Embedded Vehicular Assistance Platform Using Multi-Modal Sensor Integration and Edge Intelligence

Abstract

Road traffic accidents caused by drunk driving, driver drowsiness, and reckless vehicle operation constitute one of the leading causes of fatalities and serious injuries worldwide, necessitating the development of intelligent, real-time driver monitoring and intervention systems. This project presents an IoT-enabled Advanced Driver Assistance System (ADAS) built around the ESP-32 microcontroller, integrating four key sensing modalities for comprehensive driver and vehicle monitoring: an alcohol sensor for blood alcohol level detection, an eye-blink sensor for real-time drowsiness assessment, a vibration sensor for accident and road anomaly detection, and a GPS module for continuous vehicle location tracking. Upon detection of any hazardous condition, the ESP-32 processes the sensor inputs and activates appropriate alert and intervention mechanisms, including an LCD display for visual notifications, a buzzer for audible alerts, a DC motor interface for engine control, and an IoT cloud platform for remote monitoring and emergency reporting. A regulated power supply (RPS) ensures stable operation of all system components. The proposed system provides a cost-effective, multi-modal, and real-time safety solution capable of significantly reducing road accidents through proactive intervention and continuous cloud-based fleet monitoring.