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

Authors

  • Glenson Toney Lovely Professional University https://orcid.org/0000-0001-9536-6627
  • Gaurav Sethi Lovely Professional University
  • Cherry Bhargava Symbiosis International University
  • Aldrin Claytus Vaz Visvesvaraya Technological University
  • Navya Thirumaleshwar Hegde Manipal Academy of Higher Education

DOI:

https://doi.org/10.18196/jrc.v6i5.26740

Keywords:

Adaptive Headlamps, Headlamp Steering, Inertial Measurement Unit (IMU), Kalman Filter, Nighttime Safety, Slip Angle Estimation, Vehicle Dynamics Control

Abstract

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.

Author Biography

Glenson Toney, Lovely Professional University

Mr. Glenson is Research Scholar at SEEE, LPU Punjab. He works as an Assistant Professor in the Department of Electronics and Communication Engineering (ECE) and Training Coordinator at Training & Placement Cell of SJEC Mangaluru. He leads the Campus to Corporate Program and is an active member of Innoventure-SJEC Innovation Platform. Previously, Mr. Toney has served as the District Innovation Associate to the Government of Karnataka, promoting campus entrepreneurship through the New Age Innovation Network initiative.

Mr. Toney has received an Innovation Award from Texas Instruments, Active SPOC Award from NPTEL IIT Madras, and an Appreciation Award from Anveshana-Synopsys and the Agastya Foundation. He was also awarded with the SJEC Innovation Mentorship Award 2024. He hold a Master's degree in VLSI Design and is pursuing a PhD in AI technologies for automobiles at School of Electronics & Electrical Engineering, Lovely Professional University. He has published over fifteen papers in esteemed journals and international conferences and has reviewed papers at multiple IEEE Conferences and Engineering Education Journals. In addition to his academic and professional roles, Mr. Toney is an experienced host and has a passion for singing and dancing.

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Published

2025-08-29

How to Cite

[1]
G. Toney, G. Sethi, C. Bhargava, A. C. Vaz, and N. T. Hegde, “Sensor Fusion and Predictive Control for Adaptive Vehicle Headlamp Alignment: A Comparative Analysis”, J Robot Control (JRC), vol. 6, no. 5, pp. 2166–2183, Aug. 2025.

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