{"id":"https://openalex.org/W2903632709","doi":"https://doi.org/10.1109/itsc.2018.8569575","title":"Deep Traffic Light Detection for Self-driving Cars from a Large-scale Dataset","display_name":"Deep Traffic Light Detection for Self-driving Cars from a Large-scale Dataset","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2903632709","doi":"https://doi.org/10.1109/itsc.2018.8569575","mag":"2903632709"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-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/A5061842716","display_name":"Jinkyu Kim","orcid":"https://orcid.org/0000-0001-6520-2074"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinkyu Kim","raw_affiliation_strings":["Department of Electrical Engineering and Computer Sciences, UC Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Sciences, UC Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069581005","display_name":"Hyunggi Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyunggi Cho","raw_affiliation_strings":["Phantom AI Inc., Burlingame, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Phantom AI Inc., Burlingame, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044252650","display_name":"Myung Hwangbo","orcid":"https://orcid.org/0000-0001-9216-0519"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Myung Hwangbo","raw_affiliation_strings":["Phantom AI Inc., Burlingame, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Phantom AI Inc., Burlingame, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101415546","display_name":"Jaehyung Choi","orcid":"https://orcid.org/0000-0003-1998-6132"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaehyung Choi","raw_affiliation_strings":["Phantom AI Inc., Burlingame, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Phantom AI Inc., Burlingame, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089723214","display_name":"John Canny","orcid":"https://orcid.org/0000-0002-7161-7927"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Canny","raw_affiliation_strings":["Department of Electrical Engineering and Computer Sciences, UC Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Sciences, UC Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032742136","display_name":"Youngwook Paul Kwon","orcid":"https://orcid.org/0009-0003-0529-3578"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Youngwook Paul Kwon","raw_affiliation_strings":["Department of Mechanical Engineering Engineering, UC Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering Engineering, UC Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6516,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.85290097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"280","last_page":"285"},"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/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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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":0.8059879541397095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6699941158294678},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6683010458946228},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6652939319610596},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5477036237716675},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.525611162185669},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4969384968280792},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49307772517204285},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4645300805568695},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41791558265686035},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.41601958870887756},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4131471514701843},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41142329573631287},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35216742753982544},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2659642696380615},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12121054530143738}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8059879541397095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6699941158294678},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6683010458946228},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6652939319610596},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5477036237716675},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.525611162185669},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4969384968280792},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49307772517204285},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4645300805568695},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41791558265686035},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.41601958870887756},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4131471514701843},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41142329573631287},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35216742753982544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2659642696380615},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12121054530143738},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2018.8569575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1562810981","https://openalex.org/W1686810756","https://openalex.org/W2117539524","https://openalex.org/W2155893237","https://openalex.org/W2512944926","https://openalex.org/W2557728737","https://openalex.org/W2737202447","https://openalex.org/W2950094539","https://openalex.org/W2962835968","https://openalex.org/W2963150697","https://openalex.org/W3106250896","https://openalex.org/W6637373629","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W2922421953","https://openalex.org/W3002270006","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W2970686063"],"abstract_inverted_index":{"Traffic":[0],"lights":[1,89,126,142,170],"perception":[2],"problem":[3],"is":[4,120],"one":[5],"of":[6,20,36,67,128],"the":[7,113,129,133,161,175],"key":[8],"challenges":[9],"for":[10,22,69],"autonomous":[11],"vehicle":[12,157],"controllers":[13],"in":[14,55,92],"urban":[15],"areas.":[16],"While":[17],"a":[18,33,81,93,100,116,138,150],"number":[19],"approaches":[21],"traffic":[23,88,109,125,141,169],"light":[24,110],"detection":[25,104],"have":[26],"been":[27],"proposed,":[28],"these":[29],"methods":[30],"often":[31],"require":[32],"prior":[34],"knowledge":[35],"map":[37],"and/or":[38],"show":[39],"high":[40],"false":[41,124,168],"positive":[42],"rates.":[43],"Recent":[44],"successes":[45],"suggest":[46],"that":[47,85,160],"deep":[48,73,101],"neural":[49,74,102],"networks":[50],"will":[51],"be":[52],"widely":[53],"used":[54,121],"self-driving":[56,156],"cars,":[57],"but":[58],"current":[59],"public":[60],"datasets":[61],"do":[62],"not":[63],"provide":[64],"sufficient":[65],"amount":[66],"labels":[68],"training":[70],"such":[71],"large":[72],"networks.":[75],"In":[76,112],"this":[77],"paper,":[78],"we":[79,136],"developed":[80],"two-step":[82],"computational":[83],"method":[84,163],"can":[86],"detect":[87],"from":[90],"images":[91],"real-time":[94],"manner.":[95],"The":[96],"first":[97],"step":[98],"exploits":[99],"object":[103],"architecture":[105],"to":[106,122,166],"fine":[107],"true":[108],"candidates.":[111,130],"second":[114],"step,":[115],"point-based":[117],"reward":[118],"system":[119],"eliminate":[123],"out":[127],"To":[131],"evaluate":[132],"proposed":[134,162],"approach,":[135],"collected":[137],"human-annotated":[139],"large-scale":[140],"dataset":[143],"(over":[144],"60":[145],"hours).":[146],"We":[147],"also":[148],"designed":[149],"real-world":[151],"experiment":[152],"with":[153,174],"an":[154],"instrumented":[155],"and":[158],"observed":[159],"was":[164],"able":[165],"handle":[167],"substantially":[171],"better":[172],"compared":[173],"baseline":[176],"considered.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
