{"id":"https://openalex.org/W7141362800","doi":"https://doi.org/10.48550/arxiv.2603.25462","title":"Temporally Decoupled Diffusion Planning for Autonomous Driving","display_name":"Temporally Decoupled Diffusion Planning for Autonomous Driving","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141362800","doi":"https://doi.org/10.48550/arxiv.2603.25462"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25462","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.2603.25462","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130794179","display_name":"Xiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Xiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075202802","display_name":"Bikun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bikun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130755290","display_name":"John Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, John","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100410426","display_name":"Jianjun Wang","orcid":"https://orcid.org/0000-0002-5344-4460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianjun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5130794179"],"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":0.6657999753952026,"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":0.6657999753952026,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.1492999941110611,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.04410000145435333,"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/noise","display_name":"Noise (video)","score":0.5846999883651733},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5644000172615051},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5450999736785889},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.4643000066280365},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45879998803138733},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4569000005722046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621999740600586},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5846999883651733},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5450999736785889},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45879998803138733},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4569000005722046},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.438400000333786},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C28427503","wikidata":"https://www.wikidata.org/wiki/Q13580300","display_name":"Internal model","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25462","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.2603.25462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25462","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8204642534255981}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Motion":[0],"planning":[1],"in":[2,148],"dynamic":[3],"urban":[4],"environments":[5],"requires":[6],"balancing":[7],"immediate":[8,131],"safety":[9],"with":[10,68,99],"long-term":[11],"goals.":[12,44],"While":[13],"diffusion":[14],"models":[15],"effectively":[16],"capture":[17],"multi-modal":[18],"decision-making,":[19],"existing":[20],"approaches":[21,141],"treat":[22,74],"trajectories":[23,65],"as":[24,77,83],"monolithic":[25],"entities,":[26],"overlooking":[27],"heterogeneous":[28],"temporal":[29,101],"dependencies":[30],"where":[31],"near-term":[32,93],"plans":[33,41],"are":[34],"constrained":[35],"by":[36,42,95],"instantaneous":[37],"dynamics":[38],"and":[39,80],"far-term":[40,127],"navigational":[43],"To":[45],"address":[46],"this,":[47],"we":[48,72,104],"propose":[49],"Temporally":[50,107],"Decoupled":[51,108],"Diffusion":[52],"Model":[53],"(TDDM),":[54],"which":[55],"reformulates":[56],"trajectory":[57],"generation":[58],"via":[59],"a":[60,106],"noise-as-mask":[61],"paradigm.":[62],"By":[63],"partitioning":[64],"into":[66],"segments":[67],"independent":[69],"noise":[70,76,82],"levels,":[71],"implicitly":[73],"high":[75],"information":[78],"voids":[79],"weak":[81],"contextual":[84],"cues.":[85],"This":[86],"compels":[87],"the":[88,136,149],"model":[89],"to":[90,113,129],"reconstruct":[91],"corrupted":[92],"states":[94],"leveraging":[96],"internal":[97],"correlations":[98],"better-preserved":[100],"contexts.":[102],"Architecturally,":[103],"introduce":[105],"Adaptive":[109],"Layer":[110],"Normalization":[111],"(TD-AdaLN)":[112],"inject":[114],"segment-specific":[115],"timesteps.":[116],"During":[117],"inference,":[118],"our":[119],"Asymmetric":[120],"Temporal":[121],"Classifier-Free":[122],"Guidance":[123],"utilizes":[124],"weakly":[125],"noised":[126],"priors":[128],"guide":[130],"path":[132],"generation.":[133],"Evaluations":[134],"on":[135],"nuPlan":[137],"benchmark":[138],"show":[139],"TDDM":[140],"or":[142],"exceeds":[143],"state-of-the-art":[144],"baselines,":[145],"particularly":[146],"excelling":[147],"challenging":[150],"Test14-hard":[151],"subset.":[152]},"counts_by_year":[],"updated_date":"2026-03-28T06:16:51.555046","created_date":"2026-03-28T00:00:00"}