{"id":"https://openalex.org/W4391567551","doi":"https://doi.org/10.1080/15472450.2023.2301691","title":"Generative adversarial network for car following trajectory generation and anomaly detection","display_name":"Generative adversarial network for car following trajectory generation and anomaly detection","publication_year":2024,"publication_date":"2024-02-06","ids":{"openalex":"https://openalex.org/W4391567551","doi":"https://doi.org/10.1080/15472450.2023.2301691"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2023.2301691","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2023.2301691","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","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/A5101898517","display_name":"Haotian Shi","orcid":"https://orcid.org/0000-0001-6961-1115"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haotian Shi","raw_affiliation_strings":["University of Wisconsin-Madison","University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068839040","display_name":"Shuoxuan Dong","orcid":"https://orcid.org/0000-0002-3069-7414"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuoxuan Dong","raw_affiliation_strings":["University of Wisconsin-Madison","University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370856","display_name":"Yuankai Wu","orcid":"https://orcid.org/0000-0003-4435-9413"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuankai Wu","raw_affiliation_strings":["Sichuan University","Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001542420","display_name":"Qinghui Nie","orcid":"https://orcid.org/0000-0002-1498-1423"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghui Nie","raw_affiliation_strings":["Yangzhou University","Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yangzhou University","institution_ids":["https://openalex.org/I78978612"]},{"raw_affiliation_string":"Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025735445","display_name":"Yang Zhou","orcid":"https://orcid.org/0000-0001-5366-5389"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]},{"id":"https://openalex.org/I2801613365","display_name":"Mitchell Institute","ror":"https://ror.org/03ds72003","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2801613365"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Zhou","raw_affiliation_strings":["Texas A&M University","Texas A&amp;M University, College Station, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&M University","institution_ids":["https://openalex.org/I2801613365","https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&amp;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"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/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Ran","raw_affiliation_strings":["University of Wisconsin-Madison","University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5025735445"],"corresponding_institution_ids":["https://openalex.org/I2801613365","https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":4.7668,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.95319382,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"29","issue":"1","first_page":"53","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/adversarial-system","display_name":"Adversarial system","score":0.793835461139679},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.726348876953125},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6562373638153076},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5856497287750244},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5153172016143799},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5084065794944763},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46489351987838745},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4470679759979248},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.33393433690071106},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1758701205253601},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0739457905292511}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.793835461139679},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.726348876953125},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6562373638153076},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5856497287750244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5153172016143799},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5084065794944763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46489351987838745},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4470679759979248},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.33393433690071106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1758701205253601},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0739457905292511},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2023.2301691","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2023.2301691","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1965455100","https://openalex.org/W1966809779","https://openalex.org/W1978092693","https://openalex.org/W1985403019","https://openalex.org/W1988981069","https://openalex.org/W2007971313","https://openalex.org/W2016151598","https://openalex.org/W2064675550","https://openalex.org/W2122646361","https://openalex.org/W2128853419","https://openalex.org/W2143545157","https://openalex.org/W2152374007","https://openalex.org/W2157331557","https://openalex.org/W2164700406","https://openalex.org/W2559655401","https://openalex.org/W2596110958","https://openalex.org/W2599354622","https://openalex.org/W2807834884","https://openalex.org/W2891058410","https://openalex.org/W2914917317","https://openalex.org/W2963165400","https://openalex.org/W2963491064","https://openalex.org/W3009593063","https://openalex.org/W3013361100","https://openalex.org/W3033117456","https://openalex.org/W3046095786","https://openalex.org/W3107856286","https://openalex.org/W3157846425","https://openalex.org/W3184788154","https://openalex.org/W3210590548","https://openalex.org/W4214554111","https://openalex.org/W4226051392","https://openalex.org/W4231845305","https://openalex.org/W4240592325","https://openalex.org/W4365800072","https://openalex.org/W4375930434","https://openalex.org/W4386601774","https://openalex.org/W4388994762","https://openalex.org/W4391769935","https://openalex.org/W4392845866","https://openalex.org/W7048781019"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Car-following":[0],"trajectory":[1,23,54,99,118],"generation":[2,57],"and":[3,26,58,75,93,120,143,169],"anomaly":[4,59,100,121,153],"detection":[5],"are":[6],"critical":[7],"functions":[8],"in":[9,111,165],"the":[10,44,94,125,160],"sensing":[11],"module":[12],"of":[13,65,140,147],"an":[14,38,68,76,152],"automated":[15],"vehicle.":[16],"However,":[17],"developing":[18],"models":[19],"that":[20,159],"capture":[21],"realistic":[22,134],"data":[24,55,142],"distribution":[25,139],"detect":[27],"anomalous":[28,148,171],"driving":[29,172],"behaviors":[30],"could":[31],"be":[32],"challenging.":[33],"This":[34],"paper":[35],"proposes":[36],"'TrajGAN',":[37],"unsupervised":[39],"learning":[40],"approach":[41,162],"based":[42,80,150],"on":[43,151],"Generative":[45],"Adversarial":[46],"Network":[47],"(GAN)":[48],"to":[49,87],"exploit":[50],"vehicle":[51],"car":[52,90],"following":[53,91],"for":[56,98],"detection.":[60,101],"The":[61],"proposed":[62,161],"TrajGAN":[63,131],"consists":[64],"two":[66,105],"modules,":[67],"encoder-decoder":[69],"Long":[70],"Short-Term":[71],"Memory":[72],"(LSTM)-based":[73],"generator":[74],"LSTM-multilayer":[77],"perceptron":[78],"(MLP)":[79],"discriminator,":[81],"whose":[82],"former":[83],"component":[84],"is":[85,97,163],"used":[86],"generate":[88,133],"vehicular":[89],"trajectories":[92,135,149,168],"latter":[95],"one":[96],"By":[102],"letting":[103],"these":[104],"modules":[106],"game":[107],"with":[108,124,136],"each":[109],"other":[110],"training,":[112],"we":[113],"can":[114,132],"simultaneously":[115],"achieve":[116],"robust":[117],"generators":[119],"detectors.":[122],"Trained":[123],"Next":[126],"Generation":[127],"Simulation":[128,156],"(NGSIM)":[129],"dataset,":[130],"a":[137,145],"similar":[138],"training":[141],"identify":[144],"manifold":[146],"scoring":[154],"scheme.":[155],"results":[157],"indicate":[158],"efficient":[164],"reproducing":[166],"artificial":[167],"identifying":[170],"behaviors.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":3}],"updated_date":"2026-05-19T08:33:51.333923","created_date":"2025-10-10T00:00:00"}
