Sensor Fusion and Predictive Control for Adaptive Vehicle Headlamp Alignment: A Comparative Analysis

Nighttime driving safety is often compromised by the inability of conventional adaptive headlamp systems to account for lateral slip and rapidly changing road conditions, leading to misalignment and reduced visibility during aggressive maneuvers. Most existing approaches rely solely on steering angle, which limits adaptability under dynamic slip scenarios. This study presents the development and comparative evaluation of a Fused Controller that uniquely integrates sensor fusion, adaptive gain scheduling, and multi-step predictive optimization for robust adaptive headlamp alignment. Five control architectures- Filtered Proportional Controller (FPC), Raw State MPC (RS-MPC), Extended MPC (E-MPC), Feedforward-Enhanced MPC (FF-MPC), and the proposed Fused Controller- were systematically evaluated on a 2 km synthetic road with ten challenging segments. Compared to the E-MPC baseline, the Fused Controller achieved a 42.5% reduction in root mean square error (RMSE) in long S-curves and a 30.6% improvement in sharp turns, with a settling time of 0.6 s (versus 1.8 s for FPC) and a jitter index of 9.93°/s. Frequency-domain analysis confirmed a 1.2 Hz bandwidth with actuator-compatible roll-off, and stability analysis validated robustness under noise and disturbances. Statistical analysis across 20 independent simulation runs per controller showed these improvements are highly significant (p < 0.001, large Cohen’s d), confirming the practical superiority of the Fused Controller. These results indicate enhanced driver visibility and reduced nighttime collision risk, while the controller’s computational efficiency and adaptive gains support scalability and real-world deployment. This work provides a rigorous and practical framework for next-generation adaptive lighting systems.