{"id":"https://openalex.org/W7161960100","doi":"https://doi.org/10.48550/arxiv.2605.20255","title":"Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty","display_name":"Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161960100","doi":"https://doi.org/10.48550/arxiv.2605.20255"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20255","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20255","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.2605.20255","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136622103","display_name":"Prakash Aryan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aryan, Prakash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029351705","display_name":"Kaushik Raghupathruni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghupathruni, Kaushik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078065547","display_name":"Timo Kehrer","orcid":"https://orcid.org/0000-0002-2582-5557"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kehrer, Timo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136700656","display_name":"Sebastiano Panichella","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panichella, Sebastiano","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":0.8781999945640564,"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":0.8781999945640564,"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/T10370","display_name":"Traffic and Road Safety","score":0.03610000014305115,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.025200000032782555,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/pedestrian","display_name":"Pedestrian","score":0.8363000154495239},{"id":"https://openalex.org/keywords/schema-crosswalk","display_name":"Schema crosswalk","score":0.7656999826431274},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6388999819755554},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.573199987411499},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5496000051498413},{"id":"https://openalex.org/keywords/pedestrian-crossing","display_name":"Pedestrian crossing","score":0.5199000239372253},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4586000144481659}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.8363000154495239},{"id":"https://openalex.org/C121193887","wikidata":"https://www.wikidata.org/wiki/Q7431117","display_name":"Schema crosswalk","level":3,"score":0.7656999826431274},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6388999819755554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6359000205993652},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.573199987411499},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5496000051498413},{"id":"https://openalex.org/C2777819797","wikidata":"https://www.wikidata.org/wiki/Q8010","display_name":"Pedestrian crossing","level":3,"score":0.5199000239372253},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.428600013256073},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.4171000123023987},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3792000114917755},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3197999894618988},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C2780689630","wikidata":"https://www.wikidata.org/wiki/Q2081815","display_name":"Driving simulator","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C106934330","wikidata":"https://www.wikidata.org/wiki/Q1971873","display_name":"Trait","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20255","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20255","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.2605.20255","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20255","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":{"Simulation-based":[0],"testing":[1],"of":[2,20,27,137,182,187],"self-driving":[3],"cars":[4],"(SDCs)":[5],"typically":[6],"relies":[7],"on":[8,116],"scripted":[9,101],"pedestrian":[10,67,98],"models":[11],"that":[12,46,70],"do":[13],"not":[14,177],"capture":[15],"the":[16,25,40,51,71,127,132,147,156,205],"heterogeneity":[17],"and":[18,50,69,76,89,112,124,189],"uncertainty":[19],"real":[21],"crossing":[22,183],"behavior,":[23],"limiting":[24],"realism":[26],"safety":[28],"assessments,":[29],"especially":[30],"for":[31,146],"jaywalking,":[32],"which":[33],"is":[34],"governed":[35],"by":[36,193],"latent":[37],"personality":[38],"traits":[39],"vehicle":[41],"cannot":[42],"observe.":[43],"We":[44,85],"hypothesize":[45],"jointly":[47],"training":[48,64],"pedestrians":[49,91,200],"SDC":[52,88,134,157,206],"with":[53,139],"multi-agent":[54],"reinforcement":[55],"learning":[56],"(MARL)":[57],"yields":[58],"more":[59],"realistic":[60],"interaction":[61],"scenarios":[62],"than":[63,164],"against":[65],"fixed":[66],"policies,":[68],"behavior":[72],"gap":[73],"between":[74],"predictable":[75],"unpredictable":[77],"crossings":[78],"can":[79],"be":[80],"measured":[81],"directly":[82],"from":[83,126],"trajectories.":[84],"co-train":[86],"an":[87,105],"12":[90],"using":[92],"Multi-Agent":[93],"Proximal":[94],"Policy":[95],"Optimization":[96],"(MAPPO):":[97],"locomotion":[99],"follows":[100],"Dijkstra":[102],"pathfinding":[103],"while":[104],"RL":[106,198],"policy":[107],"controls":[108],"high-level":[109],"go/wait":[110],"decisions,":[111],"jaywalking":[113,174],"probability":[114],"depends":[115],"a":[117,140],"per-pedestrian":[118],"trait":[119],"sampled":[120],"at":[121,168,208],"episode":[122],"start":[123],"hidden":[125],"SDC.":[128],"In":[129],"500-episode":[130],"evaluations,":[131],"co-trained":[133],"reached":[135],"78%":[136],"goals":[138],"14%":[141],"collision":[142],"rate,":[143],"versus":[144],"35%/33%":[145],"best":[148],"rule-based":[149],"baseline.":[150],"A":[151],"speed":[152],"differential":[153],"metric":[154],"shows":[155],"traveled":[158],"2.65":[159],"m/s":[160],"faster":[161],"near":[162,165],"jaywalkers":[163],"crosswalk":[166],"users":[167],"close":[169],"range":[170],"(0-3":[171],"m),":[172],"indicating":[173],"encounters":[175],"were":[176],"anticipated.":[178],"Jaywalking":[179],"was":[180],"13%":[181],"events":[184],"but":[185],"62%":[186],"collisions,":[188],"co-training":[190],"reduced":[191],"collisions":[192],"30%":[194],"relative":[195],"to":[196,202],"single-agent":[197],"as":[199],"learned":[201],"wait":[203],"when":[204],"approached":[207],"speed.":[209]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-22T00:00:00"}
