{"id":"https://openalex.org/W4293057731","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860889","title":"Mining Image Semantics via Deep Learning: A Robust Lane Detection Approach for Autonomous Driving","display_name":"Mining Image Semantics via Deep Learning: A Robust Lane Detection Approach for Autonomous Driving","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4293057731","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860889"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2022-spring54318.2022.9860889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860889","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/A5100400195","display_name":"Shuo Wang","orcid":"https://orcid.org/0000-0003-2746-7949"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Wang","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034502293","display_name":"Wenwei Yue","orcid":"https://orcid.org/0000-0002-1890-5911"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwei Yue","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002192789","display_name":"Nan Xue","orcid":"https://orcid.org/0000-0003-2638-4412"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Xue","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454834","display_name":"Yue Chen","orcid":"https://orcid.org/0000-0003-1350-4972"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Chen","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081899997","display_name":"Xingyi Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyi Ji","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015340798","display_name":"Changle Li","orcid":"https://orcid.org/0000-0003-2568-8908"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changle Li","raw_affiliation_strings":["Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,State Key Laboratory of Integrated Services Networks,Xi&#x2019;an,Shaanxi,China,710071","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Smart Transportation, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9957000017166138,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.7470072507858276},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.7426536083221436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6544464826583862},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.574516236782074},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5452050566673279},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.516769528388977},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11519330739974976}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470072507858276},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.7426536083221436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6544464826583862},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.574516236782074},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5452050566673279},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.516769528388977},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11519330739974976}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2022-spring54318.2022.9860889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860889","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/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W2067191022","https://openalex.org/W2136929315","https://openalex.org/W2194775991","https://openalex.org/W2211466563","https://openalex.org/W2744404335","https://openalex.org/W2745410201","https://openalex.org/W2792613771","https://openalex.org/W2914663064","https://openalex.org/W2963881378","https://openalex.org/W2964199920","https://openalex.org/W3047778044","https://openalex.org/W3101246337","https://openalex.org/W3123846747","https://openalex.org/W3127561923","https://openalex.org/W4200002157","https://openalex.org/W4289763872","https://openalex.org/W4293406525","https://openalex.org/W4297666078","https://openalex.org/W6631190155","https://openalex.org/W6635292102","https://openalex.org/W6638667902","https://openalex.org/W6687483927","https://openalex.org/W6717372056","https://openalex.org/W6752882886","https://openalex.org/W6773319185"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W3009238340","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1,21,194,199],"has":[2],"attracted":[3],"huge":[4],"research":[5],"interest":[6],"from":[7,106],"both":[8],"academia":[9],"and":[10,36,52,81,139,159],"industry.":[11],"As":[12],"one":[13],"of":[14,22,50,85,94,127,148,180,207],"the":[15,19,34,38,48,68,83,100,107,124,145,178,181,205],"key":[16],"components":[17],"for":[18,213],"safe":[20],"autonomous":[23,214],"vehicles,":[24],"lane":[25,35,60,104,114,128,185,191,210],"detection":[26,43,61,146,192,211],"allows":[27],"vehicles":[28],"to":[29,102,152,167],"correctly":[30],"locate":[31],"itself":[32],"in":[33,184],"follow":[37],"traffic":[39],"rules.":[40],"Unlike":[41],"traditional":[42],"methods":[44],"that":[45,89,144],"rely":[46],"on":[47,123,135,197],"extraction":[49],"professional":[51],"hand-designed":[53],"features,":[54],"this":[55],"paper":[56],"proposes":[57],"a":[58,91,118],"robust":[59],"method":[62,134,183,212],"by":[63,116,170],"mining":[64],"semantic":[65],"information":[66],"via":[67],"deep":[69,86],"learning":[70,87],"model":[71],"LaneNet,":[72],"which":[73,203],"can":[74,164],"cope":[75],"with":[76,172,193],"more":[77,161],"complex":[78,162],"road":[79,108],"scenes,":[80],"relieve":[82],"restriction":[84],"models":[88],"detect":[90],"fixed":[92],"number":[93],"lanes.":[95],"Specifically,":[96],"we":[97,111,131,189],"first":[98],"utilize":[99],"LaneNet":[101],"segment":[103],"pixels":[105],"scene.":[109],"Then":[110],"distinguish":[112],"different":[113],"instances":[115],"using":[117],"clustering":[119],"loss":[120],"function":[121],"based":[122,196],"distance":[125],"vector":[126],"pixels.":[129],"Finally,":[130],"verify":[132],"our":[133,208],"two":[136],"datasets,":[137],"Tusimple":[138,149],"CULane.":[140],"The":[141],"results":[142,176],"show":[143],"accuracy":[147],"is":[150,157],"up":[151,166],"94.3%,":[153],"CULane\u2019s":[154],"normal":[155],"level":[156],"90.4%":[158],"other":[160],"levels":[163],"reach":[165],"70%.":[168],"Furthermore,":[169],"comparing":[171],"existing":[173],"approaches,":[174],"simulation":[175,200],"confirm":[177],"robustness":[179],"proposed":[182,209],"detection.":[186],"In":[187],"addition,":[188],"combine":[190],"decision":[195],"intelligent":[198],"platform":[201],"PanoSim5,":[202],"illustrates":[204],"effectiveness":[206],"driving.":[215]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
