{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:27:20Z","timestamp":1770352040619,"version":"3.49.0"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Transportation and Logistics Research and Development Program"},{"DOI":"10.13039\/501100003565","name":"Ministry of Land, Infrastructure, and Transport of the Korean Government","doi-asserted-by":"publisher","award":["18TLRP-B131486-02"],"award-info":[{"award-number":["18TLRP-B131486-02"]}],"id":[{"id":"10.13039\/501100003565","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3032344","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T17:30:35Z","timestamp":1603301435000},"page":"191138-191151","source":"Crossref","is-referenced-by-count":40,"title":["Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-6839","authenticated-orcid":false,"given":"Chaoyun","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Woojin","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2442-0080","authenticated-orcid":false,"given":"Daesub","family":"Yoon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1037-0158","authenticated-orcid":false,"given":"Paul","family":"Patras","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","author":"simonyan","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(81)90024-2"},{"key":"ref33","year":"2019","journal-title":"State farm distracted driver detection"},{"key":"ref32","article-title":"Real-time distracted driver posture classification","author":"abouelnaga","year":"2017","journal-title":"arXiv 1706 09498"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3154448.3154452"},{"key":"ref37","article-title":"Average vehicle occupancy in us remains unchanged from 2009 to 2017","author":"congress","year":"2018"},{"key":"ref36","author":"bradski","year":"2008","journal-title":"Learning OpenCV Computer Vision With the OpenCV Library"},{"key":"ref35","article-title":"A multimodal dataset for various forms of distracted driving","volume":"4","author":"taamneh","year":"2017","journal-title":"Data Science Journal"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/s18020456"},{"key":"ref28","first-page":"46","article-title":"Multiple scale faster-RCNN approach to Driver&#x2019;s cell-phone usage and hands on steering wheel detection","author":"le","year":"2016","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit Workshops (CVPRW)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11758-4_28"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795622"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1037\/e363942004-001","article-title":"The role of driver distraction in traffic crashes","author":"stutts","year":"2001"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2506602"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2018.5172"},{"key":"ref22","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE-Berlin.2018.8576183"},{"key":"ref24","year":"2017","journal-title":"Driverless Cars Will Be Part of a $7 Trillion Market by 2050"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2496157"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/s16111805"},{"key":"ref50","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume":"16","author":"abadi","year":"2016","journal-title":"Proc OSDI"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3129391"},{"key":"ref55","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref54","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/12.565590"},{"key":"ref52","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2873901"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587730"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2873900"},{"key":"ref13","article-title":"Deep learning in mobile and wireless networking: A survey","author":"zhang","year":"2018","journal-title":"arXiv 1803 04311"},{"key":"ref14","year":"2018","journal-title":"The NVIDIA DRIVE AGX Takes Advantage of Breakthrough Technologies and the Power of AI to Enable New Levels of Autonomous Driving"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-019-01589-z"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2908425"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917460"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/BigMM.2019.00-28"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SSIAI.2018.8470309"},{"key":"ref4","year":"2015","journal-title":"Connected and Autonomous Vehicles-The UK Economic Opportunity"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2462084"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2007.4379556"},{"key":"ref5","year":"2016","journal-title":"Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICEICE.2017.8191851"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906654"},{"key":"ref46","first-page":"550","article-title":"Residual networks behave like ensembles of relatively shallow networks","author":"veit","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3209582.3209606"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref47","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref42","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","first-page":"971","article-title":"Self-normalizing neural networks","author":"klambauer","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09233399.pdf?arnumber=9233399","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T01:09:03Z","timestamp":1641949743000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9233399\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3032344","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}