  This paper proposes a non-linear Model Predictive Contouring Control (MPCC)
for obstacle avoidance in automated vehicles driven at the limit of handling.
The proposed controller integrates motion planning, path tracking and vehicle
stability objectives, prioritising obstacle avoidance in emergencies. The
controller's prediction model is a non-linear single-track vehicle model with
the Fiala tyre to capture the vehicle's non-linear behaviour. The MPCC computes
the optimal steering angle and brake torques to minimise tracking error in safe
situations and maximise the vehicle-to-obstacle distance in emergencies.
Furthermore, the MPCC is extended with the tyre friction circle to fully
exploit the vehicle's manoeuvrability and stability. The MPCC controller is
tested using real-time rapid prototyping hardware to prove its real-time
capability. The performance is compared with a state-of-the-art Model
Predictive Control (MPC) in a high-fidelity simulation environment. The double
lane change scenario results demonstrate a significant improvement in
successfully avoiding obstacles and maintaining vehicle stability.
