This paper proposes a closed-form feedback control framework for rear- and front-wheel-drive car-like vehicles, aiming to solve trajectory tracking and collision avoidance tasks operating in complex environments. This method constructs novel risk assessment functions that incorporate motion information, enabling accurate risk assessment and reducing the conservatism in collision avoidance. Consequently, the proposed framework can effectively handle various on-road situations, including lane-following and obstacle avoidance, parking maneuvers, and navigation through intersections. Lyapunov-based analysis proves the stability of the designed closed-form control scheme. Simulation results in various typical scenarios demonstrate that the proposed method can achieve safe, stable, and smooth trajectory tracking, with improved performance metrics such as reduced tracking error and control effort, verifying its feasibility and effectiveness.
Zhang et al. (Tue,) studied this question.