{"id":"https://openalex.org/W4415707638","doi":"https://doi.org/10.1109/tvt.2025.3627215","title":"DiffMATP: Interaction-Aware Multi-Agent Trajectory Prediction via Denoising Diffusion Models","display_name":"DiffMATP: Interaction-Aware Multi-Agent Trajectory Prediction via Denoising Diffusion Models","publication_year":2025,"publication_date":"2025-10-30","ids":{"openalex":"https://openalex.org/W4415707638","doi":"https://doi.org/10.1109/tvt.2025.3627215"},"language":null,"primary_location":{"id":"doi:10.1109/tvt.2025.3627215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3627215","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-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/A5063155651","display_name":"Chaoneng Li","orcid":"https://orcid.org/0000-0002-2781-4683"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoneng Li","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2781-4683","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100424248","display_name":"Xiaolong Wang","orcid":"https://orcid.org/0000-0001-6137-7336"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6137-7336","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039467079","display_name":"Shuxu Zhao","orcid":"https://orcid.org/0000-0001-8521-5833"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuxu Zhao","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8521-5833","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056499160","display_name":"Xiaohu Wang","orcid":"https://orcid.org/0000-0002-0943-1427"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohu Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0943-1427","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102964108","display_name":"Ze Ye","orcid":"https://orcid.org/0000-0002-8169-2242"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ze Ye","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8169-2242","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3133134087"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28969228,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"75","issue":"5","first_page":"7222","last_page":"7235"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9426000118255615,"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":0.9426000118255615,"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/T10524","display_name":"Traffic control and management","score":0.0142000000923872,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.00419999985024333,"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/trajectory","display_name":"Trajectory","score":0.7843999862670898},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.5813000202178955},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5565999746322632},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.49050000309944153},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4325999915599823},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.42260000109672546},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.414900004863739},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4052000045776367},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4016000032424927}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7843999862670898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6582000255584717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.611299991607666},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5813000202178955},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5565999746322632},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.49050000309944153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4372999966144562},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.414900004863739},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4016000032424927},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.349700003862381},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3627215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3627215","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"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":{"In":[0,83],"the":[1,9,22,36,49,107,132,146,195,203],"realm":[2],"of":[3,12,40,51,109,189,197],"intelligent":[4],"transportation":[5],"and":[6,28,42,65,79,160,177,191],"collaborative":[7],"robotics,":[8],"accurate":[10],"prediction":[11,32,98,113,133],"multi-agent":[13,96,208],"trajectories":[14,39],"has":[15],"emerged":[16],"as":[17,120],"a":[18,121,141],"critical":[19],"challenge":[20],"amid":[21],"ongoing":[23],"advancements":[24],"in":[25,55,187,201],"autonomous":[26],"driving":[27],"robotics":[29],"technologies.":[30],"Trajectory":[31],"aims":[33],"to":[34,74,85,128,167],"observe":[35],"historical":[37],"motion":[38,118],"agents":[41],"predict":[43],"their":[44],"likely":[45],"future":[46],"locations.":[47],"Despite":[48],"potential":[50,196],"deep":[52],"generative":[53],"models":[54,166,200],"this":[56],"domain,":[57],"popular":[58],"approaches":[59],"like":[60],"Generative":[61],"Adversarial":[62],"Networks":[63],"(GANs)":[64],"Variational":[66],"AutoEncoders":[67],"(VAEs)":[68],"often":[69],"grapple":[70],"with":[71,207],"concerns":[72],"related":[73],"unsteady":[75],"training,":[76],"mode":[77],"collapse,":[78],"subpar":[80],"sample":[81],"quality.":[82],"response":[84],"these":[86],"challenges,":[87],"we":[88,157],"introduce":[89],"DiffMATP,":[90],"an":[91],"innovative":[92],"method":[93],"for":[94],"interaction-aware":[95],"trajectory":[97,112,119,209],"that":[99,182],"leverages":[100],"cutting-edge":[101],"denoising":[102,127,198],"diffusion":[103,110,122,165,199],"models.":[104],"DiffMATP":[105,136,183],"integrates":[106],"notion":[108],"into":[111,164],"by":[114],"modeling":[115],"each":[116],"agent's":[117],"process.":[123],"It":[124],"employs":[125],"reverse":[126],"mitigate":[129],"uncertainty":[130],"during":[131],"phase.":[134],"Training":[135],"involves":[137],"variational":[138,148],"inference":[139],"on":[140,175],"parameterized":[142],"Markov":[143],"chain,":[144],"optimizing":[145],"reweighted":[147],"lower":[149],"bound":[150],"through":[151],"mean":[152],"square":[153],"error":[154],"minimization.":[155],"Additionally,":[156],"incorporate":[158],"temporal":[159],"spatial":[161],"attention":[162],"mechanisms":[163],"capture":[168],"dynamic":[169],"interactions":[170],"among":[171],"agents.":[172],"Experiments":[173],"conducted":[174],"pedestrian":[176],"highway":[178],"vehicle":[179],"datasets":[180],"demonstrate":[181],"outperforms":[184],"existing":[185],"methods":[186],"terms":[188],"accuracy":[190],"generalization,":[192],"which":[193],"underscores":[194],"addressing":[202],"intricate":[204],"challenges":[205],"associated":[206],"prediction.":[210]},"counts_by_year":[],"updated_date":"2026-05-20T06:11:20.791850","created_date":"2025-10-30T00:00:00"}
