  Autonomous racing represents a uniquely challenging control environment where
agents must act while on the limits of a vehicle's capability in order to set
competitive lap times. This places the agent on a knife's edge, with a very
small margin between success and loss of control. Pushing towards this limit
leads to a practical tension: we want agents to explore the limitations of
vehicle control to maximise speed, but inadvertently going past that limit and
losing control can cause irreparable damage to the vehicle itself. We provide a
model predictive control (MPC) baseline that is able to, in a single lap,
safely adapt to an unseen racetrack and achieve competitive lap times. Our
approaches efficacy is demonstrated in simulation using the Learn To Race
Challenge's environment and metrics.
