A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres

TL;DR

This paper introduces a learning-based predictive control method for vehicle evasive maneuvers that uses Student-t Processes to better handle uncertainties and improve obstacle avoidance at high speeds.

Contribution

The paper proposes a novel L-MPCC algorithm utilizing Student-t Processes to online model mismatches, enhancing vehicle stability and maneuverability during evasive actions.

Findings

Successfully avoids obstacles at higher speeds.

Reduces peak sideslip angle significantly.

Improves vehicle stability during double lane change.

Abstract

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.