Non-Intrusive Detection of Drowsy Driving Based on Eye Tracking Data:
作者： Ali Shahidi Zandi , Azhar Quddus , Laura Prest , Felix J. E. Comeau
关键词:
摘要: … an RF classifier were used for binary identification of the state of vigilance, … machine learning framework for nonintrusive drowsy driving detection using a specific set of 34 eye tracking …
sagepub.com
sci-hub.se 参考文章(33)
Jarek Krajewski, David Sommer, Udo Trutschel, Dave Edwards, Martin Golz, Steering wheel behavior based estimation of fatigue Driving Assessment 2009: 5th International Driving Symposium on Human Factors in Driving Assessment, Training and Vehicle DesignFederal Motor Carrier Safety AdministrationWestern Transportation InstituteNissan Technical Center, North AmericaHonda R&D Americas, IncorporatedUniversity of Iowa, Iowa City5DT, Inc.DriveCam, IncorporatedHFES Surface Transportation Technical GroupUniversity of LeedsLiberty Mutual Research Institute for Safety and HealthRealtime Technologies IncorporatedSeeing MachinesSWERVE Driver TrainingTransportation Research BoardNational Highway Traffic Safety AdministrationUniversity of Minnesota, Minneapolis. ,vol. 5, pp. 118- 124 ,(2017) , 10.17077/DRIVINGASSESSMENT.1311
Melinda L Jackson, Susan Raj, Rodney J Croft, Amie C Hayley, Luke A Downey, Gerard A Kennedy, Mark E Howard, None, Slow eyelid closure as a measure of driver drowsiness and its relationship to performance Traffic Injury Prevention. ,vol. 17, pp. 251- 257 ,(2016) , 10.1080/15389588.2015.1055327
Xuesong Wang, Chuan Xu, Driver drowsiness detection based on non-intrusive metrics considering individual specifics Accident Analysis & Prevention. ,vol. 95, pp. 350- 357 ,(2016) , 10.1016/J.AAP.2015.09.002
A.V. Lazo, P. Rathie, On the entropy of continuous probability distributions (Corresp.) IEEE Transactions on Information Theory. ,vol. 24, pp. 120- 122 ,(1978) , 10.1109/TIT.1978.1055832
Chun-Hsiang Chuang, Chih-Sheng Huang, Li-Wei Ko, Chin-Teng Lin, An EEG-based perceptual function integration network for application to drowsy driving Knowledge Based Systems. ,vol. 80, pp. 143- 152 ,(2015) , 10.1016/J.KNOSYS.2015.01.007
I. Garcia, S. Bronte, L. M. Bergasa, J. Almazan, J. Yebes, Vision-based drowsiness detector for real driving conditions ieee intelligent vehicles symposium. pp. 618- 623 ,(2012) , 10.1109/IVS.2012.6232222
Ron Kohavi, George H. John, Wrappers for feature subset selection Artificial Intelligence. ,vol. 97, pp. 273- 324 ,(1997) , 10.1016/S0004-3702(97)00043-X
Tayyaba Azim, M. Arfan Jaffar, Anwar M. Mirza, Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems soft computing. ,vol. 18, pp. 25- 38 ,(2014) , 10.1016/J.ASOC.2014.01.020
Guosheng Yang, Yingzi Lin, Prabir Bhattacharya, A driver fatigue recognition model based on information fusion and dynamic Bayesian network Information Sciences. ,vol. 180, pp. 1942- 1954 ,(2010) , 10.1016/J.INS.2010.01.011
Xiao Fan, Yanfeng Sun, Baocai Yin, Xiuming Guo, Gabor-based dynamic representation for human fatigue monitoring in facial image sequences Pattern Recognition Letters. ,vol. 31, pp. 234- 243 ,(2010) , 10.1016/J.PATREC.2009.08.014