{"id":"https://openalex.org/W4401414063","doi":"https://doi.org/10.1109/icra57147.2024.10611535","title":"HHGNN: Heterogeneous Hypergraph Neural Network for Traffic Agents Trajectory Prediction in Grouping Scenarios","display_name":"HHGNN: Heterogeneous Hypergraph Neural Network for Traffic Agents Trajectory Prediction in Grouping Scenarios","publication_year":2024,"publication_date":"2024-05-13","ids":{"openalex":"https://openalex.org/W4401414063","doi":"https://doi.org/10.1109/icra57147.2024.10611535"},"language":"en","primary_location":{"id":"doi:10.1109/icra57147.2024.10611535","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","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/A5113325511","display_name":"Hetian Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hetian Guo","raw_affiliation_strings":["Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014425638","display_name":"Yingzhi Peng","orcid":"https://orcid.org/0009-0009-5755-0429"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingzhi Peng","raw_affiliation_strings":["Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009062546","display_name":"Zipei Fan","orcid":"https://orcid.org/0000-0002-1442-1530"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zipei Fan","raw_affiliation_strings":["Jilin University,School of Artificial Intelligence,Changchun,P.R. China,130012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,School of Artificial Intelligence,Changchun,P.R. China,130012","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085969322","display_name":"He Zhu","orcid":"https://orcid.org/0000-0003-2481-3447"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Zhu","raw_affiliation_strings":["Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108885439","display_name":"Xuan Song","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Song","raw_affiliation_strings":["Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China,518055","institution_ids":["https://openalex.org/I3045169105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"14101","last_page":"14108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9995999932289124,"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":0.9995999932289124,"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":0.995199978351593,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.798157811164856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7640118598937988},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6962542533874512},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5916246175765991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4653495252132416},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43373435735702515},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.33803316950798035},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3285357356071472},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09463557600975037}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.798157811164856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7640118598937988},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6962542533874512},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5916246175765991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4653495252132416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43373435735702515},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.33803316950798035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3285357356071472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09463557600975037},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra57147.2024.10611535","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10611535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2424778531","https://openalex.org/W2560609797","https://openalex.org/W2892614179","https://openalex.org/W2955189650","https://openalex.org/W2963001155","https://openalex.org/W2985871763","https://openalex.org/W3008700642","https://openalex.org/W3034722190","https://openalex.org/W3035054225","https://openalex.org/W3035574168","https://openalex.org/W3097388010","https://openalex.org/W3108486966","https://openalex.org/W3116651890","https://openalex.org/W3125605478","https://openalex.org/W3156216502","https://openalex.org/W3169575318","https://openalex.org/W3170929336","https://openalex.org/W3177765762","https://openalex.org/W3196864007","https://openalex.org/W3202586194","https://openalex.org/W3203006492","https://openalex.org/W3204875639","https://openalex.org/W3209837334","https://openalex.org/W3214950490","https://openalex.org/W4205377509","https://openalex.org/W4210457203","https://openalex.org/W4224266469","https://openalex.org/W4281965085","https://openalex.org/W4296473294","https://openalex.org/W4297944254","https://openalex.org/W4306317336","https://openalex.org/W4306317428","https://openalex.org/W4312339440","https://openalex.org/W4313041951","https://openalex.org/W4383108225","https://openalex.org/W4383109074","https://openalex.org/W4385245566","https://openalex.org/W4388452210","https://openalex.org/W6769043036","https://openalex.org/W6782468546","https://openalex.org/W6796539847","https://openalex.org/W6801880476","https://openalex.org/W6839142250","https://openalex.org/W6843475265"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2963081352","https://openalex.org/W4376608938","https://openalex.org/W2472555608","https://openalex.org/W4214498971"],"abstract_inverted_index":{"In":[0,69],"many":[1],"intelligent":[2],"transportation":[3],"systems,":[4],"predicting":[5],"the":[6,24,94,100,109,123,150,158,168],"future":[7],"motion":[8,89,169],"of":[9,60,73,96],"heterogeneous":[10,130],"traffic":[11,34,104,131],"participants":[12],"is":[13],"a":[14,81,141],"fundamental":[15],"but":[16],"challenging":[17],"task":[18],"due":[19],"to":[20,65,98,121,148],"various":[21],"factors":[22],"encompassing":[23],"agents\u2019":[25,67],"dynamic":[26],"states,":[27],"interactions":[28,59,102,128],"with":[29,117],"neighboring":[30],"agents":[31,132],"and":[32,36,39,125,136,154],"surrounding":[33],"infrastructures,":[35],"their":[37],"stochastic":[38],"multi-modal":[40],"natural":[41],"behavior":[42],"tendencies.":[43],"However,":[44],"existing":[45],"approaches":[46],"have":[47],"limitations":[48],"as":[49],"they":[50],"either":[51],"focus":[52],"solely":[53,75],"on":[54,76,157],"static,":[55],"pairwise":[56,77],"interactions,":[57,78],"ignoring":[58],"varied":[61],"granularity,":[62],"or":[63],"fail":[64],"tackle":[66],"heterogeneity.":[68],"this":[70],"paper,":[71],"instead":[72],"focusing":[74],"we":[79,107],"propose":[80,108],"Heterogenous":[82],"Hypergraph":[83],"Graph":[84],"Neural":[85],"Network":[86],"(HHGNN)":[87],"based":[88],"prediction":[90,170],"model":[91,122],"that":[92],"leverages":[93],"nature":[95],"hypergraph":[97,112],"encode":[99],"groupwise":[101],"among":[103,129],"participants.":[105],"Moreover,":[106],"type-aware":[110],"two-level":[111],"message":[113],"passing":[114],"module":[115],"(TTHMS)":[116],"learnable":[118],"hyperedge-type":[119],"embeddings":[120],"intra-group":[124],"inter-group":[126],"level":[127],"(e.g.,":[133],"vehicles,":[134],"pedestrians,":[135],"cyclists).":[137],"Besides,":[138],"We":[139],"integrate":[140],"scene":[142,151],"context":[143],"fusion":[144],"layer":[145],"in":[146],"TTHMS":[147],"incorporate":[149],"context.":[152],"Comparison":[153],"ablation":[155],"experiments":[156],"Waymo":[159],"Open":[160],"Motion":[161],"Dataset":[162],"(WOMD)":[163],"demonstrate":[164],"HHGNN\u2019s":[165],"effectiveness":[166],"within":[167],"task.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}