  A good racing strategy and in particular the racing line is decisive to
winning races in Formula 1, MotoGP, and other forms of motor racing. The racing
line defines the path followed around a track as well as the optimal speed
profile along the path. The objective is to minimize lap time by driving the
vehicle at the limits of friction and handling capability. The solution
naturally depends upon the geometry of the track and vehicle dynamics. We
introduce a novel method to compute the racing line using Bayesian
optimization. Our approach is fully data-driven and computationally more
efficient compared to other methods based on dynamic programming and random
search. The approach is specifically relevant in autonomous racing where teams
can quickly compute the racing line for a new track and then exploit this
information in the design of a motion planner and a controller to optimize
real-time performance.
