Efficient Data-driven Reference Governor Design for Safe Evasive Manoeuvring

Abstract

This paper presents a novel data-driven Reference Governor with Model Predictive Control, integrating local motion replanning and path following for collision avoidance. Employing a model-free Reference Governor, the proposed framework utilises system knowledge through Bayesian Optimisation to augment predetermined evasive trajectories, minimising pathfollowing errors and simultaneously ensuring obstacle safety margins. A single-track vehicle model in combination with a nonlinear tyre model is used to capture the vehicle's dynamics. The optimised control action is the vehicle steering angle, whilst the Reference Governor optimises parameters of a sigmoid reference signal to minimise the tracking error and guarantee safety with respect to obstacles in emergency manoeuvres. The proposed approach is evaluated on a single lane change using a highfidelity simulation environment, and its performance is compared to a baseline controller integrating path following and obstacle avoidance. The results demonstrate a 14% reduction in safety critical overshoot, maximising obstacle safety distance and a four times lower controller cycle time compared to the baseline. Furthermore, through a robustness analysis, it is demonstrated that the proposed approach is more robust towards model mismatches and perception-based errors, as seen by average 30% and 40% reductions in near-miss and collision rates.