{"id":"https://openalex.org/W3199132857","doi":"https://doi.org/10.1109/tvt.2021.3111227","title":"Environment-Attention Network for Vehicle Trajectory Prediction","display_name":"Environment-Attention Network for Vehicle Trajectory Prediction","publication_year":2021,"publication_date":"2021-09-09","ids":{"openalex":"https://openalex.org/W3199132857","doi":"https://doi.org/10.1109/tvt.2021.3111227","mag":"3199132857"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2021.3111227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2021.3111227","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/A5057276700","display_name":"Yingfeng Cai","orcid":"https://orcid.org/0000-0002-0633-9887"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingfeng Cai","raw_affiliation_strings":["Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China"],"affiliations":[{"raw_affiliation_string":"Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413086","display_name":"Zihao Wang","orcid":"https://orcid.org/0000-0002-4915-7653"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Wang","raw_affiliation_strings":["School of Automotive and Traffic Engineering of Jiangsu University, Zhenjiang, China"],"affiliations":[{"raw_affiliation_string":"School of Automotive and Traffic Engineering of Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100771674","display_name":"Hai Wang","orcid":"https://orcid.org/0000-0002-9136-8091"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai Wang","raw_affiliation_strings":["School of Automotive and Traffic Engineering of Jiangsu University, Zhenjiang, China"],"affiliations":[{"raw_affiliation_string":"School of Automotive and Traffic Engineering of Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630554","display_name":"Long Chen","orcid":"https://orcid.org/0000-0002-2079-3867"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China"],"affiliations":[{"raw_affiliation_string":"Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100657987","display_name":"Yicheng Li","orcid":"https://orcid.org/0000-0003-2937-7162"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yicheng Li","raw_affiliation_strings":["Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China"],"affiliations":[{"raw_affiliation_string":"Automotive Engineering Research Institute of Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002294104","display_name":"Miguel \u00c1ngel Sotelo","orcid":"https://orcid.org/0000-0001-8809-2103"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miguel Angel Sotelo","raw_affiliation_strings":["Department of Computer Engineering, University of Alcal, Alcal de Henares, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, University of Alcal, Alcal de Henares, Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100689482","display_name":"Zhixiong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Zhixiong Li","raw_affiliation_strings":["Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5057276700"],"corresponding_institution_ids":["https://openalex.org/I115592961"],"apc_list":null,"apc_paid":null,"fwci":5.3797,"has_fulltext":false,"cited_by_count":87,"citation_normalized_percentile":{"value":0.96355097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"70","issue":"11","first_page":"11216","last_page":"11227"},"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":0.9966999888420105,"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/T10370","display_name":"Traffic and Road Safety","score":0.9897000193595886,"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":0.6525439620018005},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6332094073295593},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.604997992515564},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5356293320655823},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5277966856956482},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.48539862036705017},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46724772453308105},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4304335117340088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4278530478477478},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41706743836402893},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4122334122657776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3615703582763672},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24157151579856873}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6525439620018005},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6332094073295593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.604997992515564},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5356293320655823},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5277966856956482},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.48539862036705017},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46724772453308105},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4304335117340088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4278530478477478},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41706743836402893},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4122334122657776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3615703582763672},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24157151579856873},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2021.3111227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2021.3111227","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":[{"score":0.44999998807907104,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1375727606","display_name":null,"funder_award_id":"51875255","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1763735783","display_name":null,"funder_award_id":"BK20180100","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G2297757108","display_name":null,"funder_award_id":"U1764264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3283955804","display_name":null,"funder_award_id":"U20A20333","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G353827121","display_name":null,"funder_award_id":"52072160","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3822039893","display_name":null,"funder_award_id":"BK20190853","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1505721114","https://openalex.org/W1522301498","https://openalex.org/W1919962963","https://openalex.org/W2019550475","https://openalex.org/W2055556996","https://openalex.org/W2079150870","https://openalex.org/W2105242877","https://openalex.org/W2194775991","https://openalex.org/W2343381408","https://openalex.org/W2507412229","https://openalex.org/W2537623947","https://openalex.org/W2567297805","https://openalex.org/W2580495915","https://openalex.org/W2582443945","https://openalex.org/W2752782242","https://openalex.org/W2803184913","https://openalex.org/W2896642734","https://openalex.org/W2897440557","https://openalex.org/W2917452308","https://openalex.org/W2918400102","https://openalex.org/W2921271497","https://openalex.org/W2940129212","https://openalex.org/W2953920792","https://openalex.org/W2963309363","https://openalex.org/W2963858333","https://openalex.org/W2963906196","https://openalex.org/W2963914175","https://openalex.org/W2964121744","https://openalex.org/W2965341826","https://openalex.org/W2991653934","https://openalex.org/W3053208739","https://openalex.org/W3105115779","https://openalex.org/W3129176582","https://openalex.org/W3132718927","https://openalex.org/W3135204376","https://openalex.org/W3164400104","https://openalex.org/W4297733535","https://openalex.org/W6631190155","https://openalex.org/W6745537798"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W2250140200","https://openalex.org/W2937120648"],"abstract_inverted_index":{"In":[0,73,104],"vehicle":[1,41,60,99,159],"trajectory":[2,38,197],"prediction,":[3],"the":[4,8,16,20,23,33,36,40,43,47,56,59,62,71,77,80,86,93,98,105,131,144,147,154,158,161,165,176,179,205,209,214,225,232,248],"difficulty":[5],"in":[6,14,22,46,70,79,138,213],"modeling":[7],"interaction":[9,17,34,57,155],"relationship":[10],"between":[11,19,35,58,97,157],"vehicles":[12,21,45],"lies":[13],"constructing":[15,153],"structure":[18,65,113,142,150,227],"traffic":[24,244],"scene.":[25],"Majority":[26],"of":[27,39,111,115,146,175,208,224],"existing":[28,81,249],"models":[29],"only":[30],"focus":[31],"on":[32,185],"historical":[37],"and":[42,50,61,100,120,136,149,160,170,190,192,239],"surrounding":[44,162],"spatial":[48],"domain,":[49],"do":[51],"not":[52],"pay":[53],"attention":[54],"to":[55,75,91,242],"non-Euclidean":[63],"correlation":[64],"(graph":[66],"structure)":[67],"that":[68,204,223,231],"exists":[69],"environment.":[72,103],"order":[74],"overcome":[76],"deficiencies":[78],"models,":[82],"this":[83],"paper":[84],"proposes":[85],"Environment-Attention":[87,211],"Network":[88,212],"model":[89,156,177,234],"(EA-Net)":[90],"obtain":[92],"full":[94],"interactive":[95],"information":[96,168],"its":[101],"driving":[102],"proposed":[106,210,233,235],"model,":[107,228],"a":[108],"new":[109],"type":[110],"parallel":[112],"consisting":[114],"Graph":[116],"Attention":[117],"network":[118],"(GAT)":[119],"Convolutional":[121],"social":[122],"pooling":[123],"containing":[124],"Squeeze-and-Extraction":[125],"mechanism":[126],"(SE-CS),":[127],"is":[128,183,217],"constructed":[129],"as":[130],"environmental":[132],"feature":[133,167],"extraction":[134],"module":[135],"embedded":[137],"LSTM":[139],"encoder-decoder.":[140],"This":[141],"solves":[143],"limitation":[145],"dimension":[148],"influence":[151],"when":[152],"environment,":[163],"making":[164],"extracted":[166],"comprehensive":[169],"effective.":[171],"The":[172,201],"prediction":[173,198,206],"accuracy":[174,207],"with":[178,194,247],"RMSE":[180],"loss":[181],"function":[182],"tested":[184],"two":[186,215],"public":[187],"datasets\u2013":[188],"NGSIM":[189],"highD,":[191],"compared":[193,246],"several":[195],"state-of-the-art":[196],"algorithm":[199],"models.":[200,250],"results":[202],"show":[203],"datasets":[216],"more":[218],"than":[219,222],"20%":[220],"higher":[221],"single":[226],"which":[229],"indicates":[230],"has":[236],"superior":[237],"performance":[238],"better":[240],"adaptability":[241],"different":[243],"environments":[245]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":11}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-10T00:00:00"}