{"id":"https://openalex.org/W2764113292","doi":"https://doi.org/10.1145/3131726.3131752","title":"Driver State Estimation Based on Dynamic Bayesian Networks Considering Different Age and Gender Groups","display_name":"Driver State Estimation Based on Dynamic Bayesian Networks Considering Different Age and Gender Groups","publication_year":2017,"publication_date":"2017-09-24","ids":{"openalex":"https://openalex.org/W2764113292","doi":"https://doi.org/10.1145/3131726.3131752","mag":"2764113292"},"language":"en","primary_location":{"id":"doi:10.1145/3131726.3131752","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3131726.3131752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct","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/A5006525911","display_name":"Ji Hyun Yang","orcid":"https://orcid.org/0000-0002-8349-6931"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ji Hyun Yang","raw_affiliation_strings":["Kookmin University, Seoul, ROK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kookmin University, Seoul, ROK","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012445022","display_name":"Hyeon-Bin Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeon-Bin Jeong","raw_affiliation_strings":["Kookmin University, Seoul, ROK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kookmin University, Seoul, ROK","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021054166","display_name":"Jihyuck Han","orcid":null},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jihyuck Han","raw_affiliation_strings":["Kookmin University, Seoul, ROK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kookmin University, Seoul, ROK","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072506078","display_name":"Sejoon Lim","orcid":"https://orcid.org/0000-0003-1917-699X"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sejoon Lim","raw_affiliation_strings":["Kookmin University, Seoul, ROK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kookmin University, Seoul, ROK","institution_ids":["https://openalex.org/I110273157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.16627764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"131","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/workload","display_name":"Workload","score":0.6614595651626587},{"id":"https://openalex.org/keywords/distraction","display_name":"Distraction","score":0.6128773093223572},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.5756958723068237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5575147867202759},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5220465660095215},{"id":"https://openalex.org/keywords/alarm","display_name":"ALARM","score":0.5144384503364563},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.49969029426574707},{"id":"https://openalex.org/keywords/goodness-of-fit","display_name":"Goodness of fit","score":0.49507036805152893},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4787439703941345},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4459032118320465},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.41012051701545715},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35544252395629883},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.32829007506370544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31118762493133545},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22077953815460205},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20889812707901},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1286889910697937}],"concepts":[{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.6614595651626587},{"id":"https://openalex.org/C2776378700","wikidata":"https://www.wikidata.org/wiki/Q3030775","display_name":"Distraction","level":2,"score":0.6128773093223572},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.5756958723068237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5575147867202759},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5220465660095215},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.5144384503364563},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.49969029426574707},{"id":"https://openalex.org/C132480984","wikidata":"https://www.wikidata.org/wiki/Q2034239","display_name":"Goodness of fit","level":2,"score":0.49507036805152893},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4787439703941345},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4459032118320465},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.41012051701545715},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35544252395629883},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.32829007506370544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31118762493133545},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22077953815460205},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20889812707901},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1286889910697937},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3131726.3131752","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3131726.3131752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct","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":8,"referenced_works":["https://openalex.org/W1601122084","https://openalex.org/W1968299435","https://openalex.org/W2023293610","https://openalex.org/W2048724334","https://openalex.org/W2052604480","https://openalex.org/W2160039372","https://openalex.org/W2245408840","https://openalex.org/W2402683382"],"related_works":["https://openalex.org/W1984342691","https://openalex.org/W2470048815","https://openalex.org/W2324884046","https://openalex.org/W641612223","https://openalex.org/W2906771794","https://openalex.org/W2028203774","https://openalex.org/W2174716869","https://openalex.org/W4205873045","https://openalex.org/W2080126316","https://openalex.org/W146094510"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,53,113],"develop":[4],"a":[5,25,30,37,61,88],"driver-state":[6],"estimation":[7],"algorithm":[8,52,70,86],"based":[9],"on":[10],"multi-modal":[11],"information":[12],"for":[13,94],"various":[14],"age":[15],"and":[16,29,48,73,78,101],"gender":[17],"groups.":[18],"A":[19],"test":[20],"bed":[21],"was":[22,58,71,127],"built":[23],"under":[24,115],"simulated":[26],"driving":[27],"environment,":[28],"total":[31],"of":[32,39,45,67,90,92,110,121],"56":[33],"volunteers":[34],"participated":[35],"in":[36],"series":[38],"experiments":[40],"that":[41],"included":[42],"involved":[43],"states":[44],"drowsiness,":[46],"distraction,":[47],"high":[49],"workload.":[50],"The":[51,65,85,108],"estimate":[54],"the":[55,68,83,116,119,122],"driver":[56],"state":[57],"developed":[59,69],"using":[60],"dynamic":[62],"Bayesian":[63],"network.":[64],"performance":[66],"verified":[72],"supplemented":[74],"through":[75],"vehicle,":[76],"physiological,":[77],"image":[79],"data":[80],"obtained":[81],"from":[82],"experiments.":[84],"showed":[87],"goodness":[89,109],"fit":[91,111],"77.8%":[93],"correct":[95],"detection":[96],"rates":[97,104],"greater":[98],"than":[99,106,129],"0.7":[100],"false":[102],"alarm":[103],"less":[105],"0.3.":[107],"increased":[112],"85.7%":[114],"condition":[117],"where":[118],"area":[120],"receiver":[123],"operating":[124],"characteristic":[125],"curve":[126],"more":[128],"0.7.":[130]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}