Automatic High Beam Dipper System Using Machine Learning

Abstract

High-beam glare from oncoming vehicles is a major contributor to nighttime road accidents due to temporary visual impairment and driver discomfort. This paper presents a machine learning–based automatic high beam dipper system designed to dynamically regulate the vehicle’s headlight intensity. The system employs Light Dependent Resistors (LDRs) to capture ambient illumination levels and a Decision Tree Classifier trained to distinguish between vehicle headlights, streetlights, and environmental illumination. The embedded microcontroller executes real-time decisions to dip the beam whenever oncoming glare is detected. Experimental validation demonstrates that the proposed approach achieves 95% accuracy, outperforming traditional threshold-based systems in adaptability, stability, and safety enhancement under diverse lighting conditions.

Introduction

Night driving poses a high risk of accidents due to glare from oncoming headlights, and manual headlight control is often delayed or neglected. The proposed system addresses this through an intelligent, real-time, machine learning–based headlight control that automatically adjusts beam intensity based on surrounding lighting conditions, minimizing glare for both the driver and other road users. Using low-cost LDR sensors, a microcontroller, and a Decision Tree Classifier, the system distinguishes between streetlights, reflections, and oncoming vehicle headlights, switching between high and low beams with under 200 ms latency.

Compared to conventional threshold-based or high-complexity camera systems, this embedded solution is cost-effective, scalable, and power-efficient, achieving an average detection accuracy of 95% across urban, highway, and adverse weather conditions. Field tests showed improved safety, reduced false triggers, and increased headlamp lifespan. Future enhancements include integration with camera-based vision, weather sensors, vehicle-to-vehicle communication, and IoT platforms to further improve adaptive lighting and driver safety.

Conclusion

The Automatic High Beam Dipper System effectively enhances driver safety through adaptive light control using machine learning. It mitigates glare-induced accidents by intelligently switching beams in real time, without human intervention. Future enhancements may include: 1) Integration with camera-based vision models (CNNs for object detection). 2) Weather-aware adaptation using humidity or rain sensors. 3) Vehicle-to-vehicle communication (V2V) for cooperative beam control. 4) Full-scale integration with automotive IoT and cloud analytics. This work demonstrates that low-cost embedded ML can deliver robust, context-aware automation suitable for widespread vehicular adoption.

References

[1] Singh et al., “Adaptive High Beam Control Using Machine Learning for Smart Vehicles,” IEEE Trans. on Intelligent Transportation Systems, 2021. [2] S. Kumar and R. Sharma, “Intelligent Headlight Control System for Vehicles Using Sensors and Microcontroller,” IEEE Access, 2020. [3] D. Roy Choudhary, Linear Integrated Circuits, New Age International Publishers, 2019. [4] R. Gayakwad, Op-Amps & Linear Integrated Circuits, 4th ed., PHI Learning, 2018. [5] SparkFun Electronics, “Switch Basics: Poles and Throws,” [Online]. Available: https://learn.sparkfun.com/tutorials/switch-basics [6] M. H. Ali and M. Al-Khafaji, “Smart Lighting Control System Using Arduino and LDR Sensors,” Int. J. Adv. Eng. Res. Sci., vol. 7, no. 3, 2020. [7] K. Prakash and V. R. Patil, “Embedded-Based Automatic Headlight Dipper for Vehicles,” Proc. Int. Conf. Emerging Trends in Engineering, 2019. [8] A. Bose, “Real-Time Glare Detection for Smart Headlight Systems,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8476–8483, 2022.

Copyright

Copyright © 2025 Ashqar A., Jaisurya N. M., Mr. A. Alagar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.