{"id":"https://openalex.org/W7161237039","doi":"https://doi.org/10.48550/arxiv.2605.15120","title":"CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning","display_name":"CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161237039","doi":"https://doi.org/10.48550/arxiv.2605.15120"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15120","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15120","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.15120","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125729321","display_name":"Sining Ang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ang, Sining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136195396","display_name":"Yuguang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yuguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136260413","display_name":"Canyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Canyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136198899","display_name":"Yan Wang","orcid":"https://orcid.org/0000-0001-9474-6396"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yan","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":0.8654000163078308,"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":0.8654000163078308,"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":0.07349999994039536,"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":0.019099999219179153,"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/ranking","display_name":"Ranking (information retrieval)","score":0.6988000273704529},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.663100004196167},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5127999782562256},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5083000063896179},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.4733000099658966},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.46480000019073486},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.45320001244544983},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45179998874664307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7957000136375427},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6988000273704529},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.663100004196167},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5083000063896179},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45179998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3253999948501587},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31790000200271606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30959999561309814},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15120","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15120","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.15120","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15120","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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],"autonomous":[1,82],"driving":[2,83],"planners":[3],"are":[4,179],"commonly":[5],"trained":[6],"by":[7,15],"imitating":[8],"a":[9,28,73,87,91,98,199],"single":[10],"logged":[11,35],"trajectory,":[12],"yet":[13],"evaluated":[14],"rule-based":[16],"planning":[17,39],"metrics":[18],"that":[19,171],"measure":[20],"safety,":[21],"feasibility,":[22],"progress,":[23],"and":[24,50,66,77,97,121,154,185,195,231],"comfort.":[25],"This":[26],"creates":[27],"training--evaluation":[29],"mismatch:":[30],"trajectories":[31,120],"close":[32],"to":[33,103,139],"the":[34,45,123,135,147,168,182,203,206,216,227],"path":[36],"may":[37],"violate":[38],"rules,":[40],"while":[41,146],"alternatives":[42],"farther":[43],"from":[44],"demonstration":[46],"can":[47,166],"remain":[48,187],"valid":[49],"high-scoring.":[51],"The":[52],"mismatch":[53],"is":[54,137,149,174],"especially":[55],"limiting":[56],"for":[57,80],"proposal-selection":[58],"planners,":[59],"whose":[60],"performance":[61],"depends":[62],"on":[63,143],"candidate-set":[64],"coverage":[65,127],"scorer":[67,99,136,165],"ranking":[68],"quality.":[69],"We":[70,160],"propose":[71],"CLOVER,":[72],"Closed-LOop":[74],"Value":[75],"Estimation":[76],"Ranking":[78],"framework":[79],"end-to-end":[81],"planning.":[84],"CLOVER":[85,116,191,225],"follows":[86],"lightweight":[88],"generator--scorer":[89],"formulation:":[90],"generator":[92,124,148],"produces":[93],"diverse":[94],"candidate":[95],"trajectories,":[96],"predicts":[100],"planning-metric":[101],"sub-scores":[102,142],"rank":[104],"them":[105],"at":[106,242],"inference":[107],"time.":[108],"To":[109],"expand":[110],"proposal":[111],"support":[112],"beyond":[113],"single-trajectory":[114],"imitation,":[115],"constructs":[117],"evaluator-filtered":[118],"pseudo-expert":[119],"trains":[122],"with":[125,157],"set-level":[126],"supervision.":[128],"It":[129],"then":[130],"performs":[131],"conservative":[132],"closed-loop":[133],"self-distillation:":[134],"fitted":[138],"true":[140,183],"evaluator":[141,184],"generated":[144],"proposals,":[145],"refined":[150],"toward":[151],"teacher-selected":[152],"top-$k$":[153],"vector-Pareto":[155],"targets":[156,178],"stability":[158],"regularization.":[159],"analyze":[161],"when":[162,176],"an":[163],"imperfect":[164],"improve":[167],"generator,":[169],"showing":[170],"scorer-mediated":[172],"refinement":[173],"reliable":[175],"scorer-selected":[177],"enriched":[180],"under":[181],"updates":[186],"conservative.":[188],"On":[189,205,220],"NAVSIM,":[190],"achieves":[192,226],"94.5":[193],"PDMS":[194],"90.4":[196],"EPDMS,":[197,214],"establishing":[198],"new":[200],"state":[201],"of":[202],"art.":[204],"more":[207],"challenging":[208],"NavHard":[209],"split,":[210],"it":[211],"obtains":[212],"48.3":[213],"matching":[215],"strongest":[217],"reported":[218],"result.":[219],"supplementary":[221],"nuScenes":[222],"open-loop":[223],"evaluation,":[224],"lowest":[228],"L2":[229],"error":[230],"collision":[232],"rate":[233],"among":[234],"compared":[235],"methods.":[236],"Code":[237],"data":[238],"will":[239],"be":[240],"released":[241],"https://github.com/WilliamXuanYu/CLOVER.":[243]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}
