  In this paper we propose a hierarchical controller for autonomous racing
where the same vehicle model is used in a two level optimization framework for
motion planning. The high-level controller computes a trajectory that minimizes
the lap time, and the low-level nonlinear model predictive path following
controller tracks the computed trajectory online. Following a computed optimal
trajectory avoids online planning and enables fast computational times. The
efficiency is further enhanced by the coupling of the two levels through a
terminal constraint, computed in the high-level controller. Including this
constraint in the real-time optimization level ensures that the prediction
horizon can be shortened, while safety is guaranteed. This proves crucial for
the experimental validation of the approach on a full size driverless race car.
The vehicle in question won two international student racing competitions using
the proposed framework; moreover, our hierarchical controller achieved an
improvement of 20% in the lap time compared to the state of the art result
achieved using a very similar car and track.
