  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 the
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,
resulting in a considerable improvement 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.
