{"id":"https://openalex.org/W7140191730","doi":"https://doi.org/10.48550/arxiv.2603.20232","title":"Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving","display_name":"Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving","publication_year":2026,"publication_date":"2026-03-07","ids":{"openalex":"https://openalex.org/W7140191730","doi":"https://doi.org/10.48550/arxiv.2603.20232"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20232","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20232","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.2603.20232","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiong, Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Ziwen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ziwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Deqi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Deqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Yiyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Gou, Chao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gou, Chao","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":0.9476000070571899,"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":0.9476000070571899,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.004699999932199717,"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/T10525","display_name":"Human-Automation Interaction and Safety","score":0.003100000089034438,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/risk-management","display_name":"Risk management","score":0.420199990272522},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.41780000925064087},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.41609999537467957},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4147999882698059},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4108000099658966},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3953999876976013},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.39480000734329224},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.36010000109672546},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.33379998803138733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291000247001648},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3971000015735626},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3165000081062317},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.262800008058548},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2551000118255615},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20232","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20232","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.2603.20232","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20232","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":[{"score":0.700697660446167,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"driving":[1,14],"testing":[2],"increasingly":[3],"relies":[4],"on":[5,22,165],"mining":[6],"safety":[7],"critical":[8],"scenarios":[9],"from":[10],"large":[11,104],"scale":[12,105],"naturalistic":[13],"data,":[15],"yet":[16],"existing":[17],"screening":[18,55],"pipelines":[19],"still":[20],"depend":[21],"manual":[23],"risk":[24,31,40,50,79,90,99,117,153,161,181,210],"annotation":[25],"and":[26,37,101,107,120,127,139,162,168,178,191,201,212],"expensive":[27],"frame":[28,30,98],"by":[29,198],"evaluation,":[32],"resulting":[33],"in":[34],"low":[35],"efficiency":[36],"weakly":[38],"grounded":[39],"quantification.":[41],"To":[42],"address":[43],"this":[44],"issue,":[45],"we":[46],"propose":[47],"a":[48,71,115,121,152],"driver":[49],"fusion":[51],"based":[52],"hazardous":[53,213],"scenario":[54,88,214],"method":[56,63,175],"for":[57,144,208],"autonomous":[58],"driving.":[59],"During":[60],"training,":[61],"the":[62,128,166,173],"combines":[64],"an":[65,187,192],"improved":[66,110],"Driver":[67,111],"Risk":[68,112],"Field":[69,113],"with":[70],"dynamic":[72,129],"cost":[73,130],"model":[74,131],"to":[75,158],"generate":[76],"high":[77],"quality":[78],"supervision":[80],"signals,":[81],"while":[82],"during":[83],"inference":[84],"it":[85,185],"directly":[86],"predicts":[87],"level":[89],"scores":[91],"through":[92],"fast":[93],"forward":[94],"passes,":[95],"avoiding":[96],"per":[97],"computation":[100],"enabling":[102],"efficient":[103],"ranking":[106],"retrieval.":[108],"The":[109],"introduces":[114],"new":[116],"height":[118],"function":[119],"speed":[122],"adaptive":[123],"look":[124],"ahead":[125],"mechanism,":[126],"integrates":[132],"kinetic":[133],"energy,":[134],"oriented":[135],"bounding":[136],"box":[137],"constraints,":[138],"Gaussian":[140],"kernel":[141],"diffusion":[142],"smoothing":[143],"more":[145,179],"accurate":[146],"interaction":[147],"modeling.":[148],"We":[149],"further":[150],"design":[151],"trajectory":[154],"cross":[155],"attention":[156],"decoder":[157],"jointly":[159],"decode":[160],"trajectories.":[163],"Experiments":[164],"INTERACTION":[167],"FLUID":[169],"datasets":[170],"show":[171],"that":[172],"proposed":[174],"produces":[176],"smoother":[177],"discriminative":[180],"estimates.":[182],"On":[183],"FLUID,":[184],"achieves":[186],"AUC":[188],"of":[189,194],"0.792":[190],"AP":[193],"0.825,":[195],"outperforming":[196],"PODAR":[197],"9.1":[199],"percent":[200],"5.1":[202],"percent,":[203],"respectively,":[204],"demonstrating":[205],"its":[206],"effectiveness":[207],"scalable":[209],"labeling":[211],"screening.":[215]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}