{"id":"https://openalex.org/W2981992989","doi":"https://doi.org/10.1109/tvt.2019.2949603","title":"Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks","display_name":"Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks","publication_year":2019,"publication_date":"2019-10-25","ids":{"openalex":"https://openalex.org/W2981992989","doi":"https://doi.org/10.1109/tvt.2019.2949603","mag":"2981992989"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2019.2949603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2019.2949603","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1903.02193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Qin Zou","orcid":"https://orcid.org/0000-0001-7955-0782"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Zou","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7955-0782","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hanwen Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanwen Jiang","raw_affiliation_strings":["Department of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qiyu Dai","orcid":"https://orcid.org/0000-0003-4659-2438"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiyu Dai","raw_affiliation_strings":["Department of Power and Mechanical Engineering, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-4659-2438","affiliations":[{"raw_affiliation_string":"Department of Power and Mechanical Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuanhao Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhao Yue","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Long Chen","orcid":"https://orcid.org/0000-0003-4925-0572"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4925-0572","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":null,"display_name":"Qian Wang","orcid":"https://orcid.org/0000-0002-8967-8525"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8967-8525","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":15.5057,"has_fulltext":false,"cited_by_count":349,"citation_normalized_percentile":{"value":0.99546434,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"69","issue":"1","first_page":"41","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.983299970626831,"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":0.983299970626831,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.005900000222027302,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0015999999595806003,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6825000047683716},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6802999973297119},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6304000020027161},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6290000081062317},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5479000210762024},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5396999716758728},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4715999960899353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7232000231742859},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6825000047683716},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6802999973297119},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6304000020027161},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6290000081062317},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5396999716758728},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5342000126838684},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4142000079154968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3165999948978424},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.26080000400543213}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tvt.2019.2949603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2019.2949603","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1903.02193","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.02193","pdf_url":"https://arxiv.org/pdf/1903.02193","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1903.02193","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.02193","pdf_url":"https://arxiv.org/pdf/1903.02193","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1612684229","display_name":null,"funder_award_id":"2018CFB482","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"},{"id":"https://openalex.org/G3035364503","display_name":null,"funder_award_id":"2042019kf0210","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4387305886","display_name":null,"funder_award_id":"61872277","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4927203819","display_name":"\u955c\u9762\u8f85\u52a9\u7684\u5730\u9762\u4e09\u7ef4\u6fc0\u5149\u626b\u63cf\u7814\u7a76","funder_award_id":"41571437","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G546264426","display_name":null,"funder_award_id":"U1636219","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6106187397","display_name":null,"funder_award_id":"61773414","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7711705859","display_name":null,"funder_award_id":"61822207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1479789543","https://openalex.org/W1536680647","https://openalex.org/W1544151864","https://openalex.org/W1901129140","https://openalex.org/W1968256263","https://openalex.org/W1976362452","https://openalex.org/W2014632812","https://openalex.org/W2031081486","https://openalex.org/W2038270407","https://openalex.org/W2039466015","https://openalex.org/W2070724998","https://openalex.org/W2082747607","https://openalex.org/W2087390872","https://openalex.org/W2092282499","https://openalex.org/W2094263170","https://openalex.org/W2094990266","https://openalex.org/W2106976646","https://openalex.org/W2108598243","https://openalex.org/W2112921976","https://openalex.org/W2117594136","https://openalex.org/W2118545852","https://openalex.org/W2131076267","https://openalex.org/W2155520527","https://openalex.org/W2156128637","https://openalex.org/W2159132531","https://openalex.org/W2160124664","https://openalex.org/W2277132981","https://openalex.org/W2395611524","https://openalex.org/W2478820856","https://openalex.org/W2529429228","https://openalex.org/W2548928501","https://openalex.org/W2560323025","https://openalex.org/W2592152148","https://openalex.org/W2610147486","https://openalex.org/W2612361394","https://openalex.org/W2695874637","https://openalex.org/W2738638456","https://openalex.org/W2779250840","https://openalex.org/W2780740184","https://openalex.org/W2793546227","https://openalex.org/W2795543364","https://openalex.org/W2804070054","https://openalex.org/W2806247261","https://openalex.org/W2889986507","https://openalex.org/W2890657615","https://openalex.org/W2895340898","https://openalex.org/W2899242765","https://openalex.org/W2941750187","https://openalex.org/W2943963440","https://openalex.org/W2944240183","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963881378","https://openalex.org/W2964199920","https://openalex.org/W2964332990","https://openalex.org/W2966218308","https://openalex.org/W2971544778","https://openalex.org/W6603606898","https://openalex.org/W6628877408","https://openalex.org/W6631190155","https://openalex.org/W6635292102","https://openalex.org/W6637373629","https://openalex.org/W6682473269","https://openalex.org/W6726329059","https://openalex.org/W6730548322","https://openalex.org/W6731515422","https://openalex.org/W6738465933","https://openalex.org/W6752610181","https://openalex.org/W6756427901"],"related_works":["https://openalex.org/W2541791370","https://openalex.org/W2035976912","https://openalex.org/W2109974539","https://openalex.org/W2125927971","https://openalex.org/W2771156424","https://openalex.org/W2738084969","https://openalex.org/W1831365897","https://openalex.org/W2042311553","https://openalex.org/W1999222583","https://openalex.org/W2980357211"],"abstract_inverted_index":{"Lane":[0],"detection":[1,23,103],"in":[2,46,82,184,188],"driving":[3,111],"scenes":[4],"is":[5,137],"an":[6],"important":[7],"module":[8],"for":[9,163],"autonomous":[10],"vehicles":[11],"and":[12,40,61,113,126,143,166],"advanced":[13],"driver":[14],"assistance":[15],"systems.":[16],"In":[17,64],"recent":[18],"years,":[19],"many":[20],"sophisticated":[21],"lane":[22,35,76,102,167,185],"methods":[24,30,183],"have":[25],"been":[26],"proposed.":[27],"However,":[28],"most":[29],"focus":[31],"on":[32,71,171],"detecting":[33],"the":[34,72,75,121,127,144,152,160,177,181],"from":[36],"one":[37,83],"single":[38],"image,":[39],"often":[41],"lead":[42],"to":[43],"unsatisfactory":[44],"performance":[45],"handling":[47,189],"some":[48],"extremely-bad":[49],"situations":[50],"such":[51],"as":[52],"heavy":[53],"shadow,":[54],"severe":[55],"mark":[56],"degradation,":[57],"serious":[58],"vehicle":[59],"occlusion,":[60],"so":[62],"on.":[63],"fact,":[65],"lanes":[66],"are":[67,156],"continuous":[68,110,149],"line":[69],"structures":[70],"road.":[73],"Consequently,":[74],"that":[77],"cannot":[78],"be":[79,88],"accurately":[80],"detected":[81],"current":[84],"frame":[85,136],"may":[86],"potentially":[87],"inferred":[89],"out":[90],"by":[91,104,119,139],"incorporating":[92],"information":[93,133],"of":[94,108,134,147,154],"previous":[95],"frames.":[96],"To":[97],"this":[98],"end,":[99],"we":[100],"investigate":[101],"using":[105],"multiple":[106,148],"frames":[107],"a":[109,115,140],"scene,":[112],"propose":[114],"hybrid":[116],"deep":[117],"architecture":[118],"combining":[120],"convolutional":[122],"neural":[123,129],"network":[124,130],"(CNN)":[125],"recurrent":[128],"(RNN).":[131],"Specifically,":[132],"each":[135],"abstracted":[138],"CNN":[141,145],"block,":[142],"features":[146],"frames,":[150],"holding":[151],"property":[153],"time-series,":[155],"then":[157],"fed":[158],"into":[159],"RNN":[161],"block":[162],"feature":[164],"learning":[165],"prediction.":[168],"Extensive":[169],"experiments":[170],"two":[172],"large-scale":[173],"datasets":[174],"demonstrate":[175],"that,":[176],"proposed":[178],"method":[179],"outperforms":[180],"competing":[182],"detection,":[186],"especially":[187],"difficult":[190],"situations.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":51},{"year":2024,"cited_by_count":67},{"year":2023,"cited_by_count":66},{"year":2022,"cited_by_count":71},{"year":2021,"cited_by_count":62},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2019-11-01T00:00:00"}