{"id":"https://openalex.org/W2948202027","doi":"https://doi.org/10.1109/ivs.2019.8814278","title":"An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition","display_name":"An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2948202027","doi":"https://doi.org/10.1109/ivs.2019.8814278","mag":"2948202027"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2019.8814278","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8814278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1906.03683","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103235511","display_name":"Kuan-Hui Lee","orcid":"https://orcid.org/0000-0001-5109-4797"},"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":"Kuan-Hui Lee","raw_affiliation_strings":["Toyota Research Institute, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, CA, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080675230","display_name":"Takaaki Tagawa","orcid":null},"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":"Takaaki Tagawa","raw_affiliation_strings":["Toyota Research Institute, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, CA, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028292707","display_name":"Jia-En M. Pan","orcid":null},"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":"Jia-En M. Pan","raw_affiliation_strings":["Toyota Research Institute, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, CA, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075018873","display_name":"Adrien Gaidon","orcid":"https://orcid.org/0000-0001-8820-550X"},"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":"Adrien Gaidon","raw_affiliation_strings":["Toyota Research Institute, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, CA, USA","institution_ids":["https://openalex.org/I4391768151"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022459396","display_name":"Bertrand Douillard","orcid":null},"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":"Bertrand Douillard","raw_affiliation_strings":["Toyota Research Institute, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Research Institute, CA, USA","institution_ids":["https://openalex.org/I4391768151"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4391768151"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2365","last_page":"2370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7557847499847412},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7503597140312195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6604313850402832},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6450216174125671},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5753028392791748},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5038644671440125},{"id":"https://openalex.org/keywords/brake","display_name":"Brake","score":0.498607873916626},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.49587973952293396},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.45094043016433716},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4339805245399475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3693352937698364},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3609306514263153},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09824618697166443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7557847499847412},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7503597140312195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6604313850402832},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6450216174125671},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5753028392791748},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5038644671440125},{"id":"https://openalex.org/C2780999251","wikidata":"https://www.wikidata.org/wiki/Q17022503","display_name":"Brake","level":2,"score":0.498607873916626},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.49587973952293396},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.45094043016433716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4339805245399475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693352937698364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3609306514263153},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09824618697166443},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/ivs.2019.8814278","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8814278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1906.03683","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.03683","pdf_url":"https://arxiv.org/pdf/1906.03683","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},{"id":"mag:2948202027","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1906.03683","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1906.03683","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1906.03683","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:2988584091","is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ivs/ivs2019.html#LeeTPGD19","pdf_url":null,"source":{"id":"https://openalex.org/S4363604372","display_name":"IV","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IV","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1906.03683","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.03683","pdf_url":"https://arxiv.org/pdf/1906.03683","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2948202027.pdf","grobid_xml":"https://content.openalex.org/works/W2948202027.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1484210532","https://openalex.org/W1514535095","https://openalex.org/W1522734439","https://openalex.org/W1586939924","https://openalex.org/W1902237438","https://openalex.org/W1903029394","https://openalex.org/W1929630580","https://openalex.org/W1947481528","https://openalex.org/W1959445404","https://openalex.org/W1983473365","https://openalex.org/W2047177684","https://openalex.org/W2078783643","https://openalex.org/W2084298397","https://openalex.org/W2090518410","https://openalex.org/W2118045339","https://openalex.org/W2121056381","https://openalex.org/W2124651605","https://openalex.org/W2130942839","https://openalex.org/W2145786074","https://openalex.org/W2147527908","https://openalex.org/W2156303437","https://openalex.org/W2172806452","https://openalex.org/W2194775991","https://openalex.org/W2220750102","https://openalex.org/W2292288263","https://openalex.org/W2342662179","https://openalex.org/W2515328799","https://openalex.org/W2556782416","https://openalex.org/W2563859304","https://openalex.org/W2607101357","https://openalex.org/W2613904329","https://openalex.org/W2758107681","https://openalex.org/W2794383732","https://openalex.org/W2896249524","https://openalex.org/W2909676666","https://openalex.org/W2950178297","https://openalex.org/W2952587893","https://openalex.org/W2963524571","https://openalex.org/W2964308564","https://openalex.org/W6630875275","https://openalex.org/W6679436768","https://openalex.org/W6682137061","https://openalex.org/W6682864246","https://openalex.org/W6730028046","https://openalex.org/W6731120476","https://openalex.org/W6737046247"],"related_works":["https://openalex.org/W2988584091","https://openalex.org/W2556782416","https://openalex.org/W3118951565","https://openalex.org/W3125815523","https://openalex.org/W3160300648","https://openalex.org/W3005796607","https://openalex.org/W2770446450","https://openalex.org/W3129002371","https://openalex.org/W2592659495","https://openalex.org/W3119265355","https://openalex.org/W2914898847","https://openalex.org/W3186814128","https://openalex.org/W3007398289","https://openalex.org/W3028451190","https://openalex.org/W3128675159","https://openalex.org/W3137872188","https://openalex.org/W2912100719","https://openalex.org/W2900883569","https://openalex.org/W3185192194","https://openalex.org/W3120853220"],"abstract_inverted_index":{"Vehicle":[0,119],"taillight":[1,131],"recognition":[2],"is":[3,102],"an":[4,29],"important":[5],"application":[6],"for":[7,11,129],"automated":[8],"driving,":[9],"especially":[10],"intent":[12],"prediction":[13],"of":[14,20,46,108,113,126],"ado":[15],"vehicles":[16],"and":[17,40,62,83,97],"trajectory":[18],"planning":[19],"the":[21,87,106,109,116,124],"ego":[22],"vehicle.":[23],"In":[24],"this":[25],"work,":[26],"we":[27,76],"propose":[28],"end-to-end":[30],"deep":[31],"learning":[32],"framework":[33],"to":[34,58,71,91,104],"recognize":[35],"taillights,":[36],"i.e.":[37],"rear":[38],"turn":[39],"brake":[41],"signals,":[42],"from":[43],"a":[44,53,65],"sequence":[45],"images.":[47],"The":[48],"proposed":[49],"method":[50,101],"starts":[51],"with":[52],"Convolutional":[54],"Neural":[55],"Network":[56],"(CNN)":[57],"extract":[59],"spatial":[60,82,96],"features,":[61],"then":[63],"applies":[64],"Long":[66],"Short-Term":[67],"Memory":[68],"network":[69],"(LSTM)":[70],"learn":[72,90],"temporal":[73,84,98],"dependencies.":[74],"Furthermore,":[75],"integrate":[77],"attention":[78,88,127],"models":[79,89,128],"in":[80,111],"both":[81,95],"domains,":[85],"where":[86],"selectively":[92],"focus":[93],"on":[94,115],"features.":[99],"Our":[100],"able":[103],"outperform":[105],"state":[107],"art":[110],"terms":[112],"accuracy":[114],"UC":[117],"Merced":[118],"Rear":[120],"Signal":[121],"Dataset,":[122],"demonstrating":[123],"effectiveness":[125],"vehicle":[130],"recognition.":[132]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}