{"id":"https://openalex.org/W7119095728","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310636","title":"An Improved LSTM Trajectory Prediction Method Based on Attention Mechanism in Vehicular Multi-modal Environments","display_name":"An Improved LSTM Trajectory Prediction Method Based on Attention Mechanism in Vehicular Multi-modal Environments","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7119095728","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310636"},"language":null,"primary_location":{"id":"doi:10.1109/vtc2025-fall65116.2025.11310636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","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/A5122172429","display_name":"Jiahong Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahong Zhu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089331349","display_name":"Tingting Liu","orcid":"https://orcid.org/0000-0003-3652-5979"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Liu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027733268","display_name":"Le Yu","orcid":"https://orcid.org/0000-0003-1457-6329"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Yu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121288955","display_name":"Deqiang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deqiang Li","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122095043","display_name":"Jun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Southeast University,School of Information Science and Engineering,Nanjing,China,210096"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University,School of Information Science and Engineering,Nanjing,China,210096","institution_ids":["https://openalex.org/I76569877"]}]}],"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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9796000123023987,"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":0.9796000123023987,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10036","display_name":"Advanced Neural Network Applications","score":0.00279999990016222,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7574999928474426},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5666999816894531},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5102999806404114},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4327000081539154},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.42570000886917114},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41119998693466187},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40619999170303345},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.37459999322891235}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7574999928474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483000159263611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.58160001039505},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5102999806404114},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4390999972820282},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C173386949","wikidata":"https://www.wikidata.org/wiki/Q192735","display_name":"Inertial frame of reference","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2025-fall65116.2025.11310636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.4848247170448303,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"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"},{"id":"https://openalex.org/F4320324852","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2103496339","https://openalex.org/W2117211893","https://openalex.org/W2145889067","https://openalex.org/W2335829695","https://openalex.org/W2806070179","https://openalex.org/W2944851425","https://openalex.org/W2956317147","https://openalex.org/W2965132998","https://openalex.org/W3140854437","https://openalex.org/W3146366485","https://openalex.org/W3164170369","https://openalex.org/W4400189345"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,29,52,89,124,129,133,152,155,162,183,190,200,227,236,251,258,284,294,302],"rapid":[2],"development":[3],"of":[4,13,23,54,154,202,212,238,253,304],"Vehicle-to-Everything":[5],"(V2X)":[6],"technology,":[7],"connected":[8],"vehicles":[9],"are":[10,109],"now":[11],"capable":[12],"acquiring":[14],"high-precision":[15,313],"environmental":[16],"perception":[17,85],"and":[18,45,103,114,142,178,209,245,267,279,296,319,335],"positioning":[19],"information.":[20],"Accurate":[21],"prediction":[22,57,148,315],"a":[24,59,194,210,309],"vehicle\u2019s":[25],"future":[26],"trajectory":[27,56,314],"within":[28,128],"network":[30,69],"is":[31,120],"fundamental":[32],"for":[33,312],"enabling":[34],"key":[35,324],"functions":[36],"such":[37,329],"as":[38,330],"autonomous":[39,327],"path":[40,331],"planning,":[41,332],"proactive":[42],"safety":[43],"control,":[44,334],"driver":[46],"behavior":[47,337],"modeling.":[48],"This":[49],"paper":[50],"addresses":[51],"problem":[53],"vehicle":[55,90,295],"in":[58,199,316,326],"V2X":[60,317],"environment":[61],"by":[62,88],"proposing":[63],"an":[64,73,117],"improved":[65,230,249],"Long":[66],"Short-Term":[67],"Memory":[68],"method":[70,82,192,260,287],"based":[71],"on":[72,138],"attention":[74,118,298],"mechanism,":[75],"named":[76],"Attention":[77,265],"Stack-LSTM":[78],"(AS-LSTM).":[79],"The":[80,167],"proposed":[81,285],"integrates":[83],"multi-modal":[84,290],"data":[86,292],"collected":[87],"itself,":[91],"including":[92,180],"Inertial":[93],"Measurement":[94],"Unit":[95],"(IMU),":[96],"Global":[97],"Positioning":[98],"System":[99],"(GPS),":[100],"multi-line":[101],"LiDAR,":[102],"onboard":[104],"cameras.":[105],"Initially,":[106],"time-series":[107],"features":[108,177],"extracted":[110],"from":[111,293],"each":[112],"modality":[113,144],"concatenated.":[115],"Then,":[116],"mechanism":[119],"introduced":[121],"to":[122,136,235,250,300,323],"model":[123],"dynamic":[125],"temporal":[126,305],"dependencies":[127],"sequence,":[130],"thereby":[131],"enhancing":[132],"model\u2019s":[134,228],"ability":[135],"focus":[137],"critical":[139],"time":[140],"steps":[141],"important":[143],"information,":[145],"ultimately":[146],"improving":[147],"accuracy.":[149],"To":[150],"validate":[151],"effectiveness":[153],"AS-LSTM":[156,191,259,286],"method,":[157],"experiments":[158],"were":[159],"conducted":[160],"using":[161],"publicly":[163],"available":[164],"NCLT":[165,185],"dataset.":[166],"evaluation":[168],"was":[169],"performed":[170],"under":[171],"two":[172],"scenarios:":[173],"one":[174,179],"excluding":[175],"image":[176,188,225],"them.":[181],"In":[182,256,282],"small-scale":[184],"subset":[186],"without":[187],"features,":[189,226],"achieved":[193],"Mean":[195],"Squared":[196],"Error":[197],"(MSE)":[198],"range":[201,237,252],"[5\u00d710<sup":[203,239],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[204,207,215,240,243,247],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u22128</sup>,":[205,241],"7\u00d710<sup":[206],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u22128</sup>]":[208,244],"coefficient":[211],"determination":[213],"(R<sup":[214],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>)":[216],"between":[217],"[-0.780,":[218],"-0.281],":[219],"demonstrating":[220],"stable":[221],"performance.":[222],"After":[223],"incorporating":[224],"performance":[229],"further,":[231],"with":[232],"MSE":[233],"reduced":[234],"6\u00d710<sup":[242],"R<sup":[246],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[248],"[-0.701,":[254],"-0.148].":[255],"addition,":[257],"consistently":[261],"outperformed":[262],"traditional":[263],"LSTM,":[264,266],"Seq2Seq":[268],"LSTM":[269],"models":[270],"across":[271],"different":[272],"learning":[273],"rate":[274],"settings,":[275],"exhibiting":[276],"superior":[277],"robustness":[278],"generalization":[280],"ability.":[281],"summary,":[283],"effectively":[288],"fuses":[289],"perceptual":[291],"leverages":[297],"mechanisms":[299],"enhance":[301],"modeling":[303],"dynamics.":[306],"It":[307],"offers":[308],"viable":[310],"solution":[311],"environments":[318],"can":[320],"be":[321],"applied":[322],"modules":[325],"vehicles,":[328],"motion":[333],"driving":[336],"prediction.":[338]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-08T00:00:00"}