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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM8868

Distracted Driver Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM8868

Volume:

5 |

6 |

Jun 2021

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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

NN

Nikita Negi1

SG2

Shivani Gupta 2

NS

Nayanika Shukla3

TA

Tanvi Attri4

SC

Sharanya Chandran5

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Abstract

In India, the death rate due to traffic accidents is one of the most serious issues. Driver distraction is a factor in about 80% of accidents. We work to create a reliable system for identifying driver distraction. There are various ways to find it, but they take longer. Early distraction detection and communication with the driver are crucial. In order to minimise the danger of accidents and detect this distraction as early as feasible, computer-based applications must be created. The goal is to create a quick and effective distraction detection tool. Some methods that have been proposed include the preprocessing, feature extraction, and classification steps. We employ a two-layer neural network to improve the result’s accuracy. ELM, Softmax, and SVM-ELM are used for feature extraction and classification, respectively. The proposed method’s accuracy is 97.2%, demonstrating its dependability.

KEYWORDS

Driver distraction SVM CNN classification machine learning.

Cite This Article

Nikita Negi1, Shivani Gupta 2, Nayanika Shukla3, Tanvi Attri4, Sharanya Chandran5, "Distracted Driver Detection," International Journal of Scientific Research in Engineering and Management, vol. 5, no. 6, Jun 2021.

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Publication Date

10/4/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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

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