{"id":"https://openalex.org/W7160598780","doi":"https://doi.org/10.48550/arxiv.2605.06264","title":"Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving","display_name":"Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160598780","doi":"https://doi.org/10.48550/arxiv.2605.06264"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06264","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06264","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":"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.2605.06264","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135697822","display_name":"Le Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Le","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135666312","display_name":"Ruoyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135658764","display_name":"Haijun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Haijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009016727","display_name":"Jiawei Liang","orcid":"https://orcid.org/0000-0003-1143-6873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130171564","display_name":"Shangquan Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, ShangQuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135663860","display_name":"Xiaochun Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Xiaochun","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":0.9514999985694885,"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":0.9514999985694885,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0071000000461936,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.006200000178068876,"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/attribution","display_name":"Attribution","score":0.8672999739646912},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.40860000252723694},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.35989999771118164},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.35010001063346863},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3497999906539917},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3075999915599823}],"concepts":[{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.8672999739646912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49399998784065247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48190000653266907},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.40860000252723694},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.29319998621940613},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06264","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06264","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":"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.2605.06264","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06264","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":"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],"driving":[2],"models":[3,27],"generate":[4,29],"future":[5],"trajectories":[6],"from":[7,35,57],"multi-view":[8],"inputs,":[9],"improving":[10],"system":[11],"integration":[12],"but":[13,32],"introducing":[14],"opaque":[15],"decisions":[16],"and":[17,39,67,79,81,135,182,201,221,239],"hard-to-localize":[18],"risks.":[19],"Existing":[20],"methods":[21],"either":[22],"rely":[23],"on":[24,84,198],"auxiliary":[25],"monitoring":[26],"or":[28],"textual":[30],"explanations,":[31],"are":[33],"decoupled":[34],"the":[36,43,113,117,131,159,164,176,194],"planning":[37,55,150,209],"process":[38],"fail":[40],"to":[41,74,155,171,186,233],"reveal":[42],"visual":[44,166],"evidence":[45],"underlying":[46],"trajectory":[47,115,219],"generation.":[48],"While":[49],"attribution":[50,73,88,103,124,137,144,153,177,190,245],"offers":[51],"a":[52,101,121],"direct":[53],"alternative,":[54],"differs":[56],"image":[58],"classification":[59],"by":[60],"taking":[61],"six-view":[62,133],"camera":[63],"images":[64],"as":[65,116,146],"input":[66,134],"predicting":[68],"continuous":[69],"multi-step":[70],"trajectories,":[71],"requiring":[72],"capture":[75],"both":[76],"critical":[77],"views":[78],"regions":[80,129],"their":[82],"influence":[83],"outputs.":[85],"Moreover,":[86],"whether":[87],"maps":[89],"can":[90],"support":[91],"risk":[92],"identification":[93],"remains":[94,240],"underexplored.":[95],"To":[96],"address":[97],"this,":[98],"we":[99,119],"propose":[100],"hierarchical":[102],"framework":[104],"for":[105,149,227],"end-to-end":[106],"planning.":[107],"Specifically,":[108],"using":[109],"L2":[110],"consistency":[111],"with":[112,208,218,236],"original":[114],"objective,":[118],"design":[120],"coarse-to-fine":[122],"region":[123],"strategy":[125],"that":[126,204],"searches":[127],"candidate":[128],"across":[130,193],"full":[132],"refines":[136],"within":[138,179],"them.":[139],"We":[140],"further":[141],"extract":[142],"three":[143],"statistics":[145,206],"predictive":[147],"signals":[148],"risk,":[151,210],"including":[152],"entropy":[154],"measure":[156],"how":[157,173,188],"concentrated":[158],"planner's":[160],"reliance":[161],"is":[162,178,191],"over":[163],"joint":[165],"space,":[167],"within-camera":[168],"spatial":[169],"variance":[170],"characterize":[172],"spread":[174],"out":[175],"each":[180],"view,":[181],"cross-camera":[183],"Gini":[184],"coefficient":[185],"quantify":[187],"unevenly":[189],"distributed":[192],"six":[195],"cameras.":[196],"Experiments":[197],"BridgeAD,":[199],"UniAD,":[200],"GenAD":[202],"show":[203],"these":[205],"correlate":[207],"achieving":[211],"Spearman":[212],"correlations":[213],"of":[214,223],"$0.30":[215],"\\pm":[216,225],"0.07$":[217],"error":[220],"AUROC":[222],"$0.77":[224],"0.04$":[226],"collision":[228],"detection.":[229],"The":[230],"signal":[231],"generalizes":[232],"held-out":[234],"scenes":[235],"negligible":[237],"degradation":[238],"stable":[241],"under":[242],"an":[243],"alternative":[244],"baseline.":[246]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
