{"id":"https://openalex.org/W4385834030","doi":"https://doi.org/10.1109/jiot.2023.3305395","title":"A Physical Law Constrained Deep Learning Model for Vehicle Trajectory Prediction","display_name":"A Physical Law Constrained Deep Learning Model for Vehicle Trajectory Prediction","publication_year":2023,"publication_date":"2023-08-15","ids":{"openalex":"https://openalex.org/W4385834030","doi":"https://doi.org/10.1109/jiot.2023.3305395"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2023.3305395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3305395","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5038861435","display_name":"Hanchu Li","orcid":"https://orcid.org/0000-0001-9580-0868"},"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":true,"raw_author_name":"Hanchu Li","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-9580-0868","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025786966","display_name":"Ziyi Liao","orcid":"https://orcid.org/0009-0006-6147-4850"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyi Liao","raw_affiliation_strings":["College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078950392","display_name":"Yikang Rui","orcid":"https://orcid.org/0000-0003-3750-2082"},"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":"Yikang Rui","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-3750-2082","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445638","display_name":"Linchao Li","orcid":"https://orcid.org/0000-0002-2574-0174"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"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":"Linchao Li","raw_affiliation_strings":["College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","Joint Research Institute on Internet of Mobility, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2574-0174","affiliations":[{"raw_affiliation_string":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"Joint Research Institute on Internet of Mobility, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060394098","display_name":"Bin Ran","orcid":"https://orcid.org/0000-0002-5464-0930"},"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":"Bin Ran","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5038861435"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":5.127,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95991892,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"10","issue":"24","first_page":"22775","last_page":"22790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9969000220298767,"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":0.9969000220298767,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.996399998664856,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9869999885559082,"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/trajectory","display_name":"Trajectory","score":0.8639171719551086},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8249590396881104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7100769281387329},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5337928533554077},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48031705617904663},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4706741273403168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4680972099304199},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4658675193786621},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3548307418823242}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8639171719551086},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8249590396881104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7100769281387329},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5337928533554077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48031705617904663},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4706741273403168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4680972099304199},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4658675193786621},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3548307418823242},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/jiot.2023.3305395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3305395","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3814048985","display_name":null,"funder_award_id":"52202402","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W32453388","https://openalex.org/W579199739","https://openalex.org/W1560949607","https://openalex.org/W1598796236","https://openalex.org/W1965455100","https://openalex.org/W1979690402","https://openalex.org/W1992996956","https://openalex.org/W2008039411","https://openalex.org/W2011931151","https://openalex.org/W2056877664","https://openalex.org/W2064675550","https://openalex.org/W2089080831","https://openalex.org/W2124298315","https://openalex.org/W2126311411","https://openalex.org/W2154376416","https://openalex.org/W2157331557","https://openalex.org/W2163590332","https://openalex.org/W2167052694","https://openalex.org/W2281287867","https://openalex.org/W2755552418","https://openalex.org/W2756385104","https://openalex.org/W2791175987","https://openalex.org/W2896642734","https://openalex.org/W2940129212","https://openalex.org/W2963906196","https://openalex.org/W2963914175","https://openalex.org/W2967078791","https://openalex.org/W2990116160","https://openalex.org/W3024761859","https://openalex.org/W3034721499","https://openalex.org/W3044934783","https://openalex.org/W3128486562","https://openalex.org/W3132175731","https://openalex.org/W3205464992","https://openalex.org/W4226051392","https://openalex.org/W4226239849","https://openalex.org/W4306814723","https://openalex.org/W6601322911","https://openalex.org/W6610572085","https://openalex.org/W6616647006","https://openalex.org/W6633540119","https://openalex.org/W6635679246","https://openalex.org/W6638545294","https://openalex.org/W6779104384","https://openalex.org/W6846331420"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4247136043","https://openalex.org/W4312407344","https://openalex.org/W4384115502","https://openalex.org/W4226258012","https://openalex.org/W2066625485"],"abstract_inverted_index":{"Vehicle":[0],"trajectory":[1,43,77,126,237],"prediction":[2,46,194,219,238],"is":[3,101,153],"crucial":[4],"and":[5,11,66,94,110,117,132,147,155,178,181,202,209,239],"indispensable":[6],"for":[7,41,244],"ensuring":[8],"the":[9,29,62,73,98,104,119,130,142,148,158,168,197,205,212,218,223,226,245],"safe":[10],"efficient":[12],"operation":[13],"of":[14,23,25,53,61,75,135,214,225,248],"autonomous":[15,249],"vehicles":[16,136,146],"in":[17,28,175,192,217,235],"complex":[18],"traffic":[19],"environments.":[20],"The":[21,151,229],"application":[22],"Internet":[24],"Things":[26],"technology":[27],"collaborative":[30],"automated":[31],"driving":[32,64,131,250],"system":[33],"(CADS)":[34],"has":[35,232],"established":[36],"a":[37,50,58,82],"robust":[38],"data":[39,55,162],"foundation":[40],"vehicle":[42,76,124,236],"prediction.":[44],"Accurate":[45],"requires":[47],"not":[48],"only":[49],"substantial":[51],"amount":[52],"high-quality":[54],"but":[56],"also":[57],"deep":[59],"understanding":[60],"vehicle\u2019s":[63],"characteristics":[65],"interactions":[67,143,206],"between":[68,144,207],"neighboring":[69,145],"vehicles.":[70,251],"To":[71],"enhance":[72,116],"study":[74],"prediction,":[78],"this":[79],"article":[80],"proposes":[81],"novel":[83],"Social":[84],"Force-constrained":[85],"Gated":[86],"Recurrent":[87],"Unit":[88],"(SF-GRU)":[89],"model,":[90],"which":[91,128],"integrates":[92],"data-driven":[93,227],"physics-driven":[95],"models.":[96],"Specifically,":[97],"SF-GRU":[99,169,198,230],"model":[100,120,152,170,199,231],"based":[102,122],"on":[103,123],"gated":[105],"recurrent":[106],"unit":[107],"encoder\u2013decoder":[108],"framework":[109],"incorporates":[111],"social":[112,183],"force":[113,184],"constraints":[114,185],"to":[115],"supplement":[118],"input":[121],"time-series":[125],"data,":[127],"describes":[129],"interactive":[133],"behaviors":[134],"during":[137],"driving,":[138],"as":[139,141],"well":[140],"surrounding":[149],"environment.":[150],"trained":[154],"validated":[156],"using":[157],"next":[159],"generation":[160],"simulation":[161],"set.":[163],"Experimental":[164],"results":[165],"demonstrate":[166],"that":[167,182],"outperforms":[171],"existing":[172],"state-of-the-art":[173],"models":[174],"both":[176],"longitudinal":[177],"lateral":[179],"motion,":[180],"are":[186],"more":[187],"effective":[188],"than":[189],"spatial":[190],"variables":[191,216],"improving":[193],"accuracy.":[195],"Furthermore,":[196],"can":[200,240],"intuitively":[201],"accurately":[203],"consider":[204],"vehicles,":[208],"precisely":[210],"describe":[211],"changes":[213],"relevant":[215],"process,":[220],"thus":[221],"enhancing":[222],"interpretability":[224],"model.":[228],"great":[233],"potential":[234],"provide":[241],"important":[242],"support":[243],"practical":[246],"implementation":[247]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}