Human-Guided Shared Control Strategy for Drift-Grip Adaptation Under Low Adhesion Conditions

Extreme environments such as icy roads can easily lead to vehicular instability-induced accidents. The insight is that under such operating conditions, the motion of vehicle-tire integrated system shows strong nonlinearity, while driver’s handling experience is insufficient. This article proposes a cooperative driving framework aiming for robust shared control of intelligent vehicles. Specifically, a human-guided shared control (HGSC) strategy is developed to assist human drivers to keep the vehicle controllable under low adhesion conditions. To attain the goal, a hierarchical drift-grip adaptive controller (H-DGAC) is designed out of event-triggered model predictive control problem. Besides, a knowledge-based dual-level Takagi–Sugeno–Kang (DL-TSK) model is established to infer the guidance information injected by the driver. Finally, the proposed strategy is verified by human-in-the-loop experiments, exhibiting its prominent superiority for vehicle stability enhancement.