{"id":"https://openalex.org/W3117627296","doi":"https://doi.org/10.1109/itsc45102.2020.9294394","title":"Anomalous State Recognition of Lane-changing Behavior using a Hybrid Autoencoder Architecture","display_name":"Anomalous State Recognition of Lane-changing Behavior using a Hybrid Autoencoder Architecture","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3117627296","doi":"https://doi.org/10.1109/itsc45102.2020.9294394","mag":"3117627296"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","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/A5083020200","display_name":"Pengcheng Fan","orcid":"https://orcid.org/0000-0002-3399-7041"},"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":"Pengcheng Fan","raw_affiliation_strings":["The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008704565","display_name":"Yangzexi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yangzexi Liu","raw_affiliation_strings":["Chengdu Eastern New City Development Committee, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chengdu Eastern New City Development Committee, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021982777","display_name":"Jingqiu Guo","orcid":"https://orcid.org/0000-0002-7378-3101"},"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":"Jingqiu Guo","raw_affiliation_strings":["The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664211","display_name":"Yibing Wang","orcid":"https://orcid.org/0000-0001-9937-2055"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibing Wang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101927712","display_name":"Min Qiu","orcid":"https://orcid.org/0000-0002-2379-0194"},"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":"Min Qiu","raw_affiliation_strings":["The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"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":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9932000041007996,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9927999973297119,"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/autoencoder","display_name":"Autoencoder","score":0.9375240206718445},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7152349948883057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6975259780883789},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.672599732875824},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6590752005577087},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6422306895256042},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5599746704101562},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5253159999847412},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5002520084381104},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4859417676925659},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4740859270095825},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4589494466781616},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4018881320953369},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37852707505226135},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12004232406616211}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9375240206718445},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7152349948883057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6975259780883789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.672599732875824},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6590752005577087},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6422306895256042},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5599746704101562},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5253159999847412},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5002520084381104},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4859417676925659},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4740859270095825},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4589494466781616},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4018881320953369},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37852707505226135},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12004232406616211},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/itsc45102.2020.9294394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1526455201","https://openalex.org/W1967444754","https://openalex.org/W1993837778","https://openalex.org/W2046017949","https://openalex.org/W2105497548","https://openalex.org/W2153740685","https://openalex.org/W2187089797","https://openalex.org/W2279065863","https://openalex.org/W2340896621","https://openalex.org/W2479935243","https://openalex.org/W2578339457","https://openalex.org/W2586457790","https://openalex.org/W2783256039","https://openalex.org/W2791925274","https://openalex.org/W2847226470","https://openalex.org/W2883109703","https://openalex.org/W2905443974","https://openalex.org/W2947106284","https://openalex.org/W2950242191","https://openalex.org/W2972960783","https://openalex.org/W3005147439","https://openalex.org/W3012140785","https://openalex.org/W3015848170","https://openalex.org/W4376523999","https://openalex.org/W6721527053","https://openalex.org/W6748854608","https://openalex.org/W6750574455"],"related_works":["https://openalex.org/W4220775285","https://openalex.org/W3136979370","https://openalex.org/W2141705618","https://openalex.org/W4213225422","https://openalex.org/W2076520961","https://openalex.org/W3044458868","https://openalex.org/W2348964713","https://openalex.org/W2785535669","https://openalex.org/W1525322161","https://openalex.org/W4312721464"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,36,120],"hybrid":[4],"unsupervised":[5],"architecture":[6],"for":[7,90],"anomalous":[8,72,104],"lane-changing":[9,73,107,126,137],"behavior":[10,19,52,74,138],"recognition.":[11],"Anomaly":[12],"detection":[13],"aims":[14],"to":[15,43,70,119],"identify":[16,71],"unusual":[17,136],"driving":[18],"caused":[20],"by":[21,80],"either":[22],"environmental":[23],"or":[24],"phycological":[25],"stimuli,":[26],"and":[27,61,105,128],"is":[28,41],"of":[29,58],"great":[30],"important":[31,131],"in":[32,75,93],"road":[33],"safety.":[34],"First,":[35],"Recurrent":[37],"Convolutional":[38],"Autoencoder":[39],"(RC-AE)":[40],"built":[42],"explore":[44],"the":[45,50,59,76,94,99],"spatial-temporal":[46],"features":[47],"derived":[48],"from":[49],"high-dimensional":[51],"data.":[53],"Second,":[54],"Reconstruct":[55],"Error":[56],"analysis":[57],"autoencoder":[60],"one-class":[62],"support":[63],"vector":[64],"machine":[65],"method":[66],"are":[67],"both":[68],"applied":[69],"learned":[77],"feature":[78],"space":[79],"autoencoder.":[81],"Last,":[82],"we":[83],"employ":[84],"T-Distributed":[85],"Stochastic":[86],"Neighbor":[87],"Embedding":[88],"(T-SNE)":[89],"data":[91],"visualization":[92],"anomaly":[95],"detection.":[96],"Based":[97],"on":[98,123],"kernel":[100],"density":[101],"estimation":[102],"analysis,":[103],"normal":[106],"sample":[108],"groups":[109],"display":[110],"distinct":[111],"difference":[112],"over":[113],"probability":[114],"distributions.":[115],"The":[116],"findings":[117],"contribute":[118],"better":[121],"understanding":[122],"drivers'":[124],"natural":[125],"behavior,":[127],"can":[129],"provide":[130],"insight":[132],"into":[133],"real-time":[134],"personalized":[135],"monitoring":[139],"system":[140],"development.":[141]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
