{"id":"https://openalex.org/W4405778634","doi":"https://doi.org/10.1109/iros58592.2024.10802382","title":"Joint Pedestrian Trajectory Prediction through Posterior Sampling","display_name":"Joint Pedestrian Trajectory Prediction through Posterior Sampling","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4405778634","doi":"https://doi.org/10.1109/iros58592.2024.10802382"},"language":"en","primary_location":{"id":"doi:10.1109/iros58592.2024.10802382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062880407","display_name":"Haotian Lin","orcid":"https://orcid.org/0000-0003-4672-9721"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haotian Lin","raw_affiliation_strings":["University of California,Berkeley"],"affiliations":[{"raw_affiliation_string":"University of California,Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069591959","display_name":"Yixiao Wang","orcid":"https://orcid.org/0000-0003-2262-8256"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixiao Wang","raw_affiliation_strings":["University of California,Berkeley"],"affiliations":[{"raw_affiliation_string":"University of California,Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001015558","display_name":"Mingxiao Huo","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingxiao Huo","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049686906","display_name":"Chensheng Peng","orcid":"https://orcid.org/0000-0001-9213-5970"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chensheng Peng","raw_affiliation_strings":["University of California,Berkeley"],"affiliations":[{"raw_affiliation_string":"University of California,Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320722","display_name":"Zhiyuan Liu","orcid":"https://orcid.org/0000-0002-6331-0810"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Liu","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064077634","display_name":"Masayoshi Tomizuka","orcid":"https://orcid.org/0000-0003-0206-6639"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masayoshi Tomizuka","raw_affiliation_strings":["University of California,Berkeley"],"affiliations":[{"raw_affiliation_string":"University of California,Berkeley","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5062880407"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":0.6502,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69299938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5672","last_page":"5679"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9858999848365784,"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":0.9858999848365784,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.9783999919891357,"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/pedestrian","display_name":"Pedestrian","score":0.7589156627655029},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.7086567878723145},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7005159854888916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6389668583869934},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5185485482215881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40225544571876526},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2613513767719269},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15170294046401978},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09946784377098083},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.060573190450668335}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7589156627655029},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.7086567878723145},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7005159854888916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6389668583869934},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5185485482215881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40225544571876526},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2613513767719269},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15170294046401978},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09946784377098083},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.060573190450668335},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros58592.2024.10802382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1970206276","https://openalex.org/W2532516272","https://openalex.org/W2963001155","https://openalex.org/W2968581240","https://openalex.org/W3034734814","https://openalex.org/W3108908812","https://openalex.org/W3117879208","https://openalex.org/W3193995421","https://openalex.org/W4210457203","https://openalex.org/W4312305613","https://openalex.org/W4312457193","https://openalex.org/W4312497550","https://openalex.org/W4312750092","https://openalex.org/W4312933868","https://openalex.org/W4313131073","https://openalex.org/W4383108499","https://openalex.org/W4386071805","https://openalex.org/W4386076060","https://openalex.org/W4386076407","https://openalex.org/W4390872831","https://openalex.org/W4390889800","https://openalex.org/W4393032949","https://openalex.org/W4393158618","https://openalex.org/W4404002719","https://openalex.org/W6779823529","https://openalex.org/W6781550413","https://openalex.org/W6786375611","https://openalex.org/W6791732101","https://openalex.org/W6792369536","https://openalex.org/W6838452192","https://openalex.org/W6838483015","https://openalex.org/W6838883754","https://openalex.org/W6841811669","https://openalex.org/W6844364769","https://openalex.org/W6850397872","https://openalex.org/W6857230591","https://openalex.org/W6862833997"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W2170799233","https://openalex.org/W2378391535"],"abstract_inverted_index":{"Joint":[0],"pedestrian":[1],"trajectory":[2,21,78,92,142],"prediction":[3,22,65,143],"has":[4],"long":[5],"grappled":[6],"with":[7,113,144],"the":[8,28,47,53,67,75,85,90,106,121],"inherent":[9],"unpredictability":[10],"of":[11,70],"human":[12],"behaviors.":[13],"Recent":[14],"works":[15],"employing":[16],"conditional":[17],"diffusion":[18],"models":[19,74],"in":[20,36,140,149],"have":[23],"exhibited":[24],"notable":[25],"success.":[26],"Nevertheless,":[27],"heavy":[29],"dependence":[30],"on":[31],"accurate":[32,102],"historical":[33,118],"data":[34,43,146],"results":[35,155],"their":[37],"vulnerability":[38],"to":[39,111],"noise":[40,114],"disturbances":[41],"and":[42,49,73,84,93,148],"incompleteness.":[44],"To":[45],"improve":[46],"robustness":[48,107],"reliability,":[50],"we":[51],"introduce":[52],"Guided":[54],"Full":[55],"Trajectory":[56],"Diffuser":[57],"(GFTD),":[58],"a":[59],"novel":[60],"diffusion-based":[61],"framework":[62],"that":[63,108],"translates":[64],"as":[66],"inverse":[68],"problem":[69],"spatial-temporal":[71],"inpainting":[72],"full":[76,91],"joint":[77,141],"distribution":[79],"which":[80],"includes":[81],"both":[82],"history":[83],"future.":[86],"By":[87],"learning":[88],"from":[89],"leveraging":[94],"flexible":[95],"posterior":[96],"sampling":[97],"methods,":[98],"GFTD":[99,136],"can":[100,109],"produce":[101],"predictions":[103],"while":[104],"improving":[105],"generalize":[110],"scenarios":[112],"perturbation":[115],"or":[116],"incomplete":[117],"data.":[119],"Moreover,":[120],"pre-trained":[122],"model":[123],"enables":[124],"controllable":[125,150],"generation":[126,151],"without":[127],"an":[128],"additional":[129],"training":[130],"budget.":[131],"Through":[132],"rigorous":[133],"experimental":[134],"evaluation,":[135],"exhibits":[137],"superior":[138],"performance":[139],"different":[145],"quality":[147],"tasks.":[152],"See":[153],"more":[154],"at":[156],"https://sites.google.com/andrew.cmu.edu/posterior-sampling-prediction.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
