{"id":"https://openalex.org/W7151312135","doi":"https://doi.org/10.48550/arxiv.2604.03581","title":"HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving","display_name":"HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151312135","doi":"https://doi.org/10.48550/arxiv.2604.03581"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03581","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.2604.03581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030865590","display_name":"Wenhao Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Wenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015760394","display_name":"Xinglong Sun","orcid":"https://orcid.org/0000-0003-4782-4966"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Xinglong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133072893","display_name":"Zhenxin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhenxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017304880","display_name":"Shiyi Lan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lan, Shiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133135028","display_name":"Zi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133111986","display_name":"Jos\u00e9 Rivera Alvarez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alvarez, Jose M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133117817","display_name":"Zuxuan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zuxuan","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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.698199987411499,"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":0.698199987411499,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.15070000290870667,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.027799999341368675,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.692799985408783},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6061000227928162},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5450999736785889},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.47119998931884766},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43790000677108765},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43140000104904175},{"id":"https://openalex.org/keywords/trajectory-optimization","display_name":"Trajectory optimization","score":0.40220001339912415},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3944999873638153},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.3531000018119812}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.692799985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6886000037193298},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43140000104904175},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.40220001339912415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40139999985694885},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3944999873638153},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29440000653266907},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03581","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.2604.03581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03581","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"End-to-end":[0],"planning":[1,100,109],"has":[2,70],"emerged":[3],"as":[4],"a":[5,16,23,81,103,111,167],"dominant":[6],"paradigm":[7],"for":[8,63],"autonomous":[9],"driving,":[10],"where":[11],"recent":[12],"models":[13],"often":[14,52],"adopt":[15],"scoring-selection":[17],"framework":[18,101],"to":[19,44,140],"choose":[20],"trajectories":[21,55],"from":[22,37],"large":[24],"set":[25],"of":[26],"candidates,":[27],"with":[28,102],"diffusion-based":[29],"decoding":[30],"showing":[31],"strong":[32],"promise.":[33],"However,":[34],"directly":[35],"selecting":[36],"the":[38,58],"entire":[39],"candidate":[40],"space":[41],"remains":[42],"difficult":[43],"optimize,":[45],"and":[46,161,174],"Gaussian":[47],"perturbations":[48],"used":[49],"in":[50],"diffusion":[51],"introduce":[53,119],"unrealistic":[54],"that":[56,107,152],"complicate":[57],"denoising":[59],"process.":[60,113],"In":[61],"addition,":[62],"training":[64],"these":[65,93],"models,":[66],"reinforcement":[67],"learning":[68],"(RL)":[69],"shown":[71],"promise,":[72],"but":[73],"existing":[74],"end-to-end":[75,99],"RL":[76,143],"approaches":[77],"typically":[78],"rely":[79],"on":[80,158,172,178],"single":[82],"coupled":[83],"reward":[84],"without":[85],"structured":[86,142],"signals,":[87],"limiting":[88],"optimization":[89,144],"effectiveness.":[90],"To":[91,114],"address":[92],"challenges,":[94],"we":[95,118,134],"propose":[96],"HAD,":[97],"an":[98],"Hierarchical":[104],"Diffusion":[105],"Policy":[106,137],"decomposes":[108],"into":[110],"coarse-to-fine":[112],"improve":[115],"trajectory":[116],"generation,":[117],"Structure-Preserved":[120],"Trajectory":[121],"Expansion,":[122],"which":[123],"produces":[124],"realistic":[125],"candidates":[126],"while":[127],"maintaining":[128],"kinematic":[129],"structure.":[130],"For":[131],"policy":[132],"learning,":[133],"develop":[135],"Metric-Decoupled":[136],"Optimization":[138],"(MDPO)":[139],"enable":[141],"across":[145],"multiple":[146],"driving":[147],"objectives.":[148],"Extensive":[149],"experiments":[150],"show":[151],"HAD":[153],"achieves":[154],"new":[155],"state-of-the-art":[156],"performance":[157],"both":[159],"NAVSIM":[160,173],"HUGSIM,":[162],"outperforming":[163],"prior":[164],"arts":[165],"by":[166],"huge":[168],"margin:":[169],"+2.3":[170],"EPDMS":[171],"+4.9":[175],"Route":[176],"Completion":[177],"HUGSIM.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}