IoT-Enabled Driver Drowsiness Detection Using Machine Learning

The most important procedure for preventing traffic accidents in recent years, maybe on a global scale, is the identification of sleepy drivers. Every day, over 350 people are killed in traffic accidents, and almost 1,000 more suffer injuries. Recent technological advancements could reduce this tendency by 40%. It is still possible to get these benefits despite significant challenges. This paper develops an intelligent alerting method to prevent accidents caused by drivers falling asleep at the wheel. As part of smart cars, the proposed method with the total capacity prevents sleepy driver impairment automatically. The proposed approach detects drowsiness in analyzing the live streaming of drivers’ videos. Eye Aspect Ratio (EAR) and the Euclidean distance of the eye are used to analyze the input video stream to identify sleepy drivers. Experimental results show that the proposed scheme can lower dangerous accidents and injuries caused by road traffic.