Research on a Lane Changing Obstacle Avoidance Control Strategy for Hub Motor-Driven Vehicles

Hub motor-driven vehicles can control vehicle attitude by regulating the speed and torque of four wheels, supporting safe and stable lane changing and obstacle avoidance. However, under high-speed scenarios, these vehicles often suffer from poor stability, limited comfort, and inadequate trajectory tracking accuracy during lane changing and obstacle avoidance operations. To address these challenges, this study proposes a lane changing obstacle avoidance control strategy for hub motor-driven vehicles based on collision risk prediction. A fuzzy controller featuring a variable weight objective function is designed to balance lane changing efficiency and ride comfort, thereby generating an optimal lane changing and obstacle avoidance trajectory. Furthermore, a linear time-varying model predictive controller (LTV-MPC) is developed, which adaptively adjusts both the weighting coefficient of lateral displacement error in the objective function and the prediction horizon of the controller, enabling dynamic tuning of vehicle trajectory tracking accuracy. A dSPACE hardware-in-the-loop (HIL) platform was established to conduct simulations under typical obstacle avoidance scenarios. The simulation results show that under two easily destabilized conditions—high-adhesion, high-speed, large-curvature, and low-adhesion, medium-speed, large-curvature maneuvers—the proposed optimized control strategy limits the maximum lateral trajectory tracking error to 0.116 m and 0.143 m, representing reductions of 58.6% and 79.6% compared with the baseline control strategy. These results demonstrate that the proposed method enhances trajectory tracking accuracy and stability during lane changing and obstacle avoidance maneuvers.