{"id":"https://openalex.org/W7161744941","doi":"https://doi.org/10.48550/arxiv.2605.17229","title":"Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions","display_name":"Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161744941","doi":"https://doi.org/10.48550/arxiv.2605.17229"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17229","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.2605.17229","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059449514","display_name":"Qian Pu","orcid":"https://orcid.org/0000-0003-3195-7262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pu, Qingwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136456322","display_name":"Kun Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136500829","display_name":"Yuan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5079263500","display_name":"Guocong Zhai","orcid":"https://orcid.org/0000-0003-4054-2376"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhai, Guocong","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":0.8598999977111816,"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":0.8598999977111816,"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/T10525","display_name":"Human-Automation Interaction and Safety","score":0.03880000114440918,"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"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.02630000002682209,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5759000182151794},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5221999883651733},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.46209999918937683},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4456999897956848},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4440999925136566},{"id":"https://openalex.org/keywords/turing","display_name":"Turing","score":0.43560001254081726},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.31470000743865967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192999720573425},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5759000182151794},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5221999883651733},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4498000144958496},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4456999897956848},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C9870796","wikidata":"https://www.wikidata.org/wiki/Q490481","display_name":"Turing","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31470000743865967},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C2780977526","wikidata":"https://www.wikidata.org/wiki/Q42417149","display_name":"Data exploration","level":3,"score":0.29280000925064087},{"id":"https://openalex.org/C80519477","wikidata":"https://www.wikidata.org/wiki/Q3532236","display_name":"Scenario testing","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28850001096725464},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C2778391309","wikidata":"https://www.wikidata.org/wiki/Q7832527","display_name":"Traffic simulation","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17229","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.2605.17229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17229","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"driving":[1],"system":[2],"deployment":[3],"requires":[4],"rigorous":[5],"validation":[6],"across":[7,82],"safety-critical":[8,43,58,192],"vehicle-pedestrian":[9],"interactions,":[10],"yet":[11],"real-world":[12,34,57,86,158,181],"datasets":[13],"rarely":[14],"capture":[15],"high-risk":[16],"scenarios":[17,44],"while":[18],"simulation":[19,38,89],"platforms":[20],"lack":[21],"realistic":[22,42,133],"behavior.":[23],"In":[24],"response,":[25],"this":[26],"study":[27],"proposes":[28],"a":[29,93],"three-stage":[30,173],"framework":[31,174],"that":[32,171],"combines":[33],"grounding":[35],"with":[36,88,101],"adaptive":[37],"to":[39,60,80,91,105],"generate":[40,106],"behaviorally":[41],"at":[45],"scale.":[46],"Stage":[47,69,97],"1":[48],"pre-trains":[49],"multi-agent":[50],"state-space":[51],"Transformer-enhanced":[52],"DDPG":[53],"(MA-SST-DDPG)":[54],"agents":[55],"on":[56],"data":[59,159],"learn":[61],"human-like":[62],"interactive":[63],"evasive":[64,134,176],"behaviors":[65,177],"through":[66],"data-driven":[67],"learning.":[68],"2":[70],"deploys":[71],"pre-trained":[72],"multi-agents":[73],"in":[74,117,131,160,189],"CARLA":[75,100],"for":[76,197],"online":[77],"reinforcement":[78],"learning":[79],"generalize":[81],"diverse":[83],"scenarios,":[84,115],"integrating":[85],"knowledge":[87],"experience":[90],"produce":[92],"refined":[94,103],"MA-SST-DDPG":[95,126],"model.":[96],"3":[98],"uses":[99],"the":[102,118,137,155,172,186,198],"model":[104,127],"over":[107],"198,000":[108],"high-resolution":[109],"interaction":[110],"episodes":[111],"from":[112,180],"eight":[113],"intersection":[114],"culminating":[116],"Vehicle-Pedestrian":[119],"Safety-Critical":[120],"Interaction":[121],"(VPSCI)":[122],"dataset.":[123],"The":[124],"Refined":[125],"outperformed":[128],"baseline":[129],"methods":[130],"reproducing":[132],"behaviors,":[135],"achieving":[136],"lowest":[138],"trajectory":[139],"errors":[140],"(ADE":[141],"=":[142,146],"0.072":[143],"m,":[144],"FDE":[145],"0.142":[147],"m).":[148],"Statistical":[149],"comparison":[150],"confirmed":[151,170],"distributional":[152],"equivalence":[153],"between":[154],"generated":[156,175],"and":[157,164,202],"both":[161],"conflict":[162],"severity":[163],"behavioral":[165],"response.":[166],"A":[167],"Turing":[168],"test":[169],"were":[178],"indistinguishable":[179],"interactions.":[182],"These":[183],"results":[184],"demonstrate":[185],"framework's":[187],"effectiveness":[188],"producing":[190],"high-fidelity":[191],"data,":[193],"offering":[194],"valuable":[195],"sources":[196],"development":[199],"of":[200],"ADS":[201],"simulation-based":[203],"safety":[204],"evaluations.":[205]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
