{"id":"https://openalex.org/W7147413225","doi":"https://doi.org/10.48550/arxiv.2603.28963","title":"AutoWorld: Scaling Multi-Agent Traffic Simulation with Self-Supervised World Models","display_name":"AutoWorld: Scaling Multi-Agent Traffic Simulation with Self-Supervised World Models","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147413225","doi":"https://doi.org/10.48550/arxiv.2603.28963"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28963","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.28963","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132635436","display_name":"Mozhgan Pourkeshavatz","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pourkeshavatz, Mozhgan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132670673","display_name":"Tianran Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tianran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077198404","display_name":"Nicholas Rhinehart","orcid":"https://orcid.org/0000-0003-4242-1236"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rhinehart, Nicholas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5132635436"],"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":0.3012000024318695,"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":0.3012000024318695,"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/T10524","display_name":"Traffic control and management","score":0.23960000276565552,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05480000004172325,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6065999865531921},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5796999931335449},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5673999786376953},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5246000289916992},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4821000099182129},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4650000035762787},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4505999982357025},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4357999861240387},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41110000014305115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70169997215271},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5796999931335449},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5246000289916992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5194000005722046},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45559999346733093},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4357999861240387},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C2778391309","wikidata":"https://www.wikidata.org/wiki/Q7832527","display_name":"Traffic simulation","level":3,"score":0.3856000006198883},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38420000672340393},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28963","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.28963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28963","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-agent":[0],"traffic":[1,56,73,83,211],"simulation":[2,57,74,84,184,212],"is":[3],"central":[4],"to":[5,34,71,112,130,173],"developing":[6],"and":[7,140,195,223],"testing":[8],"autonomous":[9],"driving":[10],"systems.":[11],"Recent":[12],"data-driven":[13],"simulators":[14],"have":[15],"achieved":[16],"promising":[17],"results,":[18],"but":[19,50],"rely":[20],"heavily":[21],"on":[22,160,169],"supervised":[23],"learning":[24],"from":[25,92],"labeled":[26],"trajectories":[27],"or":[28],"semantic":[29],"annotations,":[30],"making":[31],"it":[32],"costly":[33],"scale":[35,49],"their":[36],"performance.":[37],"Meanwhile,":[38],"large":[39],"amounts":[40],"of":[41,96,135,156,191,199],"unlabeled":[42,69,93,192],"sensor":[43],"data":[44,70],"can":[45,65],"be":[46],"collected":[47],"at":[48],"remain":[51],"largely":[52],"unused":[53],"by":[54],"existing":[55],"frameworks.":[58],"This":[59],"raises":[60],"a":[61,66,82,88,105,113,124,147],"key":[62],"question:":[63],"How":[64],"method":[67,205],"harness":[68],"improve":[72],"performance?":[75],"In":[76],"this":[77],"work,":[78],"we":[79,145],"propose":[80],"AutoWorld,":[81],"framework":[85,129],"that":[86,152,165,183],"employs":[87],"world":[89,100,138],"model":[90,101,139],"learned":[91],"occupancy":[94],"representations":[95],"LiDAR":[97,193],"data.":[98],"Given":[99],"samples,":[102],"AutoWorld":[103,122,166],"constructs":[104],"coarse-to-fine":[106],"predictive":[107],"scene":[108,157],"context":[109],"as":[110],"input":[111],"multi-agent":[114],"motion":[115,142],"generation":[116],"model.":[117,143],"To":[118],"promote":[119],"sample":[120],"diversity,":[121],"uses":[123],"cascaded":[125],"Determinantal":[126],"Point":[127],"Process":[128],"guide":[131],"the":[132,137,141,161,170,174,189,197,207],"sampling":[133],"processes":[134],"both":[136],"Furthermore,":[144],"designed":[146],"motion-aware":[148],"latent":[149],"supervision":[150],"objective":[151],"enhances":[153],"AutoWorld's":[154],"representation":[155],"dynamics.":[158],"Experiments":[159],"WOSAC":[162],"benchmark":[163],"show":[164,182],"ranks":[167],"first":[168],"leaderboard":[171],"according":[172],"primary":[175],"Realism":[176],"Meta":[177],"Metric":[178],"(RMM).":[179],"We":[180],"further":[181],"performance":[185],"consistently":[186],"improves":[187],"with":[188,202],"inclusion":[190],"data,":[194],"study":[196],"efficacy":[198],"each":[200],"component":[201],"ablations.":[203],"Our":[204,217],"paves":[206],"way":[208],"for":[209],"scaling":[210],"realism":[213],"without":[214],"additional":[215,221],"labeling.":[216],"project":[218],"page":[219],"contains":[220],"visualizations":[222],"released":[224],"code.":[225]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}