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- Vol. 4 Issue 2
- Article
Driver Drowsiness Detection
Authors
1234 Shivaji University, Kolhapur, Dr.J.J.Magdum College of engineering, Jaysingpur, India. 5 Assistant Professor, Dept. of Information Technology, Dr.J.J.Magdum College of engineering, Jaysingpur, India
Article Information
- DOI
- No DOI
- Published In
- Volume 4, Issue 2 (2023)
- Published Online
- March-April 2023
- Pages
- 131-134
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
Abstract
View PDFAbstract: Car crashes are the leading cause of death, killing around 1.3 million people every year. Most of these accidents were caused by distracted or drowsy drivers. The construction of highways reduces the margin of error for drivers. Countless people drive long distances on the highway every day. Lack of sleep or distractions, such as phone calls, conversations with passengers, etc. May cause an accident. This system uses convolutional neural networks to effectively assess levels of driver drowsiness and fatigue. The system can be divided into three parts: the model architecture, the mobile components and the website. It was created for logistics and ridesharing companies to track and analyze the performance of their drivers. The system is designed to create a safer environment for drivers and has become a major asset for these companies.
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