{"id":"https://openalex.org/W4409561172","doi":"https://doi.org/10.1109/tvt.2025.3561946","title":"The SGC-Informer-Based Deep Learning Framework: Managing the Efficiency-Accuracy Trade-Off in Vehicle Trajectory Prediction","display_name":"The SGC-Informer-Based Deep Learning Framework: Managing the Efficiency-Accuracy Trade-Off in Vehicle Trajectory Prediction","publication_year":2025,"publication_date":"2025-04-17","ids":{"openalex":"https://openalex.org/W4409561172","doi":"https://doi.org/10.1109/tvt.2025.3561946"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2025.3561946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3561946","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/A5101569756","display_name":"Rui He","orcid":"https://orcid.org/0000-0002-4288-5842"},"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":true,"raw_author_name":"Rui He","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040412645","display_name":"Zhiwei Meng","orcid":"https://orcid.org/0000-0002-4770-6698"},"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":"Zhiwei Meng","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010101374","display_name":"Sumin Zhang","orcid":"https://orcid.org/0000-0002-4860-0019"},"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":"Sumin Zhang","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072470185","display_name":"Shaohua Cui","orcid":"https://orcid.org/0000-0002-5885-3124"},"institutions":[{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohua Cui","raw_affiliation_strings":["Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beijing, China","institution_ids":["https://openalex.org/I4210127216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005447553","display_name":"Yupeng Chang","orcid":"https://orcid.org/0009-0007-7068-0864"},"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":"Yupeng Chang","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108674416","display_name":"Xiaosong Jin","orcid":null},"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":"Xiaosong Jin","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033009482","display_name":"Ri Bai","orcid":"https://orcid.org/0009-0003-1106-3031"},"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":"Ri Bai","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728476","display_name":"Tao Xu","orcid":"https://orcid.org/0000-0002-7855-4199"},"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":"Tao Xu","raw_affiliation_strings":["National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101569756"],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":null,"apc_paid":null,"fwci":2.0755,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.85150472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"74","issue":"9","first_page":"13492","last_page":"13507"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9480000138282776,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9480000138282776,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6784286499023438},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5301186442375183},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5169574618339539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43028390407562256},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3217696249485016}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6784286499023438},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5301186442375183},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5169574618339539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43028390407562256},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3217696249485016},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3561946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3561946","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2116341502","https://openalex.org/W2194775991","https://openalex.org/W2900880305","https://openalex.org/W2911794652","https://openalex.org/W2950635152","https://openalex.org/W2955189650","https://openalex.org/W2962687116","https://openalex.org/W2967177252","https://openalex.org/W2997958396","https://openalex.org/W3034722190","https://openalex.org/W3035054225","https://openalex.org/W3043572562","https://openalex.org/W3090166818","https://openalex.org/W3097237405","https://openalex.org/W3108486966","https://openalex.org/W3116651890","https://openalex.org/W3125605478","https://openalex.org/W3131807685","https://openalex.org/W3139491754","https://openalex.org/W3169575318","https://openalex.org/W3177318507","https://openalex.org/W3204875639","https://openalex.org/W3205464992","https://openalex.org/W3209837334","https://openalex.org/W3214950490","https://openalex.org/W4212975401","https://openalex.org/W4226239849","https://openalex.org/W4282003642","https://openalex.org/W4289812631","https://openalex.org/W4312221869","https://openalex.org/W4312731878","https://openalex.org/W4313590738","https://openalex.org/W4322588511","https://openalex.org/W4380632223","https://openalex.org/W4385245566","https://openalex.org/W4386432277","https://openalex.org/W4389538642","https://openalex.org/W4389891321","https://openalex.org/W4390342130","https://openalex.org/W4390492375","https://openalex.org/W4390576863","https://openalex.org/W4390871698","https://openalex.org/W4391309499","https://openalex.org/W4392505162","https://openalex.org/W4402916169","https://openalex.org/W4405386924"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"Accurate":[0],"and":[1,26,52,89,95,98,102,114,128,151,160,205,212,222,249],"efficient":[2],"trajectory":[3,33,130,254],"prediction":[4,82,255],"of":[5,14,62,66,70,76,109,112,183,192],"surrounding":[6],"vehicles":[7,67],"is":[8,35,141,200],"critical":[9],"for":[10,23,126,147],"ensuring":[11],"the":[12,63,71,77,107,110,121,148,171,180,189,193,197,207,210,215,219,236,242,245,252,259],"safety":[13],"autonomous":[15],"driving":[16],"systems,":[17],"as":[18,49],"it":[19],"provides":[20],"vital":[21],"input":[22],"subsequent":[24],"decision-making":[25],"planning":[27],"modules.":[28],"Relying":[29],"solely":[30],"on":[31,144,235],"historical":[32,64],"information":[34,91,162,194],"insufficient":[36],"to":[37,165,177,202],"accurately":[38],"predict,":[39],"especially":[40],"in":[41,209,251],"complex":[42,216],"traffic":[43],"scenarios":[44],"with":[45,258],"interwoven":[46],"roads":[47],"such":[48],"ramps,":[50],"intersections,":[51],"roundabouts.":[53],"Therefore,":[54],"predicting":[55],"future":[56],"trajectories":[57,65,150],"requires":[58],"consideration":[59],"not":[60],"only":[61],"but":[68],"also":[69],"interaction":[72,100],"among":[73],"various":[74],"entities":[75,208,224],"environmental":[78],"context.":[79],"However,":[80],"existing":[81,260],"methods":[83],"suffer":[84],"from":[85],"expensive":[86,157],"rendering":[87,158],"costs":[88,159],"entity":[90,138,161],"loss,":[92],"unnecessary":[93],"complexity":[94],"redundant":[96],"computation,":[97],"all-to-all":[99],"modeling":[101],"high":[103],"computational":[104],"costs,":[105],"facing":[106],"challenge":[108],"trade-off":[111,246],"efficiency":[113,250],"accuracy.":[115],"To":[116],"this":[117],"end,":[118],"we":[119,169],"propose":[120],"SGC-Informer-based":[122],"deep":[123],"learning":[124],"framework":[125],"fast":[127],"accurate":[129],"prediction,":[131],"called":[132],"FESIG.":[133],"Specifically,":[134],"a":[135],"comprehensive":[136],"explicit":[137],"vectorized":[139],"representation":[140],"constructed":[142],"based":[143],"Feature":[145],"Engineering":[146],"agent's":[149],"High-Definition":[152],"(HD)":[153],"map":[154],"information,":[155],"avoiding":[156],"loss.":[163],"Then,":[164],"mitigate":[166],"excess":[167],"complexity,":[168],"introduce":[170],"Simple":[172],"Graph":[173],"Convolution":[174],"(SGC)":[175],"network":[176],"effectively":[178],"extract":[179],"local":[181],"feature":[182],"each":[184],"entity,":[185],"which":[186],"can":[187],"remove":[188],"nonlinear":[190],"transformation":[191],"propagation.":[195],"Additionally,":[196],"Informer":[198],"mechanism":[199],"employed":[201],"automatically":[203],"sort":[204],"select":[206],"scenario,":[211],"then":[213],"capture":[214],"interactions":[217],"between":[218,247],"target":[220],"vehicle":[221,253],"these":[223],"that":[225,241],"significantly":[226],"impact":[227],"it,":[228],"thereby":[229],"reducing":[230],"computation":[231],"costs.":[232],"Experiments":[233],"performed":[234],"Argoverse":[237],"forecasting":[238],"dataset":[239],"illustrate":[240],"FESIG":[243],"achieves":[244],"accuracy":[248],"task":[256],"compared":[257],"methods.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
