  We present a holistically designed three layer control architecture capable
of outperforming a professional driver racing the same car. Our approach
focuses on the co-design of the motion planning and control layers, extracting
the full potential of the connected system. First, a high-level planner
computes an optimal trajectory around the track, then in real-time a mid-level
nonlinear model predictive controller follows this path using the high-level
information as guidance. Finally a high frequency, low-level controller tracks
the states predicted by the mid-level controller. Tracking the predicted
behavior has two advantages: it reduces the mismatch between the model used in
the upper layers and the real car, and allows for a torque vectoring command to
be optimized by the higher level motion planners. The tailored design of the
low-level controller proved to be crucial for bridging the gap between planning
and control, unlocking unseen performance in autonomous racing. The proposed
approach was verified on a full size racecar, considerably improving over the
state-of-the-art results achieved on the same vehicle. Finally, we also show
that the proposed co-design approach outperforms a professional racecar driver.

Robotics;Systems and Control;Systems and Control