{"id":"https://openalex.org/W2903959260","doi":"https://doi.org/10.1109/itsc.2018.8569522","title":"Deep Convolutional Traffic Light Recognition for Automated Driving","display_name":"Deep Convolutional Traffic Light Recognition for Automated Driving","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2903959260","doi":"https://doi.org/10.1109/itsc.2018.8569522","mag":"2903959260"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569522","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/A5071377898","display_name":"Martin Bach","orcid":"https://orcid.org/0000-0002-8695-7529"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Martin Bach","raw_affiliation_strings":["Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087479789","display_name":"Daniel Stumper","orcid":null},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Stumper","raw_affiliation_strings":["Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085054529","display_name":"Klaus Dietmayer","orcid":"https://orcid.org/0000-0002-1651-014X"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus Dietmayer","raw_affiliation_strings":["Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071377898"],"corresponding_institution_ids":["https://openalex.org/I196349391"],"apc_list":null,"apc_paid":null,"fwci":1.8122,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.89614265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"851","last_page":"858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9984999895095825,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7567325830459595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6652287840843201},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.652660071849823},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5802983641624451},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5276877880096436},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5132205486297607},{"id":"https://openalex.org/keywords/traffic-signal","display_name":"Traffic signal","score":0.4959655702114105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4696207344532013},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41543954610824585},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.23595786094665527},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09574294090270996},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08507692813873291}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7567325830459595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6652287840843201},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.652660071849823},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5802983641624451},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5276877880096436},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5132205486297607},{"id":"https://openalex.org/C2987419075","wikidata":"https://www.wikidata.org/wiki/Q8004","display_name":"Traffic signal","level":2,"score":0.4959655702114105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4696207344532013},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41543954610824585},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.23595786094665527},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09574294090270996},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08507692813873291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2018.8569522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569522","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","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1562810981","https://openalex.org/W1686810756","https://openalex.org/W1832500336","https://openalex.org/W1981562962","https://openalex.org/W2031489346","https://openalex.org/W2060602100","https://openalex.org/W2105529173","https://openalex.org/W2136891917","https://openalex.org/W2170841223","https://openalex.org/W2194775991","https://openalex.org/W2407521645","https://openalex.org/W2512944926","https://openalex.org/W2557728737","https://openalex.org/W2567157498","https://openalex.org/W2613718673","https://openalex.org/W2615277952","https://openalex.org/W2737202447","https://openalex.org/W2741637292","https://openalex.org/W2890748873","https://openalex.org/W2950800384","https://openalex.org/W2962835968","https://openalex.org/W2963037989","https://openalex.org/W2963516811","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6714138976","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W2099261052","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3115426489"],"abstract_inverted_index":{"Robust":[0],"traffic":[1,52,65,84,125,137,163],"light":[2,66,164],"detection":[3,118],"and":[4,86,98,109],"state":[5],"recognition":[6,43,67],"is":[7,54,78],"of":[8,21,36,44,72,103,120,127,136,148,156],"crucial":[9],"importance":[10],"on":[11,50,69,106],"the":[12,18,22,37,42,51,70,73,107,154,157,162],"path":[13],"to":[14,80,160],"automated":[15],"vehicles.":[16],"However,":[17],"mere":[19],"classification":[20],"signaled":[23],"states":[24,165],"does":[25],"not":[26,81],"suffice":[27],"at":[28,40],"complex":[29],"multi-lane":[30],"intersections.":[31],"Rather,":[32],"a":[33,61],"complete":[34],"understanding":[35],"intersection,":[38],"but":[39,90],"least":[41],"additional":[45],"information":[46],"(like":[47],"arrows":[48],"displayed":[49],"lights)":[53],"necessary.":[55],"In":[56],"this":[57],"work,":[58],"we":[59,152],"developed":[60,158],"unified":[62],"deep":[63],"convolutional":[64],"system":[68,159],"basis":[71],"Faster":[74],"R-CNN":[75],"architecture,":[76],"which":[77],"able":[79],"only":[82],"detect":[83],"lights":[85,126],"classify":[87],"their":[88,93],"state,":[89],"also":[91],"distinguish":[92],"type":[94],"(circle,":[95],"straight,":[96],"left,":[97],"right).":[99],"An":[100],"in-depth":[101],"analysis":[102],"its":[104],"performance":[105,119],"large":[108],"diverse":[110],"DriveU":[111],"Traffic":[112],"Light":[113],"Dataset":[114],"shows":[115],"an":[116],"overall":[117],"0.92":[121],"Average":[122],"Precision":[123],"for":[124,166],"width":[128],"greater":[129],"than":[130],"8":[131],"px.":[132],"Additionally,":[133],"other":[134],"kinds":[135],"lights,":[138,141],"e.g.":[139],"pedestrian":[140],"have":[142],"been":[143],"identified":[144],"as":[145],"main":[146],"cause":[147],"false":[149],"positives.":[150],"Moreover,":[151],"evaluated":[153],"usefulness":[155],"assess":[161],"all":[167],"present":[168],"driving":[169],"directions":[170],"revealing":[171],"inconsistencies":[172],"among":[173],"multiple":[174],"detections":[175],"in":[176],"single":[177],"images.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
