{"id":"https://openalex.org/W7168265512","doi":"https://doi.org/10.48550/arxiv.2607.10037","title":"Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving","display_name":"Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7168265512","doi":"https://doi.org/10.48550/arxiv.2607.10037"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10037","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.10037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140705936","display_name":"Jiewen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiewen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140658758","display_name":"Rui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140614730","display_name":"Matthew Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140617104","display_name":"Ming C. Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Ming C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140730514","display_name":"Xiaorui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaorui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140676813","display_name":"Peng Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Peng","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8248999714851379,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8248999714851379,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.0934000015258789,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.013000000268220901,"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/perception","display_name":"Perception","score":0.5674999952316284},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4636000096797943},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4611000120639801},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46070000529289246},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.41130000352859497},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.40369999408721924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7134000062942505},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48010000586509705},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4636000096797943},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38690000772476196},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C28063669","wikidata":"https://www.wikidata.org/wiki/Q7167042","display_name":"Perceptual system","level":3,"score":0.3273000121116638},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25690001249313354},{"id":"https://openalex.org/C161759796","wikidata":"https://www.wikidata.org/wiki/Q3982902","display_name":"Teleoperation","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10037","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.10037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10037","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4589076638221741}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Collaborative":[0,57],"decision-making":[1,138],"is":[2],"a":[3,54,92],"fundamental":[4],"capability":[5],"in":[6,20,147],"multi-robot":[7],"systems,":[8],"such":[9],"as":[10],"connected":[11],"autonomous":[12],"vehicles.":[13],"However,":[14],"perceptual":[15,71,151],"noise":[16,152],"and":[17,73,81,116,153,176],"adversarial":[18],"attacks":[19,157],"collaborators":[21],"can":[22,131],"severely":[23],"affect":[24],"decision":[25],"reliability.":[26],"Overall,":[27],"existing":[28,170],"methods":[29,171],"typically":[30],"rely":[31],"on":[32,38],"retraining":[33],"with":[34],"attack-specific":[35],"defenses":[36],"or":[37],"restrictive":[39],"perturbation":[40],"assumptions":[41],"to":[42,87,112,120,174],"improve":[43,85],"resilience,":[44],"which":[45],"limits":[46],"their":[47],"practicality.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,90],"propose":[53],"novel":[55,93],"Resilient":[56],"Decision-Making":[58],"(RCDM)":[59],"framework":[60],"that":[61,97,122,165],"consists":[62],"of":[63,101,108,156],"an":[64,74],"attention-based":[65,75,136],"encoder":[66],"for":[67,77],"extracting":[68],"individual":[69],"robot":[70],"embeddings":[72],"decoder":[76],"fusing":[78],"collaborator":[79],"perceptions":[80],"making":[82],"decisions.":[83],"To":[84],"resilience":[86],"corrupted":[88,102],"observations,":[89],"design":[91],"plug-and-play":[94],"reweighting":[95],"module":[96,130],"down-weights":[98],"the":[99,106,113,126],"influence":[100],"inputs":[103],"by":[104,172],"analyzing":[105],"consistency":[107],"neighborhood":[109],"points":[110,121],"relative":[111],"local":[114,127],"structure":[115],"assigning":[117],"smaller":[118],"weights":[119],"deviate":[123],"strongly":[124],"from":[125],"median.":[128],"This":[129],"be":[132],"seamlessly":[133],"integrated":[134],"into":[135],"collaborative":[137],"without":[139],"requiring":[140],"additional":[141],"training.":[142],"We":[143],"evaluate":[144],"our":[145,166],"method":[146],"high-fidelity":[148],"simulations,":[149],"considering":[150],"five":[154],"types":[155],"across":[158],"diverse":[159],"accident-prone":[160],"scenarios.":[161],"Experimental":[162],"results":[163],"demonstrate":[164],"approach":[167],"consistently":[168],"outperforms":[169],"up":[173],"26%":[175],"achieves":[177],"state-of-the-art":[178],"resilient":[179],"performance.":[180]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}