{"id":"https://openalex.org/W2277132981","doi":"https://doi.org/10.1109/tnnls.2016.2522428","title":"Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene","display_name":"Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene","publication_year":2016,"publication_date":"2016-02-16","ids":{"openalex":"https://openalex.org/W2277132981","doi":"https://doi.org/10.1109/tnnls.2016.2522428","mag":"2277132981","pmid":"https://pubmed.ncbi.nlm.nih.gov/26890928"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2016.2522428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2016.2522428","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://opus.lib.uts.edu.au/bitstream/10453/106032/4/TNNLS-2015-P-5585.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100635867","display_name":"Jun Li","orcid":"https://orcid.org/0000-0002-1336-2241"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]},{"id":"https://openalex.org/I4210096034","display_name":"Centre for Quantum Computation and Communication Technology","ror":"https://ror.org/00rnbty21","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1337719021","https://openalex.org/I165143802","https://openalex.org/I2801453606","https://openalex.org/I4210096034","https://openalex.org/I4210132349"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jun Li","raw_affiliation_strings":["Centre for Quantum Computation Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Quantum Computation Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I4210096034","https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101986286","display_name":"Xue Mei","orcid":"https://orcid.org/0000-0002-8237-1539"},"institutions":[{"id":"https://openalex.org/I4391768151","display_name":"Toyota Research Institute","ror":"https://ror.org/04fpkc108","country_code":null,"type":"facility","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4391768151"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xue Mei","raw_affiliation_strings":["Toyota Research Institute, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061971606","display_name":"Danil Prokhorov","orcid":"https://orcid.org/0000-0002-6208-4233"},"institutions":[{"id":"https://openalex.org/I4391768151","display_name":"Toyota Research Institute","ror":"https://ror.org/04fpkc108","country_code":null,"type":"facility","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4391768151"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danil Prokhorov","raw_affiliation_strings":["Toyota Research Institute, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074103823","display_name":"Dacheng Tao","orcid":"https://orcid.org/0000-0001-7225-5449"},"institutions":[{"id":"https://openalex.org/I4210096034","display_name":"Centre for Quantum Computation and Communication Technology","ror":"https://ror.org/00rnbty21","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1337719021","https://openalex.org/I165143802","https://openalex.org/I2801453606","https://openalex.org/I4210096034","https://openalex.org/I4210132349"]},{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dacheng Tao","raw_affiliation_strings":["Centre for Quantum Computation Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Quantum Computation Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I4210096034","https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100635867"],"corresponding_institution_ids":["https://openalex.org/I114017466","https://openalex.org/I4210096034"],"apc_list":null,"apc_paid":null,"fwci":35.9198,"has_fulltext":true,"cited_by_count":432,"citation_normalized_percentile":{"value":0.99855235,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"28","issue":"3","first_page":"690","last_page":"703"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7885744571685791},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.764484167098999},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6943563222885132},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.6155104637145996},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5924108028411865},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5766404271125793},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.575270414352417},{"id":"https://openalex.org/keywords/sensory-cue","display_name":"Sensory cue","score":0.5302391052246094},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5267947316169739},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5089542865753174},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5035430788993835},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43666356801986694},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.42896538972854614},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4183606207370758},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06934481859207153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7885744571685791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.764484167098999},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6943563222885132},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.6155104637145996},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5924108028411865},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5766404271125793},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.575270414352417},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.5302391052246094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5267947316169739},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5089542865753174},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5035430788993835},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43666356801986694},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.42896538972854614},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4183606207370758},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06934481859207153},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2016.2522428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2016.2522428","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:26890928","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26890928","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/106032","is_oa":true,"landing_page_url":"http://hdl.handle.net/10453/106032","pdf_url":"https://opus.lib.uts.edu.au/bitstream/10453/106032/4/TNNLS-2015-P-5585.pdf","source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":{"id":"pmh:oai:opus.lib.uts.edu.au:10453/106032","is_oa":true,"landing_page_url":"http://hdl.handle.net/10453/106032","pdf_url":"https://opus.lib.uts.edu.au/bitstream/10453/106032/4/TNNLS-2015-P-5585.pdf","source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"},"sustainable_development_goals":[{"score":0.4000000059604645,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G5659964068","display_name":null,"funder_award_id":"FT-130101457","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320315934","display_name":"Toyota Research Institute","ror":null},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2277132981.pdf"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W196214544","https://openalex.org/W1515020792","https://openalex.org/W1674799117","https://openalex.org/W1677182931","https://openalex.org/W1685006559","https://openalex.org/W1899504021","https://openalex.org/W1978508564","https://openalex.org/W2010181071","https://openalex.org/W2010522557","https://openalex.org/W2033337358","https://openalex.org/W2048060899","https://openalex.org/W2063756240","https://openalex.org/W2064675550","https://openalex.org/W2065436291","https://openalex.org/W2066624635","https://openalex.org/W2072128103","https://openalex.org/W2076063813","https://openalex.org/W2078566703","https://openalex.org/W2085261163","https://openalex.org/W2089947415","https://openalex.org/W2097117768","https://openalex.org/W2106122512","https://openalex.org/W2110485445","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2116768950","https://openalex.org/W2118545852","https://openalex.org/W2126811778","https://openalex.org/W2129926140","https://openalex.org/W2130325614","https://openalex.org/W2131076267","https://openalex.org/W2136922672","https://openalex.org/W2137097255","https://openalex.org/W2144499799","https://openalex.org/W2146575011","https://openalex.org/W2147527908","https://openalex.org/W2147880316","https://openalex.org/W2150355110","https://openalex.org/W2159132531","https://openalex.org/W2160815625","https://openalex.org/W2161914416","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2167998037","https://openalex.org/W2168356304","https://openalex.org/W2171128212","https://openalex.org/W2178624248","https://openalex.org/W2950179405","https://openalex.org/W2951527505","https://openalex.org/W3102168793","https://openalex.org/W4231109964","https://openalex.org/W4254816979","https://openalex.org/W6607974698","https://openalex.org/W6631636882","https://openalex.org/W6637157234","https://openalex.org/W6637625270","https://openalex.org/W6653248861","https://openalex.org/W6674914833","https://openalex.org/W6676481782","https://openalex.org/W6681794868","https://openalex.org/W6682082992","https://openalex.org/W6682137061","https://openalex.org/W6683825394","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2502138317","https://openalex.org/W2944926427","https://openalex.org/W3093942970"],"abstract_inverted_index":{"Hierarchical":[0],"neural":[1,64,81,174,192],"networks":[2,21,65,82,157],"have":[3,83],"been":[4,84],"shown":[5],"to":[6,114,143,152,180],"be":[7],"effective":[8],"in":[9,49,72,168],"learning":[10],"representative":[11],"image":[12,51],"features":[13],"and":[14,102,107],"recognizing":[15],"object":[16,145],"classes.":[17],"However,":[18],"most":[19],"existing":[20],"combine":[22],"the":[23,43,60,69,73,97,100,103,110,115,136,156,161,182,186],"low/middle":[24],"level":[25],"cues":[26,45,71,141],"for":[27,31,54,68,126],"classification":[28],"without":[29,204],"accounting":[30,67],"any":[32,205],"spatial":[33,137],"structures.":[34,189],"For":[35],"applications":[36],"such":[37],"as":[38],"understanding":[39],"a":[40,89,120],"scene,":[41],"how":[42],"visual":[44,74,128],"are":[46,158],"spatially":[47],"distributed":[48],"an":[50,144],"becomes":[52],"essential":[53],"successful":[55],"analysis.":[56],"This":[57],"paper":[58],"extends":[59],"framework":[61],"of":[62,80,99,109,117,139,164,185],"deep":[63,91],"by":[66,160],"structural":[70],"signals.":[75],"In":[76],"particular,":[77],"two":[78],"kinds":[79],"proposed.":[85],"First,":[86],"we":[87],"develop":[88],"multitask":[90,172],"convolutional":[92,173],"network,":[93],"which":[94],"simultaneously":[95],"detects":[96,195],"presence":[98],"target":[101,111],"geometric":[104,178],"attributes":[105],"(location":[106],"orientation)":[108],"with":[112,135],"respect":[113],"region":[116],"interest.":[118],"Second,":[119],"recurrent":[121,131,191],"neuron":[122],"layer":[123],"is":[124,150],"adopted":[125],"structured":[127],"detection.":[129],"The":[130,171,190],"neurons":[132],"can":[133],"deal":[134],"distribution":[138],"visible":[140],"belonging":[142],"whose":[146],"shape":[147],"or":[148,209],"structure":[149],"difficult":[151],"explicitly":[153],"define.":[154],"Both":[155],"demonstrated":[159],"practical":[162],"task":[163],"detecting":[165],"lane":[166,188,196],"boundaries":[167],"traffic":[169],"scenes.":[170],"network":[175,193],"provides":[176],"auxiliary":[177],"information":[179],"help":[181],"subsequent":[183],"modeling":[184],"given":[187],"automatically":[194],"boundaries,":[197],"including":[198],"those":[199],"areas":[200],"containing":[201],"no":[202],"marks,":[203],"explicit":[206],"prior":[207],"knowledge":[208],"secondary":[210],"modeling.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":39},{"year":2022,"cited_by_count":56},{"year":2021,"cited_by_count":72},{"year":2020,"cited_by_count":74},{"year":2019,"cited_by_count":63},{"year":2018,"cited_by_count":54},{"year":2017,"cited_by_count":17},{"year":2016,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
