lies in predicting the vehicle's future state (e.g.position, orientation, and speed) with high accuracy. The root cause is thedifficulty in identifying vehicle model parameters that capture the effects oflateral tire slip. We present a model-based planning and control framework forautonomous racing that significantly reduces the effort required in systemidentification and control design. Our approach alleviates the gap induced bysimulation-based controller design by learning from on-board sensormeasurements. A major focus of this work is empirical, thus, we demonstrate ourcontributions by experiments on validated 1:43 and 1:10 scale autonomous racingsimulations.

BayesRace: Learning to race autonomously using prior experience