IoT-Enabled Driver Fatigue Alcohol Detection and Accident Prevention System for Next Generation Electric Vehicles

Abstract

The rapid growth of electric vehicles (EVs) and smart transportation systems has significantly increased the demand for intelligent driver assistance technologies, with the global EV market expected to exceed 350 million vehicles by 2030 and road accidents accounting for approximately 1.3 million deaths annually worldwide These applications require systems capable of detecting driver fatigue, intoxication, and accidents while providing immediate alerts and remote accessibility. Traditional vehicles lack intelligent monitoring systems, relying heavily on manual observation and delayed response mechanisms, which increases the risk of accidents and reduces the effectiveness of emergency interventions. Furthermore, conventional systems do not provide integrated communication, real-time tracking, or cloud-based data analysis, limiting their ability to support proactive safety measures. To address these challenges, the proposed Smart EV driver assistance system integrates Global System for Mobile (GSM), Global Position System (GPS), and IoT technologies with advanced sensor modules, including eye blink detection, MQ-3 alcohol sensing, and accident detection mechanisms. The system continuously monitors driver behavior and vehicle conditions, and upon detecting abnormalities such as drowsiness, alcohol influence, or collisions, it automatically transmits alert messages with precise GPS coordinates to predefined emergency contacts via GSM networks. The Internet of Things (IoT) connectivity enables real-time data visualization and remote monitoring through web or mobile dashboards, allowing users to track vehicle movement and system status efficiently. This intelligent integration enhances driver safety, ensures rapid emergency response, and supports the development of connected, smart, and reliable electric vehicle ecosystems