Minimum System Design for Identifying Drunken Drivers to Prevent Road Accidents

Abstract

The main factors causing the majority of road accidents are fatigue and vertigo. Currently, the most important method to prevent any road accidents, perhaps globally, is the detection of drivers who are dizzy. The human body naturally experiences fatigue for a variety of reasons. Most of the drivers using heavy transport vehicles, trucks and buses are facing life threat because of this. Therefore, creating a strong alarm system is necessary to anticipate and prevent the major mishaps. In this paper an IoT-based drowsiness monitoring technique is implemented to avoid these mishaps. In this paper an alarm system for sleepy drivers has been developed using various sensors and IOT technology. In order to detect drowsiness, the Eye Aspect Ratio (EAR) and Lip Aspect Ratio (LAR) are computed. When the EAR falls below the threshold value, it is regarded as eye blinking. If the system notices more than three blinks of the eyes, buzzer will give sound to alert the driver.