{"id":"https://openalex.org/W4406266570","doi":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757851","title":"3D YOLO-SM: End-to-End Approach for Real-time Traffic Light Detection and Recognition in Complex Scenarios","display_name":"3D YOLO-SM: End-to-End Approach for Real-time Traffic Light Detection and Recognition in Complex Scenarios","publication_year":2024,"publication_date":"2024-10-07","ids":{"openalex":"https://openalex.org/W4406266570","doi":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757851"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2024-fall63153.2024.10757851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)","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/A5047826067","display_name":"Kshitiz Kumar","orcid":"https://orcid.org/0000-0003-2728-2493"},"institutions":[{"id":"https://openalex.org/I4210146682","display_name":"Intel (India)","ror":"https://ror.org/04f2n1245","country_code":"IN","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210146682"]},{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kshitiz Kumar","raw_affiliation_strings":["Indian Institute of Technology,Dept. of Artificial Intelligence,Hyderabad,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Dept. of Artificial Intelligence,Hyderabad,India","institution_ids":["https://openalex.org/I65181880","https://openalex.org/I4210146682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079460629","display_name":"D Santhosh Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D Santhosh Reddy","raw_affiliation_strings":["Indian Institute of Technology,Dept. of Electrical Engineering,Hyderabad,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Dept. of Electrical Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"last","author":{"id":null,"display_name":"P Rajalakshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P Rajalakshmi","raw_affiliation_strings":["NM-ICPS TiHAN Indian Institute of Technology,Dept. of Electrical Engineering,Hyderabad,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NM-ICPS TiHAN Indian Institute of Technology,Dept. of Electrical Engineering,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25888335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9074000120162964,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9074000120162964,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.904699981212616,"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/end-to-end-principle","display_name":"End-to-end principle","score":0.7705155611038208},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6286064386367798},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4047645330429077},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37461018562316895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37015271186828613}],"concepts":[{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.7705155611038208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6286064386367798},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4047645330429077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37461018562316895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37015271186828613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2024-fall63153.2024.10757851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Real-time":[0],"traffic":[1,20,41,140,171,208,240,256,267],"light":[2,42,172,257],"detection":[3,47,97,133,233],"and":[4,18,34,60,76,116,138,145,196,238,254],"recognition":[5],"(TLDR)":[6],"remains":[7],"a":[8,117,218],"crucial":[9],"challenge":[10],"for":[11,136],"autonomous":[12,261],"vehicles":[13],"(AVs),":[14],"particularly":[15,135],"in":[16,206,235,243,264],"complex":[17,266],"chaotic":[19],"scenarios.":[21],"These":[22],"scenarios":[23,244],"are":[24],"defined":[25],"by":[26,156],"dense":[27],"traffic,":[28],"frequent":[29],"occlusions,":[30],"diverse":[31],"vehicle":[32,166],"types,":[33],"unpredictable":[35],"movements,":[36],"which":[37,105],"can":[38],"significantly":[39,259],"impede":[40],"detection.":[43],"Current":[44],"neural":[45],"object":[46,96,109,232],"models":[48,69,234],"often":[49],"struggle":[50],"with":[51,70,101,187,217,245],"such":[52],"variability,":[53],"leading":[54],"to":[55,79,131,148],"high":[56],"false":[57,154],"positive":[58],"rates":[59],"reduced":[61],"accuracy.":[62],"This":[63,249],"necessitates":[64],"the":[65,113,193,199,210],"development":[66],"of":[67,190,215,221],"novel":[68],"advanced":[71],"feature":[72,150],"extraction,":[73],"context":[74],"awareness,":[75],"temporal":[77],"reasoning":[78],"ensure":[80],"safe":[81],"AV":[82],"operation.":[83],"We":[84,142],"present":[85],"an":[86,107,213],"end-to-end":[87],"solution:":[88],"3D":[89],"YOLO-SM":[90],"(3D":[91],"Depth-perception":[92],"based":[93,111,168],"on":[94,112,169,184,192,198],"2D":[95],"using":[98],"YOLO,":[99],"along":[100],"integrated":[102],"State":[103,119],"Machine),":[104],"features":[106],"enhanced":[108],"detector":[110],"YOLOv8":[114],"architecture":[115],"Neural":[118,161],"Machine":[120,163],"(NSM).":[121],"Our":[122,179,226],"approach":[123,251],"incorporates":[124],"depth":[125],"perception":[126],"through":[127],"stereo":[128],"vision":[129],"cameras":[130],"enhance":[132],"accuracy,":[134],"small":[137,237],"occluded":[139,239],"lights.":[141],"employ":[143],"SPD-Convolution":[144],"attention":[146],"mechanisms":[147],"improve":[149],"learning":[151],"capabilities,":[152],"minimizing":[153],"positives":[155],"analyzing":[157],"contextual":[158],"information.":[159],"The":[160],"Mealy":[162],"controller":[164],"adjusts":[165],"speed":[167],"local":[170],"detection,":[173],"thereby":[174],"imitating":[175],"human":[176],"driver":[177],"behavior.":[178],"model":[180,211],"achieves":[181],"state-of-the-art":[182,231],"results":[183],"benchmark":[185],"datasets,":[186],"mAP@0.5":[188],"scores":[189],"92.5%":[191],"LISA":[194],"dataset":[195],"89.3%":[197],"Bosch":[200],"dataset.":[201],"In":[202],"real-time":[203],"tests":[204],"conducted":[205],"urban":[207],"scenarios,":[209],"demonstrated":[212],"accuracy":[214],"97.38%":[216],"processing":[219],"time":[220],"20":[222],"milliseconds":[223],"per":[224],"frame.":[225],"proposed":[227],"method":[228],"surpasses":[229],"existing":[230],"detecting":[236],"lights,":[241],"especially":[242],"weak":[246],"semantic":[247],"cues.":[248],"comprehensive":[250],"ensures":[252],"reliable":[253],"efficient":[255],"recognition,":[258],"advancing":[260],"vehicles\u2019":[262],"capabilities":[263],"navigating":[265],"environments.":[268]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
