{"id":"https://openalex.org/W4226543885","doi":"https://doi.org/10.1109/tits.2022.3160501","title":"Action-State Joint Learning-Based Vehicle Taillight Recognition in Diverse Actual Traffic Scenes","display_name":"Action-State Joint Learning-Based Vehicle Taillight Recognition in Diverse Actual Traffic Scenes","publication_year":2022,"publication_date":"2022-04-13","ids":{"openalex":"https://openalex.org/W4226543885","doi":"https://doi.org/10.1109/tits.2022.3160501"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2022.3160501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3160501","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029808668","display_name":"Wenjie Song","orcid":"https://orcid.org/0000-0002-4121-908X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjie Song","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4121-908X","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101889464","display_name":"Shixian Liu","orcid":"https://orcid.org/0000-0003-1768-7913"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shixian Liu","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038062521","display_name":"Ting Zhang","orcid":"https://orcid.org/0000-0002-7014-4670"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Zhang","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062124960","display_name":"Yi Yang","orcid":"https://orcid.org/0000-0003-3964-2433"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Yang","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3964-2433","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091459774","display_name":"Mengyin Fu","orcid":"https://orcid.org/0000-0002-5520-7127"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengyin Fu","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China","School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6813,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.65487511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"23","issue":"10","first_page":"18088","last_page":"18099"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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":0.9991000294685364,"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":0.998199999332428,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.5904648900032043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5779522657394409},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4836079180240631},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.47549542784690857},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.47217291593551636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3668358027935028},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3443062901496887},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3351702094078064},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3075393736362457},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.0706009566783905}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5904648900032043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5779522657394409},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4836079180240631},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.47549542784690857},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.47217291593551636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3668358027935028},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3443062901496887},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3351702094078064},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3075393736362457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0706009566783905},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2022.3160501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3160501","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2217668812","display_name":null,"funder_award_id":"61903034","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5448203614","display_name":null,"funder_award_id":"61973034","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5583208626","display_name":null,"funder_award_id":"IRT-16R06","funder_id":"https://openalex.org/F4320338204","funder_display_name":"Program for Changjiang Scholars and Innovative Research Team in University"},{"id":"https://openalex.org/G6554935138","display_name":null,"funder_award_id":"T2014224","funder_id":"https://openalex.org/F4320338204","funder_display_name":"Program for Changjiang Scholars and Innovative Research Team in University"},{"id":"https://openalex.org/G7914619715","display_name":null,"funder_award_id":"U1913203","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/F4320327514","display_name":"Beijing Institute of Technology Research Fund Program for Young Scholars","ror":null},{"id":"https://openalex.org/F4320338204","display_name":"Program for Changjiang Scholars and Innovative Research Team in University","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1591801644","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1940872118","https://openalex.org/W1947481528","https://openalex.org/W1983473365","https://openalex.org/W2012653714","https://openalex.org/W2047177684","https://openalex.org/W2077964287","https://openalex.org/W2078783643","https://openalex.org/W2088371259","https://openalex.org/W2118045339","https://openalex.org/W2133059825","https://openalex.org/W2133564696","https://openalex.org/W2145786074","https://openalex.org/W2194775991","https://openalex.org/W2291627510","https://openalex.org/W2515328799","https://openalex.org/W2553594924","https://openalex.org/W2563859304","https://openalex.org/W2603203130","https://openalex.org/W2748316749","https://openalex.org/W2769122686","https://openalex.org/W2794383732","https://openalex.org/W2914076857","https://openalex.org/W2942950293","https://openalex.org/W2963037989","https://openalex.org/W2963881378","https://openalex.org/W2968942844","https://openalex.org/W2970618130","https://openalex.org/W4293584584","https://openalex.org/W4294557331","https://openalex.org/W6635446068","https://openalex.org/W6637373629","https://openalex.org/W6640362995","https://openalex.org/W6674581380","https://openalex.org/W6679434410","https://openalex.org/W6682082992","https://openalex.org/W6682864246","https://openalex.org/W6684191040","https://openalex.org/W6684983439","https://openalex.org/W6696672603","https://openalex.org/W6745960190","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W1996130883","https://openalex.org/W2748574964","https://openalex.org/W2888483922","https://openalex.org/W4396737233","https://openalex.org/W2367747139","https://openalex.org/W4391102217","https://openalex.org/W2961085424","https://openalex.org/W2566187525","https://openalex.org/W2566334511","https://openalex.org/W2367150592"],"abstract_inverted_index":{"As":[0],"the":[1,77,131,152,186,194,199],"vital":[2],"factor":[3],"of":[4,27,79,134,154],"vehicle":[5,10,72,107],"behavior":[6],"understanding":[7],"and":[8,49,82,90,114,137,163,243,252],"prediction,":[9],"taillight":[11,47,73,87,141,166,173,201],"recognition":[12,74],"is":[13,110,126,158,182],"an":[14,68,178],"important":[15],"technology":[16],"for":[17,172],"autonomous":[18],"driving,":[19],"especially":[20],"in":[21,33,60,101,245],"diverse":[22],"actual":[23,103],"traffic":[24,30],"scenes":[25],"full":[26],"dynamic":[28],"interactive":[29],"participants.":[31],"However,":[32],"practical":[34],"application,":[35],"it":[36],"always":[37],"faces":[38],"many":[39],"challenges,":[40],"such":[41],"as":[42,112],"\u2018variable":[43],"lighting":[44],"conditions\u2019,":[45],"\u2018non-uniform":[46],"standards\u2019":[48],"\u2018random":[50],"relative":[51],"observation":[52],"pose\u2019,":[53],"which":[54,84],"lead":[55],"to":[56,128,168,184,248],"few":[57],"mature":[58],"solutions":[59],"current":[61],"common":[62],"autopilot":[63],"systems.":[64],"This":[65],"work":[66,247],"proposes":[67],"action-state":[69],"joint":[70],"learning-based":[71],"method":[75],"on":[76],"basis":[78],"vehicles":[80],"detection":[81],"tracking,":[83],"takes":[85],"both":[86],"state":[88,174,202],"features":[89,93,133,171],"time":[91],"series":[92],"into":[94,116,143],"account,":[95],"consequently":[96],"getting":[97],"practicable":[98],"results":[99],"even":[100],"complex":[102],"scenes.":[104],"In":[105],"detail,":[106],"tracking":[108],"sequence":[109,175],"used":[111,183],"input":[113],"split":[115],"pieces":[117,191],"through":[118,160],"a":[119,123],"sliding":[120],"window.":[121],"Then,":[122],"CNN-LSTM":[124],"model":[125,181],"applied":[127],"simultaneously":[129],"identify":[130],"action":[132],"brake":[135,156],"lights":[136],"turn":[138],"signals,":[139],"dividing":[140],"actions":[142,167],"five":[144],"categories:":[145],"None,":[146],"Brake_on,":[147],"Brake_off,":[148],"Left_turn,":[149],"Right_turn.":[150],"Next,":[151],"brightness":[153],"high-position":[155],"light":[157],"extracted":[159],"semantic":[161],"segmentation":[162],"combined":[164],"with":[165],"form":[169],"higher-level":[170,195],"analysis.":[176],"Finally,":[177],"undirected":[179],"graph":[180],"establish":[185],"long-term":[187],"dependence":[188],"between":[189],"successive":[190],"by":[192],"analysing":[193],"features,":[196],"thus":[197],"inferring":[198],"continuous":[200],"into:":[203],"<inline-formula":[204,211,218,225],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[205,212,219,226],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[206,213,220,227],"<tex-math":[207,214,221,228],"notation=\"LaTeX\">$off$":[208],"</tex-math></inline-formula>":[209,216,223,230],",":[210,217,224],"notation=\"LaTeX\">$brake$":[215],"notation=\"LaTeX\">$left$":[222],"notation=\"LaTeX\">$right$":[229],".":[231],"Datasets":[232],"including":[233],"daytime,":[234],"nighttime,":[235],"congested":[236],"road,":[237],"highway,":[238],"etc.":[239],"were":[240],"collected,":[241],"tested":[242],"published":[244],"our":[246],"demonstrate":[249],"its":[250],"effectiveness":[251],"practicability.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}