Driver Drowsy Detection with IoT-Based Vehicle Control System

Abstract

The Driver Drowsy Detection System using Deep Learning with an IoT-based Automatic Vehicle Control System is designed to enhance road safety by detecting driver fatigue and taking automated corrective actions. This system leverages a combination of deep learning algorithms, real-time image processing, and IoT-based vehicle control mechanisms. Using OpenCV and Dlib, it monitors the driver’s facial features, specifically tracking eye movements to detect signs of drowsiness. Once signs of fatigue are detected, the system uses NodeMCU to communicate with various vehicle components, including a relay, motor, and vibrator motor, to trigger automated responses. These responses can include vibrating the seat or controlling the vehicle's speed to prevent accidents. The system is integrated with Blynk IoT, allowing remote monitoring and alerts to be sent to the driver or vehicle operator. This solution offers a proactive approach to preventing accidents caused by driver fatigue, combining deep learning with IoT to create a safer driving environment.