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Lang and H. Qi, \u201cThe study of driver fatigue monitor algorithm combined PERCLOS and AECS,\u201d International Conference on Computer Science and Software Engineering (CASCON), pp.349-352, 2008. 10.1109\/csse.2008.771","DOI":"10.1109\/CSSE.2008.771"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M. Omidyeganeh, A. Javadtalab, and S. Shirmohammadi, \u201cIntelligent driver drowsiness detection through fusion of yawning and eye closure,\u201d IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), pp.1-6, 2011. 10.1109\/vecims.2011.6053857","DOI":"10.1109\/VECIMS.2011.6053857"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] F. Zhang, J. Su, L. Geng, and Z. Xiao, \u201cDriver fatigue detection based on eye state recognition,\u201d International Conference on Machine Vision and Information Technology (CMVIT), pp.105-110, 2017. 10.1109\/cmvit.2017.25","DOI":"10.1109\/CMVIT.2017.25"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] T.-H. Shih and C.-T. Hsu, \u201cMSTN: multistage spatial-temporal network for driver drowsiness detection,\u201d Asian Conference on Computer Vision (ACCV) Workshops, vol.10118, pp.146-153, 2016. 10.1007\/978-3-319-54526-4_11","DOI":"10.1007\/978-3-319-54526-4_11"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] X.-P. Huynh, S.-M. Park, and Y.-G. Kim, \u201cDetection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine,\u201d Asian Conference on Computer Vision (ACCV) Workshops, vol.10118, pp.134-145, 2016. 10.1007\/978-3-319-54526-4_10","DOI":"10.1007\/978-3-319-54526-4_10"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] B. Reddy, Y.-H. Kim, S. Yun, C. Seo, and J. Jang, \u201cReal-time driver drowsiness detection for embedded system using model compression of deep neural networks,\u201d IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.438-445, 2017. 10.1109\/cvprw.2017.59","DOI":"10.1109\/CVPRW.2017.59"},{"key":"7","unstructured":"[7] W.W. Wierwille, S.S. Wreggit, C. Kirn, L.A. Ellsworth, and R.J. Fairbanks, \u201cResearch on vehicle-based driver status\/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness. final report,\u201d National Highway Traffic Safety Administration, no.DOT HS 808 247, 1994."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M. Tsujikawa, Y. Onishi, Y. Kiuchi, T. Ogatsu, A. Nishino, and S. Hashimoto, \u201cDrowsiness estimation from low-frame-rate facial videos using eyelid variability features,\u201d International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.5203-5206, 2018. 10.1109\/embc.2018.8513470","DOI":"10.1109\/EMBC.2018.8513470"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J.W. Baek, B.-G. Han, K.-J. Kim, Y.-S. Chung, and S.-I. Lee, \u201cReal-time drowsiness detection algorithm for driver state monitoring systems,\u201d International Conference on Ubiquitous and Future Networks (ICUFN), pp.73-75, 2018. 10.1109\/icufn.2018.8436988","DOI":"10.1109\/ICUFN.2018.8436988"},{"key":"10","unstructured":"[10] J. Lyu, Z. Yuan, and D. Chen, \u201cLong-term multi-granularity deep framework for driver drowsiness detection,\u201d arXiv preprint arXiv:1801.02325, 2018."},{"key":"11","unstructured":"[11] T. Nakamura, A. Maejima, and S. Morishima, \u201cDriver drowsiness estimation from facial expression features computer vision feature investigation using a cg model,\u201d International Conference on Computer Vision Theory and Applications (VISAPP), pp.207-214, 2014. 10.5220\/0004648902070214"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M. Sun, M. Tsujikawa, Y. Onishi, X. Ma, A. Nishino, and S. Hashimoto, \u201cA neural-network-based investigation of eye-related movements for accurate drowsiness estimation,\u201d International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.5207-5210, 2018. 10.1109\/embc.2018.8513491","DOI":"10.1109\/EMBC.2018.8513491"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J. Krajewski, D. Sommer, U. Trutschel, D. Edwards, and M. Golz, \u201cSteering wheel behavior based estimation of fatigue,\u201d International Driving Symposium on Human Factors in Driving Assessment, Training and Vehicle Design, pp.118-124, 2009. 10.17077\/drivingassessment.1311","DOI":"10.17077\/drivingassessment.1311"},{"key":"14","unstructured":"[14] H. Malik, F. Naeem, Z. Zuberi, and R. ul Haq, \u201cVision based driving simulation,\u201d International Conference on Cyberworlds (CW), pp.255-259, 2004. 10.1109\/cw.2004.68"},{"key":"15","unstructured":"[15] R.F. Knipling and W.W. Wierwille, \u201cVehicle-based drowsy driver detection: Current status and future prospects,\u201d The Intelligent Vehicle-Highway Society of America (IVHS America), 1994."},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] Z. Mardi, S.N.M. Ashtiani, and M. Mikaili, \u201cEeg-based drowsiness detection for safe driving using chaotic features and statistical tests,\u201d Journal of medical signals and sensors, vol.1, no.2, pp.130-137, 2011. 10.4103\/2228-7477.95297","DOI":"10.4103\/2228-7477.95297"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M.V.M. Yeo, X. Li, K. Shen, and E.P.V. Wilder-Smith, \u201cCan SVM be used for automatic EEG detection of drowsiness during car driving?,\u201d Safety Science, vol.47, no.1, pp.115-124, 2009. 10.1016\/j.ssci.2008.01.007","DOI":"10.1016\/j.ssci.2008.01.007"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] C.-T. Lin, C.-J. Chang, B.-S. Lin, S.-H. Hung, C.-F. Chao, and I.-J. Wang, \u201cA real-time wireless brain-computer interface system for drowsiness detection,\u201d IEEE Transactions on Biomedical Circuits and Systems, vol.4, no.4, pp.214-222, 2010. 10.1109\/tbcas.2010.2046415","DOI":"10.1109\/TBCAS.2010.2046415"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] C.-T. Lin, L.-W. Ko, I.-F. Chung, T.-Y. Huang, Y.-C. Chen, T.-P. Jung, and S.-F. Liang, \u201cAdaptive eeg-based alertness estimation system by using ica-based fuzzy neural networks,\u201d IEEE Transactions on Circuits and Systems I: Regular Papers, vol.53, no.11, pp.2469-2476, 2006. 10.1109\/tcsi.2006.884408","DOI":"10.1109\/TCSI.2006.884408"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] A. Picot, S. Charbonnier, and A. Caplier, \u201cDrowsiness detection based on visual signs: blinking analysis based on high frame rate video,\u201d IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp.801-804, 2010. 10.1109\/imtc.2010.5488257","DOI":"10.1109\/IMTC.2010.5488257"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] H. Albalawi and X. Li, \u201cSingle-channel real-time drowsiness detection based on electroencephalography,\u201d International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.98-101, 2018. 10.1109\/embc.2018.8512205","DOI":"10.1109\/EMBC.2018.8512205"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] A. Tsuchida, M.S. Bhuiyan, and K. Oguri, \u201cEstimation of drowsiness level based on eyelid closure and heart rate variability,\u201d International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2543-2546, 2009. 10.1109\/iembs.2009.5334766","DOI":"10.1109\/IEMBS.2009.5334766"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] A. Tsuchida, M.S. Bhuiyan, and K. Oguri, \u201cEstimation of drivers' drowsiness level using a neural network based error correcting output coding method,\u201d International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1887-1892, 2010. 10.1109\/itsc.2010.5624964","DOI":"10.1109\/ITSC.2010.5624964"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] E. Zilberg, Z.M. Xu, D. Burton, M. Karrar, and S. Lal, \u201cMethodology and initial analysis results for development of non-invasive and hybrid driver drowsiness detection systems,\u201d International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless), p.16, 2007. 10.1109\/auswireless.2007.44","DOI":"10.1109\/AUSWIRELESS.2007.44"},{"key":"25","unstructured":"[25] D. Smilkov, N. Thorat, B. Kim, F. Vi\u00e9gas, and M. Wattenberg, \u201cSmoothgrad: removing noise by adding noise,\u201d arXiv preprint arXiv:1706.03825, 2017."},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] W. Zhang, B. Cheng, and Y. Lin, \u201cDriver drowsiness recognition based on computer vision technology,\u201d Tsinghua Science and Technology, vol.17, no.3, pp.354-362, 2012. 10.1109\/tst.2012.6216768","DOI":"10.1109\/TST.2012.6216768"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] F.A. Gers, J. Schmidhuber, and F. Cummins, \u201cLearning to forget: Continual prediction with LSTM,\u201d Neural Computation, vol.12, no.10, pp.2451-2471, 2000. 10.1162\/089976600300015015","DOI":"10.1162\/089976600300015015"},{"key":"28","unstructured":"[28] W. Zaremba, I. Sutskever, and O. Vinyals, \u201cRecurrent neural network regularization,\u201d arXiv preprint arXiv:1409.2329, 2014."},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] S. Ji, W. Xu, M. Yang, and K. Yu, \u201c3D convolutional neural networks for human action recognition,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, no.1, pp.221-231, 2013. 10.1109\/tpami.2012.59","DOI":"10.1109\/TPAMI.2012.59"},{"key":"30","unstructured":"[30] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"31","unstructured":"[31] \u201cOKAO Vision.\u201d https:\/\/plus-sensing.omron.com\/."},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"33","unstructured":"[33] J.Y.-H. Ng, M.J. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. 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