{"id":"https://openalex.org/W4416737548","doi":"https://doi.org/10.3389/frai.2025.1690704","title":"ADP-Net: a hierarchical attention-diffusion-prediction framework for human trajectory prediction","display_name":"ADP-Net: a hierarchical attention-diffusion-prediction framework for human trajectory prediction","publication_year":2025,"publication_date":"2025-11-27","ids":{"openalex":"https://openalex.org/W4416737548","doi":"https://doi.org/10.3389/frai.2025.1690704","pmid":"https://pubmed.ncbi.nlm.nih.gov/41395372"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2025.1690704","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2025.1690704","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1690704/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1690704/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060714384","display_name":"Zhenggui Zhang","orcid":"https://orcid.org/0000-0003-4539-5731"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenggui Zhang","raw_affiliation_strings":["School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079537451","display_name":"Shanlin Xiao","orcid":"https://orcid.org/0000-0002-1250-8704"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shanlin Xiao","raw_affiliation_strings":["School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017560036","display_name":"Zhiyi Yu","orcid":"https://orcid.org/0000-0002-8802-0457"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyi Yu","raw_affiliation_strings":["School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics Science and Technology, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079537451"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38654195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1690704","last_page":"1690704"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.20489999651908875,"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":0.20489999651908875,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.11479999870061874,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.09870000183582306,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/graph","display_name":"Graph","score":0.580299973487854},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5235999822616577},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4505000114440918},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4390000104904175},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4323999881744385},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42080000042915344},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3959999978542328},{"id":"https://openalex.org/keywords/propagation-of-uncertainty","display_name":"Propagation of uncertainty","score":0.33399999141693115},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.32260000705718994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732699990272522},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.580299973487854},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5235999822616577},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5020999908447266},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4390000104904175},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4323999881744385},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42080000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39730000495910645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36239999532699585},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.3125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C43091099","wikidata":"https://www.wikidata.org/wiki/Q1067788","display_name":"Through-the-lens metering","level":3,"score":0.28450000286102295},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C194980680","wikidata":"https://www.wikidata.org/wiki/Q245450","display_name":"Circular convolution","level":5,"score":0.26499998569488525},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/frai.2025.1690704","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2025.1690704","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1690704/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},{"id":"pmid:41395372","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41395372","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in artificial intelligence","raw_type":null},{"id":"pmh:oai:doaj.org/article:5cdce8c479264994b338ec29519ac29a","is_oa":true,"landing_page_url":"https://doaj.org/article/5cdce8c479264994b338ec29519ac29a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Artificial Intelligence, Vol 8 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12696591","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12696591/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Artif Intell","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3389/frai.2025.1690704","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2025.1690704","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1690704/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1477544716","display_name":null,"funder_award_id":"Guangdong","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G691677441","display_name":null,"funder_award_id":"62334014","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416737548.pdf","grobid_xml":"https://content.openalex.org/works/W4416737548.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2424778531","https://openalex.org/W2519586580","https://openalex.org/W2532516272","https://openalex.org/W2962687116","https://openalex.org/W2963001155","https://openalex.org/W3035096461","https://openalex.org/W3175661831","https://openalex.org/W3192421672","https://openalex.org/W4287817646","https://openalex.org/W4288089072","https://openalex.org/W4297801949","https://openalex.org/W4312505697","https://openalex.org/W4312538728","https://openalex.org/W4379622683","https://openalex.org/W4380537908","https://openalex.org/W4385413695","https://openalex.org/W4390204040","https://openalex.org/W4390205049","https://openalex.org/W4396533633","https://openalex.org/W4404176664","https://openalex.org/W4406457627","https://openalex.org/W4407826349","https://openalex.org/W4410394780","https://openalex.org/W4410949756","https://openalex.org/W4411173126","https://openalex.org/W4413144366"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2,27,196],"human":[3],"crowd":[4,229],"behavior":[5,230],"presents":[6],"a":[7,22,221],"significant":[8],"challenge":[9],"with":[10,57,127,194,215],"critical":[11],"implications":[12],"for":[13,155,165,198,206,228],"autonomous":[14,233],"systems.":[15,234],"The":[16,150],"core":[17],"difficulty":[18],"lies":[19],"in":[20,232],"developing":[21],"comprehensive":[23],"computational":[24],"framework":[25,117,224],"capable":[26],"effectively":[28],"modeling":[29,171,231],"the":[30,97,199,207],"spatial-temporal":[31,45],"dynamics":[32],"through":[33],"three":[34,62,119],"essential":[35],"components:":[36],"feature":[37],"extraction,":[38],"attention":[39,111,129,133],"propagation,":[40],"and":[41,79,104,141,158,178,187,204,225],"predictive":[42],"modeling.":[43],"Current":[44],"graph":[46,101,124,137],"convolutional":[47,102],"networks":[48,103],"(STGCNs),":[49],"which":[50],"typically":[51],"employ":[52],"single-hop":[53],"neighborhood":[54],"message":[55],"passing":[56],"optional":[58],"self-attention":[59],"mechanisms,":[60],"exhibit":[61],"fundamental":[63],"limitations:":[64],"restricted":[65],"receptive":[66],"fields":[67],"due":[68],"to":[69,72,88],"being":[70],"confined":[71],"limited":[73],"propagation":[74,106,163],"steps,":[75],"poor":[76],"topological":[77],"extensibility,":[78],"structural":[80,174],"inconsistencies":[81],"between":[82,100],"network":[83,113],"components":[84],"that":[85],"collectively":[86],"lead":[87],"suboptimal":[89],"performance.":[90],"To":[91],"address":[92],"these":[93],"challenges,":[94],"we":[95],"establish":[96],"theoretical":[98,223],"connection":[99],"personalized":[105,162],"neural":[107],"architectures,":[108],"thereby":[109],"proposing":[110],"diffusion-prediction":[112],"(ADP-Net).":[114],"This":[115,218],"novel":[116],"integrates":[118],"key":[120],"innovations:":[121],"(1)":[122],"Consistent":[123],"convolution":[125,139,145],"layers":[126,135],"immediate":[128],"mechanisms;":[130],"(2)":[131],"Multi-scale":[132],"diffusion":[134,138],"implementing":[136],"(GDC);":[140],"(3)":[142],"Adaptive":[143],"temporal":[144,179],"modules":[146],"handling":[147],"multi-timescale":[148],"variations.":[149],"architecture":[151],"employs":[152],"polynomial":[153],"approximation":[154],"GCN":[156],"operations":[157],"implements":[159],"an":[160],"approximate":[161],"scheme":[164],"GDC,":[166],"enabling":[167],"efficient":[168],"multi-hop":[169],"interaction":[170],"while":[172],"maintaining":[173],"consistency":[175],"across":[176],"spatial":[177],"domains.":[180],"Comprehensive":[181],"experiments":[182],"on":[183],"standardized":[184],"benchmarks":[185],"(ETH/UCY":[186],"Stanford":[188],"Drone":[189],"Dataset)":[190],"show":[191],"cutting-edge":[192],"results,":[193],"enhancements":[195],"4%":[197],"average":[200],"displacement":[201,209],"error":[202,210],"(ADE)":[203],"26%":[205],"final":[208],"(FDE)":[211],"metrics":[212],"when":[213],"contrasted":[214],"prior":[216],"approaches.":[217],"advancement":[219],"provides":[220],"robust":[222],"practical":[226],"implementation":[227]},"counts_by_year":[],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-11-28T00:00:00"}