Real-Time Low-Power Anti-Sleep and Alcohol Detection System for Safer Driving

Alcohol reduces the level of awareness a driver needs to drive safely. The vision of a drunk driver is blurry or even double vision, and this leads to many problems. This paper presents the development of an IoT-based Anti-Sleep and Alcohol detection algorithm, Low Power Detection Time Operation (LPDTO), to enhance road safety by preventing accidents caused by impaired driving. The proposed method integrates advanced sensors to monitor the driver's alcohol consumption and signs of drowsiness in real time; it uses a microcontroller to set specific Blood Alcohol Content (BAC) thresholds and triggers alerts when these limits are exceeded. Additionally, it employs facial recognition technology to assess driver fatigue by analyzing eye movement patterns and facial expressions; it contributes to low power consumption and real-time monitoring. The results showed the LPDTO algorithm decreased the total average detection response time to 16.242 milliseconds, the total average alert response time to 12.93 milliseconds, and sleep detection accuracy to 98.66%, reducing the incidence of drunk driving-related road accidents by reducing accidents occurring in the city's crowded streets by early detection of drowsiness and alcohol in drivers, and also enhancing the accountability of drivers by providing a traceable record of their behavior.