Driver Sleep and Drowsiness Detection Using Machine Learning

Yerasi Varshitha; Dr. K. Sreenivasa Vijaya Simha

Driver Sleep and Drowsiness Detection Using Machine Learning

Authors: Yerasi Varshitha, Dr. K. Sreenivasa Vijaya Simha

Unique Paper ID: 157151
Volume: 9
Issue: 6
PageNo: 97-102

Keywords: Drowsy driving; fatigue; lane position prediction detection

Abstract:
Human drowsiness or fatigue required various approaches to be detected before or during the driving process. Nowadays, many people have managed to acquire personal vehicles which they use to travel around different regions. Arriving alive and on time is a crucial goal for all drivers en route. Drowsiness can be caused by long driving and lack of adequate rest. Several metrics proven to detect drowsy driving include eye detection and heart rate variability. Driving behavior like lane departure, use of indicators, braking and steering handle could also be used. The objective of this work is to develop drowsiness detection, a prediction system that integrates eye detection, which is the behavioral and physiological approach. The system should keep track of the driver's behavior and concentration while driving and give a voice warning or alarm whenever drowsiness is detected.

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Cite This Article

ISSN: 2349-6002
Volume: 9
Issue: 6
PageNo: 97-102

Driver Sleep and Drowsiness Detection Using Machine Learning

Available:https://ijirt.org/Article?manuscript=157151

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