{"id":"https://openalex.org/W7084040714","doi":"https://doi.org/10.11896/jsjkx.231100145","title":"Vehicle Trajectory Prediction Based on Spatial-Temporal Graph Attention Convolutional Network","display_name":"Vehicle Trajectory Prediction Based on Spatial-Temporal Graph Attention Convolutional Network","publication_year":2024,"publication_date":"2024-12-01","ids":{"openalex":"https://openalex.org/W7084040714","doi":"https://doi.org/10.11896/jsjkx.231100145"},"language":"en","primary_location":{"id":"pmh:oai:doaj.org/article:1409c7a576bb40ce94604ffe1a0e6f4a","doi":"https://doi.org/10.11896/jsjkx.231100145","is_oa":true,"landing_page_url":"https://doaj.org/article/1409c7a576bb40ce94604ffe1a0e6f4a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jisuanji kexue, Vol 51, Iss 12, Pp 157-165 (2024)","raw_type":"article"},"volume":null,"type":"article","indexed_in":["doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doaj.org/article/1409c7a576bb40ce94604ffe1a0e6f4a","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"YUAN Jing, XIA Ying","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"YUAN Jing, XIA Ying","raw_affiliation_strings":["College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29685798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.6929000020027161,"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":0.6929000020027161,"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":0.27880001068115234,"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/T11106","display_name":"Data Management and Algorithms","score":0.012799999676644802,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/vehicle-trajectory-prediction","display_name":"vehicle trajectory prediction","score":1.0},{"id":"https://openalex.org/keywords/spatiotemporal-feature-fusion","display_name":"spatiotemporal feature fusion","score":0.7390000224113464},{"id":"https://openalex.org/keywords/gated-recurrent-unit","display_name":"gated recurrent unit","score":0.8069999814033508},{"id":"https://openalex.org/keywords/dilated-causal-convolution","display_name":"dilated causal convolution","score":0.9610000252723694},{"id":"https://openalex.org/keywords/trajectory-embedding","display_name":"trajectory embedding","score":0.6710000038146973},{"id":"https://openalex.org/keywords/urban-traffic","display_name":"urban traffic","score":0.8330000042915344},{"id":"https://openalex.org/keywords/autonomous-driving","display_name":"autonomous driving","score":0.8309999704360962},{"id":"https://openalex.org/keywords/argoverse","display_name":"Argoverse","score":0.8309999704360962},{"id":"https://openalex.org/keywords/ngsim","display_name":"NGSIM","score":1.0}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8449000120162964},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6639999747276306},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6223000288009644},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5386000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4846999943256378},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.349700003862381},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27630001306533813},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:doaj.org/article:1409c7a576bb40ce94604ffe1a0e6f4a","doi":"https://doi.org/10.11896/jsjkx.231100145","is_oa":true,"landing_page_url":"https://doaj.org/article/1409c7a576bb40ce94604ffe1a0e6f4a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jisuanji kexue, Vol 51, Iss 12, Pp 157-165 (2024)","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:doaj.org/article:1409c7a576bb40ce94604ffe1a0e6f4a","doi":"https://doi.org/10.11896/jsjkx.231100145","is_oa":true,"landing_page_url":"https://doaj.org/article/1409c7a576bb40ce94604ffe1a0e6f4a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jisuanji kexue, Vol 51, Iss 12, Pp 157-165 (2024)","raw_type":"article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7250000238418579},{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5378999710083008}],"sustainable_development_goals_aurora":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8215091824531555}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vehicle":[0],"trajectory":[1,30,45,63,71,84,157,168],"prediction":[2,46,158,162,183],"is":[3],"a":[4,54,62,106,152],"crucial":[5],"technology":[6],"in":[7,42,135,151],"fields":[8],"such":[9],"as":[10],"traffic":[11,23],"management,intelligent-car,and":[12],"autonomous":[13],"driving.Accurately":[14],"predicting":[15],"vehicle":[16,29,70],"trajectories":[17],"contributes":[18],"to":[19,67,99,118,144],"safe":[20],"driving.In":[21],"urban":[22],"scenarios,the":[24],"spatial-":[25,55,132],"temporal":[26,56,79,120,133],"features":[27,82,134],"of":[28,83,111,131],"data":[31,72,85],"are":[32,93,164],"complex":[33],"and":[34,77,80,91,115,171,185],"variable.To":[35],"fully":[36],"capture":[37],"the":[38,43,101,122,136,142,173,177,188],"dynamic":[39],"spatial-temporal":[40,74,88,137],"correlations":[41],"data,enhance":[44],"accuracy,and":[47],"simultaneously":[48],"reduce":[49],"model":[50,104,143,153,180],"complexity,this":[51],"paper":[52],"proposes":[53],"graph":[57],"attention":[58],"convolutional":[59,108],"network(STGACN).It":[60],"utilizes":[61],"information":[64],"embedding":[65],"module":[66],"transform":[68],"historical":[69],"into":[73],"graphs.Subsequently,it":[75],"extracts":[76],"combines":[78],"spatial":[81],"through":[86],"stacked":[87],"convolution":[89,138],"blocks.Finally,encoding":[90],"decoding":[92],"performed":[94],"by":[95,126],"gated":[96,107],"recurrent":[97,127],"units":[98,117],"obtain":[100],"predicted":[102],"trajectory.The":[103],"employs":[105],"network":[109],"composed":[110],"dilated":[112],"causal":[113],"convolutions":[114],"gating":[116],"extract":[119],"features,avoiding":[121],"redundant":[123],"iterations":[124],"introduced":[125],"neural":[128],"network.The":[129],"fusion":[130],"blocks":[139],"group":[140],"enables":[141],"focus":[145],"on":[146,166],"richer":[147],"scene":[148],"features.This":[149],"results":[150,174],"with":[154],"fewer":[155],"parameters,faster":[156],"inference":[159],"speed,and":[160],"improved":[161],"accuracy.Experiments":[163],"conducted":[165],"real":[167],"datasets,including":[169],"Argoverse":[170],"NGSIM,and":[172],"demonstrate":[175],"that":[176],"proposed":[178],"STGACN":[179],"exhibits":[181],"higher":[182],"accuracy":[184],"efficiency":[186],"than":[187],"compared":[189],"baseline":[190],"models.":[191]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-10T00:00:00"}
