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A Pre-Tranind Model for Driver Drowsiness Detection

EasyChair Preprint no. 10578

8 pagesDate: July 16, 2023


Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behaviour, and the driver’s graphic information. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. The technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination. In this paper, we propose a Resnet (50) pre-trained model for driver drowsiness detection that achieves robust results and reaches 98% accuracy.

Keyphrases: deep learning, Drowsiness Detection, ResNet (50)

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Amira Ahmed},
  title = {A Pre-Tranind Model for Driver Drowsiness Detection},
  howpublished = {EasyChair Preprint no. 10578},

  year = {EasyChair, 2023}}
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