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