Prediction–Preview Cooperative Steering Control for Optimal Path Tracking in Autonomous Electric Vehicles

Reliable steering regulation under varying road curvature and actuator constraints remains a central challenge in autonomous electric vehicles (AEVs). Many exiting approaches rely on reactive error correction or treat preview information solely as a reference adjustment, limiting anticipation and physical consistency. This study proposes a prediction–preview steering control (PSC) framework in which future curvature information within state propagation and constraint handling enables forward-looking steering decisions while respecting dynamic and actuator limits. The method is evaluated using a lateral-heading vehicle model with real-road geometric variation. Experimental results indicate significant improvement in tracking performance, reducing lateral RMSE from 0.1747 m to 0.0074 m with a maximum deviation of 0.0889 m and limiting heading RMSE to 0.0867° (maximum 1.2046°). Steering angle commands remain bounded within ±8.7°, while steering angle rate is maintained within 40–60°/s, ensuring smooth and dynamically admissible operation. The proposed strategy offers a computationally efficient solution for embedded AEV steering systems and demonstrates improved robustness under practical curvature transitions.