IoT-enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology
Bakhtiar Muiz, Abdul Hasib, Md. Faishal Ahmed, Abdullah Al Zubaer, Rakib Hossen, Mst Deloara Khushi, Anichur Rahman
TL;DR
This work addresses the persistent problem of road accidents caused by drowsy and drunk drivers by proposing an IoT-enabled driver safety system that fuses IR eye-closure monitoring with a MQ-3 alcohol sensor. Implemented on an Arduino Uno with Bluetooth communication, the system issues alarms, lights, and vibrations, gradually slows or stops the vehicle when unsafe conditions persist, and transmits real-time alerts to a driver’s smartphone. Its main contributions lie in the full integration of alcohol detection, eye-tracking, wireless alerts, and automatic vehicle control, delivering a cohesive safety protocol in real-time. The approach has practical impact for reducing crashes in commercial and private driving contexts, with potential for future enhancements such as advanced facial recognition and calibration improvements to boost robustness across environments.
Abstract
Significant losses in terms of life and property occur from road traffic accidents, which are often caused by drunk and drowsy drivers. Reducing accidents requires effective detection of alcohol impairment and drowsiness as well as real-time driver monitoring. This paper aims to create an Internet of Things (IoT)--enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology. The system features an alcohol sensor and an IR sensor for detecting alcohol presence and monitoring driver eye movements, respectively. Upon detecting alcohol, alarms and warning lights are activated, the vehicle speed is progressively reduced, and the motor stops within ten to fifteen seconds if the alcohol presence persists. The IR sensor monitors prolonged eye closure, triggering alerts, or automatic vehicle stoppage to prevent accidents caused by drowsiness. Data from the IR sensor is transmitted to a mobile phone via Bluetooth for real-time monitoring and alerts. By identifying driver alcoholism and drowsiness, this system seeks to reduce accidents and save lives by providing safer transportation.