{"id":"https://openalex.org/W7162787509","doi":"https://doi.org/10.48550/arxiv.2605.29425","title":"ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control","display_name":"ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162787509","doi":"https://doi.org/10.48550/arxiv.2605.29425"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29425","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.2605.29425","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049277354","display_name":"Aoyu Pang","orcid":"https://orcid.org/0009-0001-7702-3409"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Aoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137358484","display_name":"Maonan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Maonan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137340268","display_name":"Yuejiao Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Yuejiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082908618","display_name":"Chung Shue Chen","orcid":"https://orcid.org/0000-0002-7702-2369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chung Shue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137386370","display_name":"Zhiwei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5040559125","display_name":"Man-On Pun","orcid":"https://orcid.org/0000-0003-3316-5381"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pun, Man-On","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/T10524","display_name":"Traffic control and management","score":0.5806000232696533,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10524","display_name":"Traffic control and management","score":0.5806000232696533,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.22370000183582306,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.03790000081062317,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8136000037193298},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6013000011444092},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5525000095367432},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5455999970436096},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5250999927520752},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.483599990606308},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.45489999651908875},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.44290000200271606}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8136000037193298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006000280380249},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6013000011444092},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5525000095367432},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5455999970436096},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5250999927520752},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.483599990606308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4715999960899353},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.45489999651908875},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4341999888420105},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33340001106262207},{"id":"https://openalex.org/C2987419075","wikidata":"https://www.wikidata.org/wiki/Q8004","display_name":"Traffic signal","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29425","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.2605.29425","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29425","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"(RL)":[2],"has":[3],"shown":[4],"promise":[5],"in":[6],"traffic":[7,70,120,173,204],"signal":[8],"control":[9],"(TSC).":[10],"However,":[11],"its":[12],"reliance":[13],"on":[14,157],"predefined":[15],"states":[16],"limits":[17],"responsiveness":[18],"to":[19,41,45,110,119,149,193],"observable":[20],"open-world":[21],"events":[22,162],"that":[23,178],"are":[24,131],"absent":[25],"from":[26,34,79,92],"training":[27],"data.":[28],"IoT-enabled":[29],"intersections":[30],"provide":[31],"heterogeneous":[32],"observations":[33],"roadside":[35],"sensors":[36],"and":[37,75,95,122,144,171],"cameras,":[38],"creating":[39],"opportunities":[40],"improve":[42],"RL":[43,58,82,152,166],"adaptability":[44],"such":[46],"events.":[47],"To":[48,125],"this":[49],"end,":[50],"we":[51],"propose":[52],"ReasonLight,":[53],"a":[54,80,106],"multimodal":[55],"foundation":[56],"model-enhanced":[57],"framework":[59],"for":[60],"zero-shot":[61,181],"TSC.":[62],"ReasonLight":[63,88,156,179],"integrates":[64],"three":[65],"sources":[66],"of":[67,136,160],"information:":[68],"structured":[69],"measurements,":[71],"multi-view":[72,93],"camera":[73],"observations,":[74],"candidate":[76],"phase":[77],"decisions":[78],"pre-trained":[81],"controller.":[83],"Given":[84],"an":[85],"RL-proposed":[86],"phase,":[87],"extracts":[89],"visual":[90],"semantics":[91],"images":[94],"aligns":[96],"them":[97],"with":[98,196],"compact":[99],"sensor-derived":[100],"scene":[101],"descriptions.":[102],"This":[103],"alignment":[104],"enables":[105],"semantic-guided":[107],"refinement":[108],"module":[109],"either":[111],"preserve":[112],"or":[113],"adjust":[114],"the":[115,134,145,150,197],"proposed":[116],"action":[117],"according":[118],"rules":[121],"event":[123],"semantics.":[124],"ensure":[126],"operational":[127],"reliability,":[128],"refined":[129],"actions":[130],"constrained":[132],"by":[133,191],"set":[135],"available":[137],"phases.":[138],"Any":[139],"invalid":[140],"decision":[141],"is":[142],"rejected,":[143],"system":[146],"falls":[147],"back":[148],"original":[151],"action.":[153],"We":[154],"evaluate":[155],"two":[158],"types":[159],"rare":[161],"not":[163],"seen":[164],"during":[165],"training:":[167],"emergency":[168,187],"vehicle":[169,188],"priority":[170],"temporary":[172],"regulation.":[174],"Experimental":[175],"results":[176],"show":[177],"achieves":[180],"adaptation":[182],"without":[183],"retraining.":[184],"It":[185],"reduces":[186],"waiting":[189],"time":[190],"up":[192],"88.7%":[194],"compared":[195],"RL-only":[198],"backbone":[199],"while":[200],"preserving":[201],"comparable":[202],"routine":[203],"performance.":[205]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}