{"id":"https://openalex.org/W4411799442","doi":"https://doi.org/10.1109/tvt.2025.3584225","title":"Efficient Multiscale Hybrid Cross-Learning Deep Neural Networks With Spatiotemporal Fusion for Vehicle Behavior Prediction","display_name":"Efficient Multiscale Hybrid Cross-Learning Deep Neural Networks With Spatiotemporal Fusion for Vehicle Behavior Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4411799442","doi":"https://doi.org/10.1109/tvt.2025.3584225"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2025.3584225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3584225","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":null,"display_name":"Xingyu Wang","orcid":"https://orcid.org/0000-0002-7866-3483"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyu Wang","raw_affiliation_strings":["School of Automotive Studies, Tongji University, Shanghai, China","School of Automotive Studies, Tongji university, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7866-3483","affiliations":[{"raw_affiliation_string":"School of Automotive Studies, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"School of Automotive Studies, Tongji university, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081419965","display_name":"Guangqiang Wu","orcid":"https://orcid.org/0000-0001-8013-3572"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangqiang Wu","raw_affiliation_strings":["School of Automotive Studies, Tongji University, Shanghai, China","School of Automotive Studies, Tongji university, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-8013-3572","affiliations":[{"raw_affiliation_string":"School of Automotive Studies, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"School of Automotive Studies, Tongji university, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qirui Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qirui Luo","raw_affiliation_strings":["School of Automotive Studies, Tongji University, Shanghai, China","School of Automotive Studies, Tongji university, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automotive Studies, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"School of Automotive Studies, Tongji university, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115601011","display_name":"Kai Liu","orcid":"https://orcid.org/0009-0001-2513-5652"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Liu","raw_affiliation_strings":["School of Automotive Studies, Tongji University, Shanghai, China","School of Automotive Studies, Tongji university, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-2513-5652","affiliations":[{"raw_affiliation_string":"School of Automotive Studies, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"School of Automotive Studies, Tongji university, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13357373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":"12","first_page":"18396","last_page":"18406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9257000088691711,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9257000088691711,"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/T12759","display_name":"Vehicle Noise and Vibration Control","score":0.9160000085830688,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6443844437599182},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.586894690990448},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5467965006828308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5443666577339172},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4818597733974457},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.42720210552215576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3496400713920593}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6443844437599182},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.586894690990448},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5467965006828308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5443666577339172},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4818597733974457},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.42720210552215576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3496400713920593},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3584225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3584225","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":35,"referenced_works":["https://openalex.org/W1652644688","https://openalex.org/W2092272787","https://openalex.org/W2752782242","https://openalex.org/W2792239816","https://openalex.org/W2897326341","https://openalex.org/W2919115771","https://openalex.org/W2969574430","https://openalex.org/W2975767248","https://openalex.org/W2997958396","https://openalex.org/W3016826426","https://openalex.org/W3019688365","https://openalex.org/W3034552520","https://openalex.org/W3093176731","https://openalex.org/W3117379804","https://openalex.org/W3147532979","https://openalex.org/W3163257372","https://openalex.org/W3197998683","https://openalex.org/W3201859961","https://openalex.org/W3215493153","https://openalex.org/W4285157114","https://openalex.org/W4289752563","https://openalex.org/W4293812175","https://openalex.org/W4295934546","https://openalex.org/W4313476485","https://openalex.org/W4323914173","https://openalex.org/W4372347372","https://openalex.org/W4379380529","https://openalex.org/W4380925271","https://openalex.org/W4385453084","https://openalex.org/W4386570969","https://openalex.org/W4388300135","https://openalex.org/W4391128321","https://openalex.org/W4393375094","https://openalex.org/W4394834542","https://openalex.org/W4399727969"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W4380075502","https://openalex.org/W3214791684","https://openalex.org/W2152662039"],"abstract_inverted_index":{"Recently,":[0],"deep":[1,53],"neural":[2,119],"network-based":[3],"methods":[4,15,170],"have":[5],"been":[6],"proposed":[7,179],"for":[8,155],"vehicle":[9,156],"behavior":[10,157],"prediction":[11,158],"(VBP).":[12],"However,":[13],"existing":[14],"lack":[16],"an":[17],"enhanced":[18],"discriminative":[19],"mechanism":[20],"and":[21,41,83,131,144],"robust":[22],"learning":[23,62],"ability":[24],"to":[25,96,108,128,140,173],"effectively":[26],"extract":[27],"feature":[28,78,99],"information":[29,37,100],"from":[30],"complex":[31],"data.":[32],"This":[33,66,137],"limitation":[34],"results":[35],"in":[36,94],"loss,":[38],"low":[39],"accuracy,":[40],"reduced":[42],"training":[43],"efficiency":[44],"of":[45,92,134,177],"the":[46,86,146,163,175,178],"model.":[47,180],"To":[48],"address":[49,145],"these":[50],"challenges,":[51],"a":[52,114,151],"convolutional":[54],"network":[55,120],"based":[56],"on":[57],"efficient":[58],"multiscale":[59],"hybrid":[60],"cross":[61],"(EMHC)":[63],"is":[64,126,159,171],"introduced.":[65],"method":[67],"involves":[68],"two":[69,74],"modules:":[70],"one":[71],"module":[72,88],"aggregates":[73],"independent":[75],"spatial":[76],"perception":[77],"maps":[79],"along":[80],"different":[81,90],"directions":[82],"scales,":[84],"while":[85],"other":[87],"divides":[89],"numbers":[91],"channels":[93],"parallel":[95],"achieve":[97],"cross-channel":[98],"fusion.":[101],"Additionally,":[102],"local":[103],"cross-learning":[104],"mechanisms":[105],"are":[106],"incorporated":[107],"further":[109],"enhance":[110],"effective":[111],"features.":[112],"Furthermore,":[113],"long":[115],"short-term":[116],"memory":[117],"(LSTM)":[118],"combined":[121],"with":[122,168],"multihead":[123],"self-attention":[124],"(MHSA)":[125],"developed":[127],"establish":[129],"short":[130],"long-range":[132],"dependencies":[133],"temporal":[135],"information.":[136],"integration":[138],"aims":[139],"accelerate":[141],"convergence":[142],"speed":[143],"vanishing":[147],"gradient":[148],"problem.":[149],"Finally,":[150],"spatiotemporal":[152],"fusion":[153],"model":[154],"constructed":[160],"by":[161],"combining":[162],"aforementioned":[164],"networks.":[165],"Comparative":[166],"analysis":[167],"state-of-the-art":[169],"conducted":[172],"showcase":[174],"superiority":[176]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}