{"id":"https://openalex.org/W4206545034","doi":"https://doi.org/10.1109/bigdata52589.2021.9671392","title":"Drivable Area Detection Using Deep Learning Models for Autonomous Driving","display_name":"Drivable Area Detection Using Deep Learning Models for Autonomous Driving","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206545034","doi":"https://doi.org/10.1109/bigdata52589.2021.9671392"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671392","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","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/A5050692318","display_name":"Donghao Qiao","orcid":"https://orcid.org/0000-0003-1411-0705"},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Donghao Qiao","raw_affiliation_strings":["School of Computing, Queen\u2019s University, Kingston, Canada","School of Computing, Queen's University, Kingston, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen\u2019s University, Kingston, Canada","institution_ids":["https://openalex.org/I204722609"]},{"raw_affiliation_string":"School of Computing, Queen's University, Kingston, Canada","institution_ids":["https://openalex.org/I204722609"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063480277","display_name":"Farhana Zulkernine","orcid":"https://orcid.org/0000-0002-3326-0875"},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Farhana Zulkernine","raw_affiliation_strings":["School of Computing, Queen\u2019s University, Kingston, Canada","School of Computing, Queen's University, Kingston, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen\u2019s University, Kingston, Canada","institution_ids":["https://openalex.org/I204722609"]},{"raw_affiliation_string":"School of Computing, Queen's University, Kingston, Canada","institution_ids":["https://openalex.org/I204722609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050692318"],"corresponding_institution_ids":["https://openalex.org/I204722609"],"apc_list":null,"apc_paid":null,"fwci":2.7106,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.94133054,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5233","last_page":"5238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8004124164581299},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7868717908859253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7390234470367432},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6987951397895813},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.6402016878128052},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5550490021705627},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5540480017662048},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5095041394233704},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5038651823997498},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.495416522026062},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4614701271057129},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4475223124027252},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.43273913860321045},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42385971546173096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4184267520904541},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09208089113235474}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8004124164581299},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7868717908859253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7390234470367432},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6987951397895813},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.6402016878128052},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5550490021705627},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5540480017662048},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5095041394233704},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5038651823997498},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.495416522026062},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4614701271057129},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4475223124027252},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.43273913860321045},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42385971546173096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4184267520904541},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09208089113235474},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671392","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8199999928474426,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W49811868","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1966931935","https://openalex.org/W2070724998","https://openalex.org/W2109255472","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2322480645","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2565639579","https://openalex.org/W2610794603","https://openalex.org/W2767941964","https://openalex.org/W2796497685","https://openalex.org/W2884436604","https://openalex.org/W2886216548","https://openalex.org/W2943676499","https://openalex.org/W2963335479","https://openalex.org/W2963840672","https://openalex.org/W2963857746","https://openalex.org/W2964309882","https://openalex.org/W2964444661","https://openalex.org/W3004001685","https://openalex.org/W3034971973","https://openalex.org/W3035564946","https://openalex.org/W3098757871","https://openalex.org/W3099973186","https://openalex.org/W3100019905","https://openalex.org/W3113022368","https://openalex.org/W3120453208","https://openalex.org/W3160583610","https://openalex.org/W6639824700","https://openalex.org/W6696085341","https://openalex.org/W6737133248","https://openalex.org/W6748481559"],"related_works":["https://openalex.org/W4312857205","https://openalex.org/W2613186388","https://openalex.org/W2187221949","https://openalex.org/W2734888972","https://openalex.org/W2536634271","https://openalex.org/W4285827401","https://openalex.org/W2090093270","https://openalex.org/W2739874619","https://openalex.org/W2894878591","https://openalex.org/W1485614034"],"abstract_inverted_index":{"Drivable":[0],"area":[1,61],"or":[2],"free":[3],"space":[4],"detection":[5],"is":[6,34,69,99,113,130,148,175],"an":[7,97],"important":[8],"task":[9],"in":[10,62,90,115],"Advanced":[11],"Driver-Assistance":[12],"Systems":[13],"(ADAS)":[14],"and":[15,27,81,96,118,138,161],"autonomous":[16],"driving":[17,30,155],"system.":[18],"It":[19],"can":[20,39],"help":[21],"intelligent":[22],"vehicles":[23],"understand":[24],"road":[25,143],"conditions":[26],"determine":[28],"safe":[29],"area.":[31],"Semantic":[32],"segmentation":[33,55],"a":[35,51,107],"pixel-wise":[36],"prediction":[37],"which":[38,174],"classify":[40],"each":[41],"pixel":[42],"into":[43],"its":[44],"category.":[45],"In":[46],"this":[47,167],"paper,":[48],"we":[49],"propose":[50],"deep":[52],"learning-based":[53],"semantic":[54],"architecture":[56,93,109],"to":[57,101,177],"predict":[58],"the":[59,65,76,91,102,116,119,122,133,146,151],"drivable":[60,159,163],"front":[63],"of":[64],"vehicle.":[66],"Our":[67,128],"model":[68,129,147,170],"built":[70],"based":[71],"on":[72,132,142,150],"ResNet":[73],"backbone":[74,89],"with":[75,110,157],"Feature":[77],"Pyramid":[78,84],"Network":[79],"(FPN)":[80],"Atrous":[82],"Spatial":[83],"Pooling":[85],"(ASPP)":[86],"modules.":[87],"The":[88],"bottom-up":[92],"extracts":[94],"features":[95,124],"ASPP":[98],"attached":[100],"last":[103],"decoder":[104,117],"layer.":[105],"Additionally,":[106],"top-down":[108],"lateral":[111],"connections":[112],"added":[114],"FPN":[120],"utilizes":[121],"multi-scale":[123],"for":[125],"final":[126],"prediction.":[127],"evaluated":[131,149],"Cityscapes":[134],"street":[135],"scene":[136],"dataset":[137,156,168],"achieves":[139,171],"95.90%":[140],"mIoU":[141,173],"segmentation.":[144],"Next,":[145],"BDD100K":[152],"large-scale":[153],"diverse":[154],"direct":[158],"region":[160,164],"alternative":[162],"annotations.":[165],"For":[166],"our":[169],"84.58%":[172],"comparable":[176],"some":[178],"State-of-the-Art":[179],"models.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
