{"id":"https://openalex.org/W2516360646","doi":"https://doi.org/10.1109/ivs.2016.7535560","title":"Driving word2vec: Distributed semantic vector representation for symbolized naturalistic driving data","display_name":"Driving word2vec: Distributed semantic vector representation for symbolized naturalistic driving data","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2516360646","doi":"https://doi.org/10.1109/ivs.2016.7535560","mag":"2516360646"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2016.7535560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","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/A5010861208","display_name":"Yusuke Fuchida","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":true,"raw_author_name":"Yusuke Fuchida","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","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"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102759799","display_name":"Toshiaki Takano","orcid":"https://orcid.org/0009-0001-8718-3115"},"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":"Toshiaki Takano","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110704524","display_name":"Takuma Mori","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":"Takuma Mori","raw_affiliation_strings":["Ritsumeikan Daigaku, Kyoto, Kyoto, JP"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan Daigaku, Kyoto, Kyoto, JP","institution_ids":["https://openalex.org/I135768898"]}]},{"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/I4210132650","display_name":"Denso (Japan)","ror":"https://ror.org/04hkpfa76","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210132650"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhito Takenaka","raw_affiliation_strings":["Kabushiki Kaisha Denso, Kariya, Aichi, JP"],"affiliations":[{"raw_affiliation_string":"Kabushiki Kaisha Denso, Kariya, Aichi, JP","institution_ids":["https://openalex.org/I4210132650"]}]},{"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/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":["US"],"is_corresponding":false,"raw_author_name":"Takashi Bando","raw_affiliation_strings":["Denso International America Inc, Southfield, MI, US"],"affiliations":[{"raw_affiliation_string":"Denso International America Inc, Southfield, MI, US","institution_ids":["https://openalex.org/I67530263"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5010861208"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":0.8569,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83986087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"1313","last_page":"1320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9959999918937683,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9959999918937683,"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/T11309","display_name":"Music and Audio Processing","score":0.9944999814033508,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9937000274658203,"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/word2vec","display_name":"Word2vec","score":0.9650551080703735},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7258206009864807},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.7011306285858154},{"id":"https://openalex.org/keywords/articulation","display_name":"Articulation (sociology)","score":0.5236049294471741},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.4500185251235962},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44773924350738525},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4249138832092285},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3950822353363037}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.9650551080703735},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258206009864807},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7011306285858154},{"id":"https://openalex.org/C2779337067","wikidata":"https://www.wikidata.org/wiki/Q4800961","display_name":"Articulation (sociology)","level":3,"score":0.5236049294471741},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.4500185251235962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44773924350738525},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4249138832092285},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3950822353363037},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2016.7535560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W77912868","https://openalex.org/W1550337734","https://openalex.org/W1583553994","https://openalex.org/W1614298861","https://openalex.org/W1880262756","https://openalex.org/W1972441921","https://openalex.org/W1995122024","https://openalex.org/W2005958977","https://openalex.org/W2011760672","https://openalex.org/W2027911950","https://openalex.org/W2052080463","https://openalex.org/W2091150693","https://openalex.org/W2105015918","https://openalex.org/W2109797278","https://openalex.org/W2133362858","https://openalex.org/W2140991203","https://openalex.org/W2153579005","https://openalex.org/W2170003873","https://openalex.org/W2278108376","https://openalex.org/W2290437651","https://openalex.org/W2508541768","https://openalex.org/W3104490327","https://openalex.org/W4294170691","https://openalex.org/W6603126656","https://openalex.org/W6634668525","https://openalex.org/W6636510571","https://openalex.org/W6639619044","https://openalex.org/W6682691769","https://openalex.org/W6696886522","https://openalex.org/W6725237847"],"related_works":["https://openalex.org/W2980729574","https://openalex.org/W1560851690","https://openalex.org/W3092047717","https://openalex.org/W2770162183","https://openalex.org/W4390881630","https://openalex.org/W3110772647","https://openalex.org/W2894231409","https://openalex.org/W2947721150","https://openalex.org/W2995297654","https://openalex.org/W3127365535"],"abstract_inverted_index":{"This":[0],"study":[1,54],"describes":[2],"driving":[3,15,85,91,124,132,155,171,180],"word2vec":[4],"(DW2V),":[5],"a":[6,56,60,77,115,144,175,189],"new":[7],"method":[8,58,116],"for":[9,22,117],"forming":[10],"semantic":[11,48,97,121,149,167],"representations":[12],"of":[13,38,90,96,123,152,177,179,192],"naturalistic":[14,154],"data":[16,39,87,156],"(NDD).":[17],"To":[18],"use":[19],"big":[20],"NDD":[21,67],"developing":[23],"driver":[24],"assistance":[25],"systems":[26],"or":[27],"other":[28],"information":[29,98],"services,":[30],"it":[31],"is":[32],"important":[33],"to":[34,113,142,186],"compress":[35],"large":[36],"amounts":[37],"into":[40,88],"an":[41,73,119],"abstract":[42],"and":[43,68,194],"compact":[44],"representation":[45,122,151],"without":[46],"losing":[47],"information.":[49],"For":[50],"this":[51,53],"purpose,":[52],"uses":[55,135],"symbolization":[57],"using":[59],"double":[61,78],"articulation":[62,79],"analyzer":[63],"(DAA)":[64],"assuming":[65],"that":[66,126,162],"human":[69],"speech":[70],"signals":[71],"share":[72],"analogous":[74],"structure,":[75],"called":[76],"structure.":[80],"The":[81],"DAA":[82],"can":[83,164],"encode":[84],"behavior":[86],"sequences":[89,102,178],"words.":[92,133],"However,":[93],"the":[94,128,147,166],"amount":[95],"contained":[99],"in":[100],"these":[101],"has":[103],"not":[104],"been":[105,111],"clarified.":[106],"Very":[107],"few":[108],"attempts":[109],"have":[110],"made":[112],"develop":[114],"obtaining":[118],"adequate":[120],"words":[125],"explains":[127],"relationship":[129],"between":[130,169],"different":[131,170],"DW2V":[134,163,193],"word2vec,":[136],"proposed":[137],"by":[138],"Mikolov":[139],"et":[140],"al.,":[141],"make":[143],"system":[145],"learn":[146],"distributed":[148],"vector":[150],"symbolized":[153],"(SNDD).":[157],"Through":[158],"experiments,":[159],"we":[160],"show":[161],"restore":[165],"relationships":[168],"scenes":[172],"from":[173],"only":[174],"set":[176],"words,":[181],"i.e.,":[182],"SNDD.":[183],"In":[184],"addition":[185],"quantitative":[187],"analysis,":[188],"qualitative":[190],"analysis":[191],"its":[195],"potential":[196],"applications":[197],"are":[198],"discussed.":[199]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
