  This paper presents a novel Learning-based Model Predictive Contouring
Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The
algorithm uses the Student-t Process (STP) to minimise model mismatches and
uncertainties online. The proposed STP captures the mismatches between the
prediction model and the measured lateral tyre forces and yaw rate. The
mismatches correspond to the posterior means provided to the prediction model
to improve its accuracy. Simultaneously, the posterior covariances are
propagated to the vehicle lateral velocity and yaw rate along the prediction
horizon. The STP posterior covariance directly depends on the variance of
observed data, so its variance is more significant when the online measurements
differ from the recorded ones in the training set and smaller in the opposite
case. Thus, these covariances can be utilised in the L-MPCC's cost function to
minimise the vehicle state uncertainties. In a high-fidelity simulation
environment, we demonstrate that the proposed L-MPCC can successfully avoid
obstacles, keeping the vehicle stable while driving a double lane change
manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the
proposed controller yields a significantly lower peak sideslip angle, improving
the vehicle's manoeuvrability compared to an L-MPCC with a Gaussian Process.
