  The development of vehicle controllers for autonomous racing is challenging
because racing cars operate at their physical driving limit. Prompted by the
demand for improved performance, autonomous racing research has seen the
proliferation of machine learning-based controllers. While these approaches
show competitive performance, their practical applicability is often limited.
Residual policy learning promises to mitigate this drawback by combining
classical controllers with learned residual controllers. The critical advantage
of residual controllers is their high adaptability parallel to the classical
controller's stable behavior. We propose a residual vehicle controller for
autonomous racing cars that learns to amend a classical controller for the
path-following of racing lines. In an extensive study, performance gains of our
approach are evaluated for a simulated car of the F1TENTH autonomous racing
series. The evaluation for twelve replicated real-world racetracks shows that
the residual controller reduces lap times by an average of 4.55 % compared to a
classical controller and even enables lap time gains on unknown racetracks.
