  Autonomous race cars require perception, estimation, planning, and control
modules which work together asynchronously while driving at the limit of a
vehicle's handling capability. A fundamental challenge encountered in designing
these software components lies in predicting the vehicle's future state (e.g.
position, orientation, and speed) with high accuracy. The root cause is the
difficulty in identifying vehicle model parameters that capture the effects of
lateral tire slip. We present a model-based planning and control framework for
autonomous racing that significantly reduces the effort required in system
identification and control design. Our approach alleviates the gap induced by
simulation-based controller design by learning from on-board sensor
measurements. A major focus of this work is empirical, thus, we demonstrate our
contributions by experiments on validated 1:43 and 1:10 scale autonomous racing
simulations.
