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DRIVER DROWSINESS DETECTION SYSTEM
ISSN: 2582 - 9130
Publisher: author
DRIVER DROWSINESS DETECTION SYSTEM
Indexed in
Technology and Engineering
ARTICLE-FACTOR
1.3
Article Basics Score: 2
Article Transparency Score: 3
Article Operation Score: 2
Article Articles Score: 3
Article Accessibility Score: 3
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International Category Code (ICC):
ICC-1802
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582383939130
eISSN
:
2582 - 9130
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Abstract
It uses machine learning technology to predict driver conditions and emotions, providing information to improve road safety. This is an artificial intelligence application. Artificial intelligence is a technology that allows systems to automatically learn and improve without being explicitly programmed. The driver's condition can be evaluated by biomarkers, driving behaviour, and the driver's expression. This article presents a comprehensive overview of recent work related to driver drowsiness detection and warning systems. We also present various machine learning methods such as the PERCLOS algorithm, HAAR-based cascade classifier, and Open CV used to determine driver state. Finally, it identifies the challenges facing existing systems and presents relevant research opportunities. Keywords: Artificial Intelligence, Autonomous Vehicle Technology, Drowsiness Detection, Machine Learning.
