SAFETRACK: Real-Time IoT-Based Vehicle Accident Detection, Severity Analysis and Emergency Alert System

Abstract: Road accidents are a major cause of fatalities worldwide, often worsened by delayed emergency response, drunk driving, and lack of real-time monitoring systems. This paper presents SAFETRACK, a real-time IoT-based vehicle safety system that integrates accident detection, accident severity analysis, alcohol detection, smoke detection, and automated emergency alerting into a single embedded platform. The system employs an accelerometer (MPU6050) to detect sudden vehicle impacts, an MQ-3 alcohol sensor to monitor driver sobriety, an MQ-2 smoke sensor to detect in- cabin smoke or gas, a GPS module (NEO-6M) for real-time location tracking, and a GSM module (SIM800L) for emergency SMS dispatch. Upon detection of any critical event, the microcontroller (ESP32/Arduino) processes sensor data, determines severity, and transmits alerts with GPS coordinates to pre-registered emergency contacts and uploads data to an IoT cloud dashboard. The system also disables the vehicle ignition upon alcohol detection. Experimental results demonstrate reliable detection, low response latency, and effective integration of cloud monitoring using ThingSpeak and Google Maps API.

Keywords: IoT, vehicle safety, accident detection, ESP32, MPU6050, GPS, GSM, alcohol detection, smoke detection, emergency alert, severity analysis