A Novel IoT-Driven Smart Detection of Driver Drowsiness and Alcohol Impairment

Abstract

Driver drowsiness and alcohol consumption are significant contributors to road accidents, posing a serious risk to passengers, pedestrians, and other motorists. This project introduces an advanced IoT based system that integrates behavioral and vehicle based approaches to detect driver fatigue and alcohol impairment in real time. Utilizing a Convolutional Neural Network (CNN) algorithm, the system captures and analyzes facial expressions through a camera to identify signs of drowsiness, while an alcohol sensor detects intoxication levels. Additionally, an LED display on the windshield and rear of the vehicle alerts nearby drivers with messages. A built-in SOS button enables passengers to send emergency alerts to the cab organization, ensuring swift action. By continuously monitoring the driver’s condition, issuing timely warnings, and facilitating emergency responses, this system enhances road safety and provides a robust real time solution for preventing drowsiness and drunk driving-related accidents.