Lighting Maintenance System for Vehicle Lights: Monitoring Expiry and Urgency Levels

Thenu Manickavelu1[/], Vijayakumar Ponnusamy1, Milos Milasinovic2
1 Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203
2 Belgrade Metropolitan University, Serbia
tm9421@srmist.edu.in
vijayakp@srmist.edu.in
milos.milasinovic@metropolitan.ac.rs
DOI: 110.46793/BISEC25.393M

 

ABSTRACT: Vehicle lighting systems are critical for ensuring road safety, driver visi-bility, and compliance with transportation regulations. However, traditional mainte-nance practices rely heavily on manual inspection and reactive replacement, leading to overlooked failures, expired lighting components, and increased safety risks. This paper presents an intelligent Vehicle Lighting Maintenance System (VLMs) designed to mon-itor, predict, and manage the health of automotive lighting components in real time. The system integrates IoT-based sensors, OBD-II diagnostics, and data analytics to measure key operational parameters such as brightness intensity, voltage stability, cur-rent consumption, and thermal behavior. A predictive maintenance model estimates re-maining useful life (RUL) using degradation trends and anomaly detection algorithms, while a multi-level urgency classification framework categorizes components into nor-mal, moderate, critical, and expired states. A visualization module generates heatmaps, Gantt charts, and statistical summaries to support informed decision-making for both individual  users and fleet operators. Experimental results demonstrate that the pro-posed system improves fault detection accuracy, reduces unexpected lighting failures, enables cost-efficient maintenance planning, and  offers a scalable solution for intelli-gent vehicle health management. The system ultimately enhances operational effi-ciency and safety by enabling timely and data-driven maintenance actions.

KEYWORDS: Vehicle Lighting Monitoring, Predictive Maintenance, IoT Sensors, OBD-II Di-agnostics, Lighting Expiry Tracking, Maintenance Visualization, Real-Time Fault Detection, Automotive Safety, Fleet Maintenance Optimization, Intelligent Vehicle Systems.

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IZVOR: Proceedings of the 16th International Conference on Business Information Security BISEC’2025