Artificial Intelligence-Based Drowsiness Detection

The physical and psychological demands of driving need the highest level of focus, making it the most dangerous employment. The spectrum of accidents related to tiredness in the modern era is complicated. Determining whether driver fatigue plays a role in accidents and the degree of physical and psychological discomfort that the drivers experienced is a difficult task without a tried-and-true method. Hence automatic drowsiness detection has become a major challenge froma research perspective. In this research, drowsiness detection has been implemented using Artificial Intelligence (AI). The method is derived by combining information from the image and data/log to provide a capable process for detecting sleepiness. This is supported by testing using many drivers with diverse environments to make the perfect method.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/nigercon62786.2024.10927319
Citations
1
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.51
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article

Artificial Intelligence-Based Drowsiness Detection

Ayodeji Olalekan Salau, T. Jagadesh, J. Dhanasekar, B. Gomathy et al.
1 citations
Sleep and Work-Related Fatigue
0.51
article

Artificial Intelligence-Based Drowsiness Detection

Ayodeji Olalekan Salau, T. Jagadesh, J. Dhanasekar, B. Gomathy, Kanagasabai Balamurugan, K. Satheesh Kumar, M. P.
article en
1 citations

Abstract

The physical and psychological demands of driving need the highest level of focus, making it the most dangerous employment. The spectrum of accidents related to tiredness in the modern era is complicated. Determining whether driver fatigue plays a role in accidents and the degree of physical and psychological discomfort that the drivers experienced is a difficult task without a tried-and-true method. Hence automatic drowsiness detection has become a major challenge froma research perspective. In this research, drowsiness detection has been implemented using Artificial Intelligence (AI). The method is derived by combining information from the image and data/log to provide a capable process for detecting sleepiness. This is supported by testing using many drivers with diverse environments to make the perfect method.

Afe Babalola University (NG), PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH (IN), KPR Institute of Engineering and Technology (IN), Sri Eshwar College of Engineering
Openalex Percentile: Top 26%
Sleep and Work-Related Fatigue
0.51
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Artificial Intelligence-Based Drowsiness Detection — Ayodeji Olalekan Salau, T. Jagadesh, et al. · (2024) | TGRS Research Map | TGRS