{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:31:24Z","timestamp":1776357084307,"version":"3.51.2"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2017,3,1]],"date-time":"2017-03-01T00:00:00Z","timestamp":1488326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"name":"Toyota Research Institute Collaborative Project, North America"},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT-130101457"],"award-info":[{"award-number":["FT-130101457"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2017,3]]},"DOI":"10.1109\/tnnls.2016.2522428","type":"journal-article","created":{"date-parts":[[2016,2,16]],"date-time":"2016-02-16T14:14:08Z","timestamp":1455632048000},"page":"690-703","source":"Crossref","is-referenced-by-count":369,"title":["Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene"],"prefix":"10.1109","volume":"28","author":[{"given":"Jun","family":"Li","sequence":"first","affiliation":[]},{"given":"Xue","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Danil","family":"Prokhorov","sequence":"additional","affiliation":[]},{"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/358669.358692"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630630"},{"key":"ref33","article-title":"Gradient flow in recurrent nets: The difficulty of learning long-term dependencies","author":"hochreiter","year":"2001","journal-title":"A Field Guide to Dynamical Recurrent Neural Networks"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"ref30","first-page":"433","article-title":"Gradient-based learning algorithms for recurrent networks and their computational complexity","author":"williams","year":"1995","journal-title":"Backpropagation Theory Architectures and Applications"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2246835"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.185"},{"key":"ref35","first-page":"1","article-title":"Recurrent models of visual attention","author":"mnih","year":"2014","journal-title":"Proc Adv NIPS"},{"key":"ref34","first-page":"549","article-title":"Multi-dimensional recurrent neural networks","author":"graves","year":"2007","journal-title":"Proc 17th Int Conf Artif Neural Netw"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2002.1007449"},{"key":"ref27","first-page":"23","article-title":"Dynamical neural networks for control","author":"prokhorov","year":"2001","journal-title":"A Field Guide to Dynamical Recurrent Networks"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(03)00127-8"},{"key":"ref2","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv NIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2293637"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0364-0213(90)90002-E"},{"key":"ref23","article-title":"Going deeper with convolutions","author":"szegedy","year":"2014"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2296046"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(90)90044-L"},{"key":"ref10","first-page":"1","article-title":"Greedy layer-wise training of deep networks","author":"bengio","year":"2006","journal-title":"Proc Adv NIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553453"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-012-0315-0"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2308519"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref14","first-page":"1","article-title":"Deep belief networks for phone recognition","author":"mohamed","year":"2009","journal-title":"Proc NIPS Workshop Deep Learn Speech Recogn Relat Applicat"},{"key":"ref15","first-page":"1017","article-title":"Generating text with recurrent neural networks","author":"sutskever","year":"2011","journal-title":"Proc 28th ICML"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2307532"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2183645"},{"key":"ref4","first-page":"3286","article-title":"BING: Binarized normed gradients for objectness estimation at 300 fps","author":"cheng","year":"2014","journal-title":"Proc IEEE Conf CVPR"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.28"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2007.908582"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7443.001.0001","author":"bakir","year":"2007","journal-title":"Predicting Structured Data"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TITS.2003.821339","article-title":"Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving","volume":"4","author":"yim","year":"2003","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/3477.865171"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2008.4621152"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/BMEI.2009.5305737"},{"key":"ref45","first-page":"191","article-title":"A tutorial on energy-based learning","author":"lecun","year":"2006","journal-title":"Predicting Structured Data"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.869595"},{"key":"ref47","article-title":"Supervised sequence labelling with recurrent neural networks","author":"graves","year":"2009"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.167"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-011-0404-2"},{"key":"ref44","first-page":"1","article-title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data","author":"lafferty","year":"2001","journal-title":"Proc 18th ICML"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015444"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/7857118\/07407673.pdf?arnumber=7407673","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:38:40Z","timestamp":1641987520000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7407673\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3]]},"references-count":49,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2016.2522428","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3]]}}}