{"id":"https://openalex.org/W4293095155","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860676","title":"Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism","display_name":"Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4293095155","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860676"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2022-spring54318.2022.9860676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860676","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003697769","display_name":"Abhishek Gupta","orcid":"https://orcid.org/0000-0002-8728-5146"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Abhishek Gupta","raw_affiliation_strings":["Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081341971","display_name":"Kandasamy Illanko","orcid":null},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kandasamy Illanko","raw_affiliation_strings":["Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022698330","display_name":"Xavier Fernando","orcid":"https://orcid.org/0000-0001-7120-528X"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xavier Fernando","raw_affiliation_strings":["Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ryerson University,Department of Electrical, Computer and Biomedical Engineering,Toronto,Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I530967"],"apc_list":null,"apc_paid":null,"fwci":1.202,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.86834487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9976999759674072,"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":0.8299388885498047},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7993060350418091},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7182232141494751},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6710819005966187},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.604778528213501},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5785839557647705},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5202409029006958},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46549877524375916},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40374982357025146}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8299388885498047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7993060350418091},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7182232141494751},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6710819005966187},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.604778528213501},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5785839557647705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5202409029006958},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46549877524375916},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40374982357025146}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2022-spring54318.2022.9860676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860676","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5899999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2884367402","https://openalex.org/W2894234546","https://openalex.org/W2913960518","https://openalex.org/W2923165220","https://openalex.org/W2962807143","https://openalex.org/W3019057642","https://openalex.org/W3035564946","https://openalex.org/W3087011842","https://openalex.org/W3110144974","https://openalex.org/W3112288498","https://openalex.org/W3118518413","https://openalex.org/W3126173010","https://openalex.org/W3130423852","https://openalex.org/W3135688577","https://openalex.org/W3150428877","https://openalex.org/W3158717538","https://openalex.org/W3160120488","https://openalex.org/W6786910519"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2999805992","https://openalex.org/W4291897433","https://openalex.org/W3011074480","https://openalex.org/W4311401716"],"abstract_inverted_index":{"This":[0],"paper":[1,44],"addresses":[2],"object":[3,14],"detection":[4,15,109],"and":[5,10,19,66,111,136,176],"scene":[6],"perception":[7],"for":[8,27,108],"connected":[9],"autonomous":[11,29,60],"vehicles.":[12],"Road":[13],"at":[16],"high":[17],"accuracy":[18,112,139],"fast":[20],"inference":[21],"speed":[22],"is":[23,90,106,125,169],"a":[24,46,94],"challenging":[25],"task":[26],"safe":[28],"driving":[30,73,120,149,163],"as":[31],"false":[32,36],"positives":[33],"arising":[34],"from":[35],"localization":[37],"can":[38],"lead":[39],"to":[40,51,54,113,117,141,147],"fatal":[41],"outcomes.":[42],"The":[43,103,122,165],"proposes":[45],"convolutional":[47],"neural":[48],"network":[49],"(CNN)":[50],"recognize":[52],"images":[53],"enhance":[55],"intelligent":[56],"adaptive":[57],"behavior":[58],"in":[59,71],"vehicles":[61],"by":[62,92],"correctly":[63],"classifying,":[64],"detecting,":[65],"segmenting":[67],"spatially":[68],"distributed":[69],"objects":[70,116],"the":[72,83,88,128,156,162],"environment.":[74,164],"By":[75],"focusing":[76],"on":[77,127,152],"specific":[78],"regions":[79],"of":[80,87,155],"an":[81,138],"image,":[82],"most":[84],"significant":[85],"region":[86],"image":[89],"learned":[91],"appending":[93],"CNN":[95],"with":[96,101],"probabilistic":[97],"attention":[98],"mechanism":[99],"aided":[100],"transformers.":[102],"proposed":[104,123,166],"approach":[105],"analyzed":[107],"efficiency":[110],"distinguish":[114],"different":[115],"make":[118,148],"appropriate":[119],"decisions.":[121],"method":[124],"validated":[126],"publicly":[129],"available":[130],"Berkeley":[131],"deep":[132,144],"drive":[133],"(BDD)":[134],"dataset":[135],"shows":[137],"comparable":[140],"other":[142],"state-of-the-art":[143],"learning":[145],"algorithms":[146],"decisions":[150],"based":[151],"real-time":[153],"assessment":[154],"temporal":[157],"states":[158],"encountered":[159],"while":[160],"navigating":[161],"model":[167],"performance":[168],"evaluated":[170],"using":[171],"mean":[172],"average":[173],"precision":[174],"(mAP)":[175],"speed-accuracy":[177],"trade-off.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
