{"id":"https://openalex.org/W2067907977","doi":"https://doi.org/10.1109/ivs.2014.6856506","title":"Visualization of driving behavior using deep sparse autoencoder","display_name":"Visualization of driving behavior using deep sparse autoencoder","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2067907977","doi":"https://doi.org/10.1109/ivs.2014.6856506","mag":"2067907977"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2014.6856506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2014.6856506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","raw_type":"proceedings-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/A5100606445","display_name":"Hailong Liu","orcid":"https://orcid.org/0000-0003-2195-3380"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"HaiLong Liu","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP","Grad. Sch. of Inf. Sci. & Eng, Ritsumeikan Univ., Kusatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Grad. Sch. of Inf. Sci. & Eng, Ritsumeikan Univ., Kusatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023160093","display_name":"Tadahiro Taniguchi","orcid":"https://orcid.org/0000-0002-5682-2076"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tadahiro Taniguchi","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP","Ritsumeikan Univ., Kasatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan Univ., Kasatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102127221","display_name":"Tosiaki Takano","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tosiaki Takano","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP","Ritsumeikan Univ., Kasatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan Univ., Kasatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000602137","display_name":"Yusuke Tanaka","orcid":"https://orcid.org/0000-0002-7316-1425"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Yusuke Tanaka","raw_affiliation_strings":["Technical Research Division, Toyota InfoTech-nology Center Co., Ltd","Toyota InfoTech-nology Center Co. Ltd., Toyota, Japan"],"affiliations":[{"raw_affiliation_string":"Technical Research Division, Toyota InfoTech-nology Center Co., Ltd","institution_ids":["https://openalex.org/I1293612202"]},{"raw_affiliation_string":"Toyota InfoTech-nology Center Co. Ltd., Toyota, Japan","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077857378","display_name":"Kazuhito Takenaka","orcid":"https://orcid.org/0009-0001-0821-2724"},"institutions":[{"id":"https://openalex.org/I67530263","display_name":"Denso (United States)","ror":"https://ror.org/02w314k38","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132650","https://openalex.org/I67530263"]},{"id":"https://openalex.org/I4210132650","display_name":"Denso (Japan)","ror":"https://ror.org/04hkpfa76","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210132650"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Kazuhito Takenaka","raw_affiliation_strings":["Kabushiki Kaisha Denso, Kariya, Aichi, JP","Corp. R&D Div 3, DENSO Corp., Kariya, Japan"],"affiliations":[{"raw_affiliation_string":"Kabushiki Kaisha Denso, Kariya, Aichi, JP","institution_ids":["https://openalex.org/I4210132650"]},{"raw_affiliation_string":"Corp. R&D Div 3, DENSO Corp., Kariya, Japan","institution_ids":["https://openalex.org/I67530263"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067199220","display_name":"Takashi Bando","orcid":"https://orcid.org/0000-0001-8662-0742"},"institutions":[{"id":"https://openalex.org/I4210132650","display_name":"Denso (Japan)","ror":"https://ror.org/04hkpfa76","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210132650"]},{"id":"https://openalex.org/I67530263","display_name":"Denso (United States)","ror":"https://ror.org/02w314k38","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132650","https://openalex.org/I67530263"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Takashi Bando","raw_affiliation_strings":["Kabushiki Kaisha Denso, Kariya, Aichi, JP","Corp. R&D Div 3, DENSO Corp., Kariya, Japan"],"affiliations":[{"raw_affiliation_string":"Kabushiki Kaisha Denso, Kariya, Aichi, JP","institution_ids":["https://openalex.org/I4210132650"]},{"raw_affiliation_string":"Corp. R&D Div 3, DENSO Corp., Kariya, Japan","institution_ids":["https://openalex.org/I67530263"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100606445"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":4.715,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.94657159,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9962000250816345,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9962000250816345,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9739000201225281,"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/autoencoder","display_name":"Autoencoder","score":0.8237512111663818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.704013466835022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6882118582725525},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6606197953224182},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6282742023468018},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5485079288482666},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5445551872253418},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5218821167945862},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5119019746780396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45071470737457275},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44145432114601135},{"id":"https://openalex.org/keywords/cube","display_name":"Cube (algebra)","score":0.42752766609191895},{"id":"https://openalex.org/keywords/color-space","display_name":"Color space","score":0.41959965229034424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16599765419960022}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8237512111663818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.704013466835022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6882118582725525},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6606197953224182},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6282742023468018},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5485079288482666},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5445551872253418},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5218821167945862},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5119019746780396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45071470737457275},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44145432114601135},{"id":"https://openalex.org/C53051483","wikidata":"https://www.wikidata.org/wiki/Q861555","display_name":"Cube (algebra)","level":2,"score":0.42752766609191895},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.41959965229034424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16599765419960022},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2014.6856506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2014.6856506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1813659000","https://openalex.org/W1971414456","https://openalex.org/W1989162056","https://openalex.org/W1995122024","https://openalex.org/W2027911950","https://openalex.org/W2083463163","https://openalex.org/W2084835622","https://openalex.org/W2090894570","https://openalex.org/W4231109964"],"related_works":["https://openalex.org/W4220775285","https://openalex.org/W3158522902","https://openalex.org/W2785535669","https://openalex.org/W2152649853","https://openalex.org/W2012871320","https://openalex.org/W2216935007","https://openalex.org/W2384427992","https://openalex.org/W2035701981","https://openalex.org/W2088277496","https://openalex.org/W2547255861"],"abstract_inverted_index":{"Driving":[0,87,90,93,103,160],"behavioral":[1,68],"data":[2,29,69],"is":[3,30,95,106,121],"too":[4],"high-dimensional":[5,28,65],"for":[6,34,140],"people":[7,170],"to":[8,36,58,130,171],"review":[9],"their":[10,38,48],"driving":[11,39,50,67,154,174,200],"behavior.":[12,51,175,201],"It":[13],"includes":[14],"accelerator":[15],"opening":[16],"rate,":[17],"steering":[18],"angle,":[19],"brake":[20],"Master-Cylinder":[21],"pressure":[22],"and":[23,89,148,163],"other":[24,189,208],"various":[25],"information.":[26],"The":[27,119],"not":[31],"very":[32],"intuitive":[33],"drivers":[35],"understand":[37],"behavior":[40,155],"when":[41],"they":[42],"take":[43],"a":[44,54,72,96,107,112,203],"look":[45],"back":[46],"on":[47,77,111],"recorded":[49,153],"We":[52],"used":[53],"deep":[55,185],"sparse":[56,186],"autoencoder":[57,187],"extract":[59],"the":[60,116,124,131,137,158,178,193],"low-dimensional":[61,79,142,181],"high-level":[62],"representation":[63,98],"from":[64,71,192],"raw":[66],"obtained":[70],"control":[73],"area":[74],"network.":[75],"Based":[76],"this":[78],"representation,":[80,182],"we":[81,144,183],"propose":[82],"two":[83],"visualization":[84,166],"methods":[85,167,191,206],"called":[86],"Cube":[88,94],"Color":[91,104,161],"Map.":[92],"cubic":[97],"displaying":[99],"extracted":[100,117,132],"three-dimensional":[101,133],"features.":[102,118,134],"Map":[105,162],"colored":[108,122],"trajectory":[109,120],"shown":[110],"road":[113],"map":[114],"representing":[115],"using":[123],"RGB":[125],"color":[126],"space,":[127],"which":[128],"corresponds":[129],"To":[135,176],"evaluate":[136,177],"proposed":[138],"method":[139],"extracting":[141],"feature,":[143],"conducted":[145],"an":[146],"experiment":[147],"found":[149],"several":[150],"differences":[151],"with":[152,188],"by":[156],"viewing":[157],"visualized":[159],"that":[164],"our":[165,205],"can":[168],"help":[169],"recognize":[172],"different":[173],"effectiveness":[179],"of":[180,195,198],"compared":[184],"conventional":[190,209],"viewpoint":[194],"linear":[196],"separability":[197],"elemental":[199],"As":[202],"result,":[204],"outperformed":[207],"methods.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
