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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM27927

Real Time Prevention of Driver Fatigue and Exhaustion Using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM27927

Volume:

8 |

1 |

Jan 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM27927

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PAK

Prof. Ankita Kotalwar

PD

Parth Dongare

MI

Mushir Inamdar

YD

Yash Dhomse

SG

Samruddhi Gadekar

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Abstract

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. If advanced warning systems were in place to alert drowsy drivers, it could potentially prevent numerous fatal accidents. There are a number of drowsiness detection technologies that can be used to check for signs of inattention while driving and alert the driver. Self-driving cars must have sensors that can tell if a driver is drowsy, irritated, or going through dramatic swings in their emotions. So far, various additional alert systems have been proposed to help prevent drowsy driving. These systems sound an alarm when they notice a major lane departure, a change in the headway distance, or other symptoms of poor driving performance. Other warning signs include increased head wobble or prolonged eye closure duration. These systems typically identify such behaviour after sleepy driving has started. This piece explores numerous ways in which Driver Drowsiness Detection Systems can be applied in automobiles to address this particular concern.

KEYWORDS

Drowsiness-Detection Driver-Fatigue Computer Vision Real Time Survey

Cite This Article

Prof. Ankita Kotalwar, Parth Dongare, Mushir Inamdar, Yash Dhomse, Samruddhi Gadekar, "Real Time Prevention of Driver Fatigue and Exhaustion Using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 1, Jan 2024. https://doi.org/10.55041/IJSREM27927

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Publication Date

12/31/45305

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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