Hierarchical drifting control strategy for intelligent vehicles with supervisory mechanism

As an extreme vehicle dynamic behavior, the drift maneuver holds significant research value for enhancing the handling performance and active safety of vehicles under extreme operating conditions. However, maintaining the drift state of autonomous vehicles while ensuring accurate path following remains a significant challenge. Thus, this paper proposes an integrated control framework that incorporates a vehicle drift strategy into general path following, enabling drift tracking control for all-wheel-drive (AWD) vehicles. First, based on a nonlinear three-degree-of-freedom (3-DOF) vehicle dynamics model, the steady-state drift characteristics of the vehicle are analyzed, and different drift equilibrium points are determined. Subsequently, a hierarchical drift tracking controller is proposed based on Model Predictive Control (MPC): the upper-layer controller achieves path following functionality based on vehicle kinematics and drift equilibrium points, while the lower-layer controller derives the control inputs for the vehicle using the full-vehicle dynamics model and desired state derivatives. Lastly, a controller switching strategy is proposed to manage the transition into and out of the drift state, and its effectiveness and real-time performance are validated via simulations and hardware-in-the-loop (HiL) tests.