{"id":"https://openalex.org/W7162635241","doi":"https://doi.org/10.48550/arxiv.2605.28544","title":"DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving","display_name":"DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162635241","doi":"https://doi.org/10.48550/arxiv.2605.28544"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28544","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.2605.28544","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137252047","display_name":"Chen Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137307629","display_name":"Jinrui Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jinrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137200016","display_name":"Shaoshuai Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Shaoshuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016509766","display_name":"Kehua Sheng","orcid":"https://orcid.org/0009-0008-3370-7711"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Kehua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137202970","display_name":"Bo Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137238015","display_name":"Li Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Li","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9287999868392944,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9287999868392944,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.032600000500679016,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.005100000184029341,"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/prior-probability","display_name":"Prior probability","score":0.6570000052452087},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5554999709129333},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.43549999594688416},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.42329999804496765},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3668999969959259},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.36489999294281006},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3612000048160553},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.33730000257492065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7994999885559082},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6570000052452087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326000094413757},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5554999709129333},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.42329999804496765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3537999987602234},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.33730000257492065},{"id":"https://openalex.org/C519536355","wikidata":"https://www.wikidata.org/wiki/Q21021151","display_name":"Repurposing","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28544","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.2605.28544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28544","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":[{"id":"https://metadata.un.org/sdg/16","score":0.44161102175712585,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pretrained":[0],"foundation":[1],"models":[2,16,25],"have":[3],"become":[4],"an":[5,53],"important":[6],"basis":[7],"for":[8,36,172],"end-to-end":[9,173],"autonomous":[10,174],"driving.":[11,37,175],"In":[12],"contrast":[13],"to":[14,88,109,160],"vision-language":[15],"pretrained":[17,48,79],"primarily":[18],"on":[19,141],"static":[20],"image-text":[21],"pairs,":[22],"video":[23,49,59,86,128],"generative":[24],"capture":[26],"temporal":[27,66],"dynamics":[28],"and":[29,60,69,129,143,154],"motion":[30],"priors":[31,87],"that":[32,45,148],"are":[33],"naturally":[34],"suited":[35],"We":[38],"present":[39],"DriveWAM,":[40],"a":[41,47,64,73,102,155],"driving":[42,99,162],"world-action":[43,170],"model":[44],"adapts":[46],"diffusion":[50],"transformer":[51],"into":[52,63],"autoregressive":[54],"video-action":[55,111],"policy.":[56],"DriveWAM":[57,149],"organizes":[58],"action":[61,89,130],"streams":[62],"unified":[65],"token":[67],"sequence":[68],"trains":[70],"them":[71],"under":[72],"joint":[74],"flow-matching":[75],"objective,":[76],"preserving":[77],"the":[78,144,166],"video-generation":[80],"architecture":[81],"while":[82],"adapting":[83],"its":[84],"large-scale":[85],"generation.":[90,112],"To":[91,113],"incorporate":[92],"high-level":[93],"scene":[94],"understanding,":[95],"we":[96,118],"introduce":[97,120],"scene-evolving":[98],"guidance,":[100],"where":[101],"frozen":[103],"VLM":[104],"produces":[105],"chunk-specific":[106],"semantic":[107],"intent":[108],"guide":[110],"keep":[114],"long-horizon":[115],"rollout":[116],"bounded,":[117],"further":[119,164],"selective":[121],"KV":[122],"memory,":[123],"which":[124],"maintains":[125],"bounded":[126],"modality-aware":[127],"memory":[131],"pools":[132],"through":[133],"relevance-redundancy":[134],"cache":[135],"selection":[136],"at":[137],"inference":[138],"time.":[139],"Experiments":[140],"NAVSIM":[142],"PhysicalAI-Autonomous-Vehicles":[145],"benchmark":[146],"show":[147],"achieves":[150],"strong":[151],"planning":[152],"performance,":[153],"data-scaling":[156],"study":[157],"from":[158],"4k":[159],"100k":[161],"clips":[163],"confirms":[165],"scaling":[167],"potential":[168],"of":[169],"modeling":[171]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-29T00:00:00"}
