{"id":"https://openalex.org/W7165110174","doi":"https://doi.org/10.48550/arxiv.2606.18864","title":"Scaling Learning-based AEB with Massive Unlabeled Data","display_name":"Scaling Learning-based AEB with Massive Unlabeled Data","publication_year":2026,"publication_date":"2026-06-17","ids":{"openalex":"https://openalex.org/W7165110174","doi":"https://doi.org/10.48550/arxiv.2606.18864"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.18864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18864","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.18864","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138840695","display_name":"Xiangyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102104096","display_name":"Yang Zhan","orcid":"https://orcid.org/0009-0004-5198-0587"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138942686","display_name":"Mengxiang Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Mengxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006104901","display_name":"Chuanchuan Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Chuanchuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091867453","display_name":"Yansong Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Yansong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138863857","display_name":"Junjie Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Junjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138898266","display_name":"Yu Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084895043","display_name":"X. Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138833723","display_name":"Zhen Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138956705","display_name":"Ying Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038585039","display_name":"Yulun Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yulun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101950131","display_name":"Zhitao Xu","orcid":"https://orcid.org/0000-0001-9977-0790"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Zhitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.4496999979019165,"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":0.4496999979019165,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.19009999930858612,"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"}},{"id":"https://openalex.org/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.07169999927282333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.6244000196456909},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.589900016784668},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5575000047683716},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5522000193595886},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5486000180244446},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4900999963283539},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4778999984264374},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.4296000003814697},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3752000033855438}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6244000196456909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5906999707221985},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5522000193595886},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4778999984264374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44429999589920044},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.4296000003814697},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4133000075817108},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3402000069618225},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2779280203","wikidata":"https://www.wikidata.org/wiki/Q17121211","display_name":"Small data","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.18864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18864","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.18864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18864","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5404821038246155,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"how":[3],"to":[4,63,95,118,133],"scale":[5,115],"learning-based":[6],"automatic":[7],"emergency":[8],"braking":[9],"(AEB)":[10],"with":[11,72,90],"massive":[12],"unlabeled":[13,35,101,113],"fleet":[14],"data":[15,37,102,114],"under":[16],"production":[17,163],"constraints.":[18],"Our":[19],"approach":[20],"is":[21,39,131],"based":[22],"on":[23,100],"meta-feedback":[24],"semi-supervised":[25],"learning":[26],"(MF-SSL),":[27],"where":[28],"a":[29,42,68,91,147,154,162],"teacher":[30,92],"generates":[31],"pseudo":[32],"labels":[33],"for":[34],"driving":[36,159],"and":[38,54,86,139,153],"updated":[40],"using":[41],"small":[43],"labeled":[44],"anchor":[45,52],"set":[46],"as":[47,112],"safety-critical":[48],"feedback.":[49],"In":[50],"production,":[51],"ambiguity":[53],"labeled-unlabeled":[55],"mismatch":[56],"can":[57],"amplify":[58],"systematic":[59],"pseudo-label":[60],"errors,":[61],"leading":[62],"spurious":[64],"triggers.":[65],"We":[66],"propose":[67],"stabilized":[69],"MF-SSL":[70],"framework":[71],"(i)":[73],"Noise-Aware":[74],"Decoupling,":[75],"which":[76],"removes":[77],"ambiguity-prone":[78],"anchors":[79],"from":[80,116],"the":[81],"teacher's":[82],"supervised":[83],"update":[84],"path,":[85],"(ii)":[87],"kinematics-gated":[88],"pseudo-labeling":[89],"conflict":[93],"penalty":[94],"suppress":[96],"mismatch-induced":[97],"risk":[98],"hallucinations":[99],"while":[103,123],"maintaining":[104],"broad":[105],"coverage.":[106],"Extensive":[107],"experiments":[108],"show":[109],"consistent":[110],"gains":[111],"1M":[117],"1B":[119],"windows,":[120],"improving":[121],"safety":[122],"keeping":[124],"comfort":[125],"stable.":[126],"The":[127],"1B-trained":[128],"student":[129],"model":[130],"deployed":[132],"hundreds":[134],"of":[135,137,144],"thousands":[136],"vehicles":[138],"validated":[140],"over":[141,161],"\\$10^9$":[142],"km":[143],"driving,":[145],"achieving":[146],"positive-to-false":[148],"activation":[149],"ratio":[150],"exceeding":[151],"100:1":[152],"35%":[155],"improvement":[156],"in":[157],"accident-free":[158],"mileage":[160],"rule-only":[164],"baseline.":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-19T00:00:00"}
