AI Enabled Accident Detection and Alerting System Using IOT.

Abstract: This project focuses on the development of an intelligent IoT-based accident detection and alerting system enhanced with machine learning techniques. The proposed system is designed for vehicle safety by continuously monitoring the driver’s physical condition, vehicle movement, and surrounding environment to reduce the risk of road accidents. A Raspberry Pi acts as the central processing unit and is interfaced with various sensors such as a USB camera, heart rate sensor, alcohol sensor, MEMS sensor, GPS module, ultrasonic sensor, and GSM module. The system analyzes real-time data collected from these sensors to assess driver alertness, health status, and driving behavior. The camera tracks eye movements to identify signs of drowsiness or fatigue, while the heart rate sensor provides information about the driver’s physiological condition. The alcohol sensor detects the presence of alcohol in the driver’s breath and generates alerts if unsafe levels are detected. Accident detection is achieved using a MEMS sensor that identifies abrupt changes in acceleration or orientation, indicating a possible collision. Additionally, the ultrasonic sensor measures the distance to nearby vehicles to assist in collision avoidance.