Driver Drowsiness Detection and Auto Vehicle Controlling System

Abstract

This project proposes an innovative solution to enhance road safety by developing an automated system for detecting driver drowsiness and health conditions using Python-based webcam technology. By analyzing facial expressions and monitoring fatigue levels through machine learning algorithms, the system can identify signs of drowsiness. Upon detection, a signal is transmitted to a local server, which communicates with a NodeMCU device installed in the vehicle. This device initiates safety measures, including reducing vehicle speed, activating hazard lights, and spraying water via a suction motor to awaken the driver. Additionally, a PPG sensor monitors the driver's heart rate, enabling automatic speed adjustments if abnormal patterns suggest potential health issues or discomfort. All data and status updates are logged on the Thing Speak cloud platform, providing real-time monitoring and analysis for comprehensive driver safety management. Ultimately, this integrated approach aims to prevent accidents by proactively addressing driver fatigue and health concerns.