{"id":"https://openalex.org/W7163378791","doi":"https://doi.org/10.48550/arxiv.2606.03296","title":"Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving","display_name":"Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163378791","doi":"https://doi.org/10.48550/arxiv.2606.03296"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03296","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.2606.03296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023863179","display_name":"Chengzhen Meng","orcid":"https://orcid.org/0009-0006-4285-1905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Chengzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137731169","display_name":"Pei Liu (93392)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137786978","display_name":"Zhiyu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zhiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137735823","display_name":"Chen Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137795761","display_name":"Jun Ma (9393)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jun","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":0.879800021648407,"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":0.879800021648407,"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":0.049300000071525574,"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":0.018400000408291817,"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/bridging","display_name":"Bridging (networking)","score":0.6990000009536743},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.633400022983551},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6061000227928162},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4918000102043152},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48649999499320984},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4611000120639801},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45680001378059387},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4242999851703644}],"concepts":[{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.6990000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6751000285148621},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.633400022983551},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4918000102043152},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4611000120639801},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45680001378059387},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.42149999737739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39169999957084656},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296999931335449},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.31769999861717224},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.2976999878883362},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03296","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.2606.03296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03296","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/11","display_name":"Sustainable cities and communities","score":0.6980744004249573}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Complex,":[0],"dynamic,":[1],"and":[2,35,155,162,170,183],"interactive":[3],"driving":[4],"environments":[5],"pose":[6],"significant":[7],"challenges":[8],"for":[9,144],"autonomous":[10],"driving,":[11],"primarily":[12],"due":[13],"to":[14,87,123,132],"the":[15,28,62,121,166],"pervasive":[16],"uncertainty":[17,33,110],"of":[18,92],"surrounding":[19],"traffic.":[20],"A":[21],"fundamental":[22],"bottleneck":[23],"in":[24,151,180],"current":[25],"systems":[26],"is":[27,130],"disconnect":[29],"between":[30],"highly":[31],"expressive":[32],"modeling":[34],"interpretable,":[36],"safe":[37],"motion":[38],"planning.":[39],"In":[40],"this":[41,53],"paper,":[42],"we":[43,138],"propose":[44],"a":[45,70,83,89,103,125,140],"novel":[46],"sample-conditioned":[47],"differentiable":[48,104],"planning":[49],"framework":[50,175],"that":[51,129,147],"bridges":[52],"gap":[54],"by":[55],"explicitly":[56,107],"incorporating":[57],"diffusion-generated":[58],"future":[59,73,94],"trajectories":[60],"into":[61,69,102],"optimization":[63],"process.":[64],"Rather":[65],"than":[66],"compressing":[67],"predictions":[68],"single":[71],"deterministic":[72],"or":[74],"relying":[75],"on":[76,165],"black-box":[77],"end-to-end":[78],"architectures,":[79],"our":[80,174],"approach":[81],"leverages":[82],"conditional":[84],"diffusion":[85],"model":[86],"generate":[88],"diverse":[90],"set":[91],"plausible":[93],"scenarios.":[95],"Crucially,":[96],"these":[97],"samples":[98],"are":[99],"directly":[100],"fed":[101],"planner,":[105],"which":[106],"mitigates":[108],"predictive":[109,153],"via":[111],"an":[112],"empirical":[113],"Conditional":[114],"Value-at-Risk":[115],"(CVaR)":[116],"tail-risk":[117],"constraint.":[118],"This":[119],"allows":[120],"planner":[122],"optimize":[124],"physically":[126],"interpretable":[127],"trajectory":[128],"robust":[131],"rare":[133],"yet":[134],"safety-critical":[135],"interactions.":[136],"Furthermore,":[137],"introduce":[139],"directed":[141],"graph":[142],"representation":[143],"scene":[145],"context":[146],"yields":[148],"substantial":[149],"improvements":[150],"both":[152],"effectiveness":[154],"computational":[156],"efficiency.":[157],"Validated":[158],"through":[159],"extensive":[160],"open-loop":[161],"closed-loop":[163],"evaluations":[164],"Waymo":[167],"Open":[168],"Motion":[169],"Argoverse":[171],"2":[172],"datasets,":[173],"significantly":[176],"outperforms":[177],"state-of-the-art":[178],"baselines":[179],"safety,":[181],"efficiency,":[182],"ride":[184],"comfort.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}