Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino

Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino

Jayaresmi J

Assistant Professor at LBS Institute of Technology for Women, and 6Assistant Professor at LBS Institute of Technology for Women

Keywords: Accidents, Alert generation, Driver drowsiness, Prevention, Real time feedback, Timely Warnings

Correspondence to: jayaresmi@lbsitw.ac.in

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Abstract Driver drowsiness is a critical issue in vehicle safety, with statistics showing it to be a significant factor in 10-40 percent of highway accidents. The consequences of falling asleep while driving is severe, leading to increased injury severity and a higher occurrence among sleep-deprived individuals. Drowsiness negatively impacts mental alertness, impairs judgment, and slows reaction time, posing a risk of human error that can result in death or injury. Previous techniques for detecting drowsiness, such as using sensitive electrodes directly attached to the driver's body, proved impractical and invasive.Machine learning alorithms and Arduino has been incorporated for dowsiness detection and alerting. The objective of the Drowsiness Detection System is to prevent accidents by identifying early signs of drowsiness and alerting the drivers. The system aims to enhance driver safety by providing real-time drowsiness feedback.