{"id":"https://openalex.org/W4392931186","doi":"https://doi.org/10.1109/icassp48485.2024.10446145","title":"Motion Latent Diffusion for Stochastic Trajectory Prediction","display_name":"Motion Latent Diffusion for Stochastic Trajectory Prediction","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392931186","doi":"https://doi.org/10.1109/icassp48485.2024.10446145"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10446145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5104176856","display_name":"Weishang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weishang Wu","raw_affiliation_strings":["Central South University,School of Computer Science and Engineering,Changsha,China","School of Computer Science and Engineering, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,School of Computer Science and Engineering,Changsha,China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086816845","display_name":"Xiaoheng Deng","orcid":"https://orcid.org/0000-0003-2740-8025"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoheng Deng","raw_affiliation_strings":["Central South University,School of Electronic Information,Changsha,China","School of Electronic Information, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,School of Electronic Information,Changsha,China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Electronic Information, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6665","last_page":"6669"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9980000257492065,"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/trajectory","display_name":"Trajectory","score":0.7944527268409729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6763295531272888},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5947595238685608},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5917497277259827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5657232999801636},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.484251469373703},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4608156085014343},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4370030462741852},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1445103883743286}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7944527268409729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6763295531272888},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5947595238685608},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5917497277259827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5657232999801636},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.484251469373703},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4608156085014343},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4370030462741852},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1445103883743286},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10446145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2030481789","display_name":null,"funder_award_id":"62172449","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2460975407","display_name":null,"funder_award_id":"62172441","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4744389466","display_name":null,"funder_award_id":"U2368201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321514","display_name":"Central South University","ror":"https://ror.org/00f1zfq44"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2124111503","https://openalex.org/W2167052694","https://openalex.org/W2963001155","https://openalex.org/W3006115429","https://openalex.org/W3034589393","https://openalex.org/W3097237405","https://openalex.org/W3108908812","https://openalex.org/W3116651890","https://openalex.org/W3121370741","https://openalex.org/W3139425555","https://openalex.org/W3139491754","https://openalex.org/W3175661831","https://openalex.org/W3201917469","https://openalex.org/W4212774754","https://openalex.org/W4312305613","https://openalex.org/W4312580307","https://openalex.org/W4312750092","https://openalex.org/W4313041951","https://openalex.org/W4321593267","https://openalex.org/W4372263438","https://openalex.org/W4386071549","https://openalex.org/W4387245341","https://openalex.org/W6779823529","https://openalex.org/W6783713337","https://openalex.org/W6849840034"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2101105382"],"abstract_inverted_index":{"The":[0,82],"indeterminacy":[1],"of":[2,32,84,119,148],"human":[3],"motion":[4],"poses":[5],"challenges":[6],"for":[7,40,48],"pedestrian":[8],"trajectory":[9,41,68],"prediction.":[10],"Consequently,":[11],"existing":[12],"methods":[13],"adopt":[14,124],"multimodal":[15],"strategy":[16],"to":[17,92,107],"model":[18,55],"pedestrians":[19],"future":[20],"trajectories.":[21],"A":[22],"significant":[23],"advancement":[24],"in":[25],"this":[26],"regard":[27],"is":[28,86],"the":[29,33,37,49,53,73,87,94,104,116,120,138,146],"growing":[30],"prominence":[31],"diffusion":[34,50,54,74,121],"model.":[35,51],"However,":[36],"two-dimensional":[38],"inputs":[39,97],"prediction":[42,69],"not":[43],"provide":[44],"sufficient":[45],"contextual":[46],"information":[47],"Furthermore,":[52],"suffers":[56],"from":[57],"substantial":[58],"inference":[59,127],"time.":[60],"To":[61],"address":[62],"these":[63],"conundrums,":[64],"we":[65,123],"propose":[66],"a":[67,99,125,131],"method":[70],"based":[71],"on":[72,137],"model,":[75,122],"named":[76],"as":[77],"Motion":[78],"Latent":[79],"Diffusion":[80],"(MLD).":[81],"core":[83],"MLD":[85],"Conditional":[88],"Variational":[89],"Autoencoder":[90],"(CVAE)":[91],"transform":[93],"original":[95],"low-dimensional":[96],"into":[98],"higher-dimensional":[100],"latent":[101],"space,":[102],"expanding":[103],"receptive":[105],"field":[106],"yield":[108],"more":[109],"comprehensive":[110],"and":[111,140],"intricate":[112],"representations.":[113],"Simultaneously,":[114],"during":[115],"inferential":[117],"stage":[118],"leapfrogging":[126],"strategy,":[128],"which":[129],"facilitates":[130],"faster":[132],"sampling":[133],"process.":[134],"Experiments":[135],"conducted":[136],"ETH/UCY":[139],"Stanford":[141],"Drone":[142],"datasets":[143],"(SDD)":[144],"corroborate":[145],"superiority":[147],"our":[149],"method.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
