To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles.
Liu et al. (Thu,) studied this question.