Using long short term memory and convolutional neural networks for driver drowsiness detection-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Using long short term memory and convolutional neural networks for driver drowsiness detection

Published:2021-06 Issue: Volume:156 Page:106107
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Quddus Azhar,Shahidi Zandi Ali,Prest Laura,Comeau Felix J.E.

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety, Risk, Reliability and Quality,Human Factors and Ergonomics

Reference41 articles.

1. Fully automated real time fatigue detection of drivers through fuzzy expert systems;Azim,2014

2. Real-time system for monitoring driver vigilance;Bergasa;IEEE Trans. Intell. Transp. Syst.,2006

3. Acute sleep deprivation;Bonnet,2011

4. An EEG-based perceptual function integration network for application to drowsy driving. Knowledge-based systems;Chuang;Elsevier B.V.,2015

5. PERCLOS : A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance;Dinges,1998

Cited by 51 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictability of driver’s stop/go decisions at flashing-light-controlled grade crossings by coupling functional brain network and deep learning methods;Transportation Research Part F: Traffic Psychology and Behaviour;2024-11

2. Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data;Accident Analysis & Prevention;2024-09

3. Multi-Index Driver Drowsiness Detection Method Based on Driver’s Facial Recognition Using Haar Features and Histograms of Oriented Gradients;Sensors;2024-08-31

4. Attention-Guided Multiscale Convolutional Neural Network for Driving Fatigue Detection;IEEE Sensors Journal;2024-07-15

5. Advanced detection techniques for driver drowsiness: a comprehensive review of machine learning, deep learning, and physiological approaches;Multimedia Tools and Applications;2024-07-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3