Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino

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.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-02-21
DOI
https://doi.org/10.55041/ijsrem41742
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
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article

Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino

J Jayaresmi
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Sleep and Work-Related Fatigue
article

Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino

J Jayaresmi
article en

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.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(02)
Openalex Percentile: Top 97%
Sleep and Work-Related Fatigue
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