{"id":"https://openalex.org/W4385484978","doi":"https://doi.org/10.1109/icasspw59220.2023.10193741","title":"Confidence-Based Federated Distillation for Vision-Based Lane-Centering","display_name":"Confidence-Based Federated Distillation for Vision-Based Lane-Centering","publication_year":2023,"publication_date":"2023-06-04","ids":{"openalex":"https://openalex.org/W4385484978","doi":"https://doi.org/10.1109/icasspw59220.2023.10193741"},"language":"en","primary_location":{"id":"doi:10.1109/icasspw59220.2023.10193741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icasspw59220.2023.10193741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","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/A5101768546","display_name":"Yitao Chen","orcid":"https://orcid.org/0009-0002-7067-3983"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yitao Chen","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322085","display_name":"Dawei Chen","orcid":"https://orcid.org/0000-0002-4162-1423"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Dawei Chen","raw_affiliation_strings":["Toyota InfoTech Labs"],"affiliations":[{"raw_affiliation_string":"Toyota InfoTech Labs","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101899365","display_name":"Haoxin Wang","orcid":"https://orcid.org/0000-0002-2658-3772"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoxin Wang","raw_affiliation_strings":["Georgia State University"],"affiliations":[{"raw_affiliation_string":"Georgia State University","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009775690","display_name":"Kyungtae Han","orcid":"https://orcid.org/0000-0001-8291-5025"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Kyungtae Han","raw_affiliation_strings":["Toyota InfoTech Labs"],"affiliations":[{"raw_affiliation_string":"Toyota InfoTech Labs","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103197618","display_name":"Ming Zhao","orcid":"https://orcid.org/0000-0003-4126-3678"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Zhao","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101768546"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.23,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49879993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9977999925613403,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.6866395473480225},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5790250301361084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42524459958076477},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42304131388664246},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.055089086294174194}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6866395473480225},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5790250301361084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42524459958076477},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42304131388664246},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.055089086294174194},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icasspw59220.2023.10193741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icasspw59220.2023.10193741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1513874326","https://openalex.org/W2194775991","https://openalex.org/W2342840547","https://openalex.org/W2407386500","https://openalex.org/W2471138382","https://openalex.org/W2483814582","https://openalex.org/W2541884796","https://openalex.org/W2807006176","https://openalex.org/W2937443896","https://openalex.org/W2978426779","https://openalex.org/W2996058491","https://openalex.org/W3035453001","https://openalex.org/W3035564946","https://openalex.org/W3104353633","https://openalex.org/W3205626500","https://openalex.org/W3215194618","https://openalex.org/W4226101686","https://openalex.org/W4226142397","https://openalex.org/W4283793264","https://openalex.org/W4285601829","https://openalex.org/W4287332481","https://openalex.org/W6704559304","https://openalex.org/W6722063826","https://openalex.org/W6752029299","https://openalex.org/W6759238902","https://openalex.org/W6762913911","https://openalex.org/W6763607942","https://openalex.org/W6772286975","https://openalex.org/W6773817997","https://openalex.org/W6780224944"],"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":{"A":[0,156],"fundamental":[1],"challenge":[2],"of":[3,13,54,113,126,133,152,159],"autonomous":[4],"driving":[5],"is":[6,84,94],"maintaining":[7],"the":[8,11,14,18,36,47,91,98,111,123,130,140,146,150,153,165],"vehicle":[9],"in":[10,50],"center":[12],"lane":[15,161],"by":[16,35,67,173],"adjusting":[17],"steering":[19,29,41,117],"angle.":[20],"Recent":[21],"advances":[22],"leverage":[23],"deep":[24],"neural":[25],"networks":[26],"to":[27,45,71,86,109,128,148],"predict":[28],"decisions":[30],"directly":[31],"from":[32],"images":[33],"captured":[34],"car":[37],"cameras.":[38],"Machine":[39],"learning-based":[40],"angle":[42,118],"prediction":[43],"needs":[44],"consider":[46],"vehicle\u2019s":[48],"limitation":[49],"uploading":[51],"large":[52],"amounts":[53],"potentially":[55],"private":[56,80],"data":[57,92],"for":[58,116],"model":[59,76,144],"training.":[60],"Federated":[61],"learning":[62,115,151],"can":[63,168],"address":[64],"these":[65],"constraints":[66],"enabling":[68],"multiple":[69],"vehicles":[70],"collaboratively":[72],"train":[73],"a":[74,103],"global":[75,154],"without":[77],"sharing":[78],"their":[79],"data,":[81],"but":[82],"it":[83,121],"difficult":[85],"achieve":[87],"good":[88],"accuracy":[89],"as":[90,145],"distribution":[93],"often":[95],"non-i.i.d.":[96],"across":[97],"vehicles.":[99],"This":[100],"paper":[101],"presents":[102],"new":[104],"confidence-based":[105],"federated":[106,114],"distillation":[107],"method":[108],"improve":[110],"performance":[112],"prediction.":[119],"Specifically,":[120],"proposes":[122],"novel":[124],"use":[125],"entropy":[127],"determine":[129],"predictive":[131],"confidence":[132],"each":[134],"local":[135,143],"model,":[136],"and":[137,171,175],"then":[138],"selects":[139],"most":[141],"confident":[142],"teacher":[147],"guide":[149],"model.":[155],"comprehensive":[157],"evaluation":[158],"vision-based":[160],"centering":[162],"shows":[163],"that":[164],"proposed":[166],"approach":[167],"outperform":[169],"FedAvg":[170],"FedDF":[172],"11.3%":[174],"9%,":[176],"respectively.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}