A Nonlinear Model-Predictive Contouring Controller for Shared Control Driving Assistance in High-Performance Scenarios

An increasing number of vehicles today are equipped with advanced driver-assistance systems that provide humans involved in the driving tasks with continuous and active support. State-of-the-art implementations of these systems frequently rely on an underlying vehicle controller based on the model-predictive control strategy. In this article, we propose a nonlinear model-predictive contouring controller for a driving assistance system in high-performance scenarios. The design follows specific features to ensure the effectiveness of the interaction, namely, adaptability with respect to the current vehicle state, high-performance driving capabilities, and tunability of the assistance system. First, the control algorithm performance is evaluated offline and compared with a commercial lap-time minimizer, then experimental implementation of the assistance system with the human driver (HD) in the loop has been accomplished on a professional dynamic driving simulator, where an evaluation of the specific features has been performed: 1) a gg-bound is exploited to adapt the controller's behavior to different driver abilities; 2) the controller's adaptability to unexpected HD behavior is tested; and 3) the controller's ability to handle the vehicle at the limit of maneuverability is established. The obtained strategy, then, demonstrates to be suitable as an underlying vehicle controller for a driver-assistance system on a racing track.