A Pre-Tranind Model for Driver Drowsiness Detection

EasyChair Preprint 10578

Abstract

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: Drowsiness Detection, ResNet (50), deep learning

@booklet{EasyChair:10578,
  author    = {Amira Ahmed},
  title     = {A Pre-Tranind Model for Driver Drowsiness Detection},
  howpublished = {EasyChair Preprint 10578},
  year      = {EasyChair, 2023}}