{"id":"https://openalex.org/W2981446900","doi":"https://doi.org/10.1109/icite.2019.8880150","title":"Temporary Vehicle ID With Deep Hashing","display_name":"Temporary Vehicle ID With Deep Hashing","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2981446900","doi":"https://doi.org/10.1109/icite.2019.8880150","mag":"2981446900"},"language":"en","primary_location":{"id":"doi:10.1109/icite.2019.8880150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite.2019.8880150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 4th International Conference on Intelligent Transportation Engineering (ICITE)","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/A5101799598","display_name":"Rui Guo","orcid":"https://orcid.org/0000-0003-0659-4025"},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Guo","raw_affiliation_strings":["R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA","institution_ids":["https://openalex.org/I4210093665"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007609871","display_name":"Haritha Muralidharan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haritha Muralidharan","raw_affiliation_strings":["R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA","institution_ids":["https://openalex.org/I4210093665"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038132951","display_name":"Shalini Keshavamurthy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shalini Keshavamurthy","raw_affiliation_strings":["R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA","institution_ids":["https://openalex.org/I4210093665"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025075066","display_name":"Kentaro Oguchi","orcid":"https://orcid.org/0000-0001-9724-6434"},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kentaro Oguchi","raw_affiliation_strings":["R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D InfoTech Labs, Toyota Motor North America, Mountain View, CA, USA","institution_ids":["https://openalex.org/I4210093665"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210093665"],"apc_list":null,"apc_paid":null,"fwci":0.1016,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45542294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"313","issue":null,"first_page":"169","last_page":"174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.8035259246826172},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7956269383430481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.513960063457489},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.505997359752655},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.4867560863494873},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.45733481645584106},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3861798048019409},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36341190338134766},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.3106827437877655},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.18765154480934143}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035259246826172},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7956269383430481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.513960063457489},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.505997359752655},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.4867560863494873},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.45733481645584106},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3861798048019409},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36341190338134766},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3106827437877655},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.18765154480934143}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icite.2019.8880150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite.2019.8880150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 4th International Conference on Intelligent Transportation Engineering (ICITE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"},{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1815811802","https://openalex.org/W1979260620","https://openalex.org/W2062368515","https://openalex.org/W2098556421","https://openalex.org/W2100495367","https://openalex.org/W2125447566","https://openalex.org/W2128942651","https://openalex.org/W2211629196","https://openalex.org/W2405027029","https://openalex.org/W2470710488","https://openalex.org/W2508837377","https://openalex.org/W2531440880","https://openalex.org/W2554539212","https://openalex.org/W2733548594","https://openalex.org/W2796581872","https://openalex.org/W2897833707","https://openalex.org/W2911048887","https://openalex.org/W2913932916","https://openalex.org/W2963398644","https://openalex.org/W2963860801","https://openalex.org/W3106250896","https://openalex.org/W6637373629","https://openalex.org/W6688152296","https://openalex.org/W6725199262","https://openalex.org/W6728374919","https://openalex.org/W6758527398","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4324372666","https://openalex.org/W4225706866","https://openalex.org/W2914646191"],"abstract_inverted_index":{"The":[0,122],"growing":[1],"development":[2],"of":[3,37,75,125,158,166],"sensing":[4,97,161],"and":[5,40,67,98,116,136],"deep":[6,55,76,110],"learning":[7,56,77],"technologies":[8],"greatly":[9],"extend":[10],"vehicle":[11,95,100,127,146,168],"perception":[12,25],"capabilities,":[13],"which":[14,70],"are":[15,119],"critical":[16],"for":[17],"intelligent":[18,79,167],"vehicles.":[19,81],"However,":[20],"to":[21,92],"achieve":[22],"high":[23,35,64],"quality":[24],"within":[26],"the":[27,34,54,72,126,144,155],"vehicle's":[28],"onboard":[29],"electrical":[30],"control":[31],"unit":[32],"(ECU),":[33],"efficiency":[36],"edge":[38],"computing":[39],"data":[41,68],"encoding":[42],"is":[43,47,60],"essential.":[44],"Unfortunately,":[45],"this":[46,84,105],"a":[48,88,107,149],"less":[49],"studied":[50],"upon":[51],"topic":[52],"from":[53],"perspective,":[57],"as":[58],"it":[59],"always":[61],"associated":[62],"with":[63,113],"computational":[65],"overhead":[66],"throughput,":[69],"limit":[71],"wide":[73],"applications":[74,165],"in":[78,163],"connected":[80],"To":[82,104],"solve":[83],"problem,":[85],"we":[86],"propose":[87],"novel":[89],"deep-learning":[90],"architecture":[91],"simultaneously":[93],"implement":[94],"on-board":[96],"detected":[99],"identification":[101],"(ID)":[102],"creation.":[103],"end,":[106],"single":[108],"pass":[109],"neural":[111],"network":[112],"triplet":[114],"structure":[115],"hashing":[117],"encoder":[118],"delicately":[120],"designed.":[121],"remarkable":[123],"properties":[124],"ID":[128,147],"include":[129],"discriminant":[130],"representation,":[131],"privacy":[132],"preservation,":[133],"geolocation":[134],"awareness,":[135],"minimal":[137],"communication":[138],"requirements.":[139],"All":[140],"these":[141],"potentials":[142],"make":[143],"proposed":[145],"be":[148],"viable":[150],"representation":[151],"that":[152],"can":[153],"meet":[154],"strict":[156],"requirements":[157],"realtime":[159],"large-scale":[160],"analytics":[162],"practical":[164],"systems.":[169]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
