IoT-Based Driver Drowsiness Detection System

Abstract

Driver fatigue is one of the leading causes of road accidents worldwide, often resulting in serious injuries and fatalities. This paper presents an IoT-based Driver Drowsiness and Alcohol Detection System that monitors a driver's alertness in real-time using embedded sensor technology. The system employs an eye-blink sensor (IR-based) to detect prolonged eye closure as an indicator of drowsiness, and an MQ-3 alcohol sensor to detect alcohol consumption. An Arduino UNO microcontroller processes sensor data and triggers immediate alerts via a buzzer and motor speed reduction when unsafe conditions are detected. Both detection modules operate independently and in parallel, ensuring that either condition triggers an appropriate safety response without requiring manual driver input. The system is designed to be low-cost, non-intrusive, and suitable for real-world vehicular applications.
Testing demonstrated accurate detection performance, with the buzzer activating consistently when eyes remained closed beyond the 3-second threshold. Future enhancements include GPS integration, cloud- based IoT monitoring, and machine learning-based detection for improved accuracy.

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