Driver Drowsiness Detection System using Machine Learning

DOI

No DOI

Published In

Volume 3, Issue 6 (2022)

Published Online

November-December 2022

Pages

77-79

Article Type

Research Article

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Abstract: In today's world, tiredness is one of the leading factors in traffic accidents, many of which result in fatalities. According to statistics, drowsy driving causes the majority of traffic accidents, which often result in fatalities and serious injuries. Because of this, numerous experiments have been conducted on creating software that can identify driver drowsiness and warn them before a serious mistake is made. Some of the more popular techniques create their own systems using techniques from the automotive industry. But other elements like the design of the road, the type of vehicle, and the ability to drive using the driver's wheel heavily influenced these conventional criteria. In order to monitor a driver's drowsiness, certain techniques use psychological methods, which frequently yield the most precise and reliable results. These methods are expensive, though, because electrodes must be applied to the head and torso. To generate results, the model is provided with a sizable database of closed and open eyes. Every time the driver is observed to be sleepy, Buzz alerts the driver. In our model, we employ a typical forward-facing smartphone camera and use the data we have collected to provide results for our website.