Integrated Driver Monitoring and Real-Time Accident Detection System using IoT and Computer Vision

Accidents relating to driver fatigue and collision are on the alarming rise, thus underlining the necessity for intelligent safety systems. This study presents an Integrated Driver Monitoring and Real-Time Accident Detection System that deploys IoT and computer vision to enhance road safety. The proposed system detects the drowsiness of the driver through real-time facial monitoring based on a Python deep learning model, whereas MEMS (Micro-Electro-Mechanical Systems) sensors detect sudden jerks or movements of the vehicle that can lead to a collision. After drowsiness detection, the system activates an alert mechanism that includes intrusion in buzzers and an ignition cutoff. In case of serious accidents, the GPS (Global Positioning System) and GSM (Global System for Mobile Communications) modules will immediately alert by sending emergency messages containing the vehicle's location to predefined contacts. The system is implemented on an Arduino microcontroller interfaced with several sensors for proper detection and immediate response. The evaluation of performance shows that the system is very accurate in detecting drowsiness and collisions, thus making the system an economical and dependable approach to preventing accidents. Future developments can integrate the system with AI-driven predictive analytics that can ultimately lower road incidents and improve response times.