{"id":"https://openalex.org/W3195670858","doi":"https://doi.org/10.1080/10447318.2021.1965774","title":"Predicting Driver Fatigue in Monotonous Automated Driving with Explanation using GPBoost and SHAP","display_name":"Predicting Driver Fatigue in Monotonous Automated Driving with Explanation using GPBoost and SHAP","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3195670858","doi":"https://doi.org/10.1080/10447318.2021.1965774","mag":"3195670858"},"language":"en","primary_location":{"id":"doi:10.1080/10447318.2021.1965774","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10447318.2021.1965774","pdf_url":null,"source":{"id":"https://openalex.org/S165559636","display_name":"International Journal of Human-Computer Interaction","issn_l":"1044-7318","issn":["1044-7318","1532-7590"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Human\u2013Computer Interaction","raw_type":"journal-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/A5047702220","display_name":"Feng Zhou","orcid":"https://orcid.org/0000-0001-6123-073X"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Feng Zhou","raw_affiliation_strings":["Department of Industrial and Manufacturing Systems Engineering, The University of Michigan, Dearborn, USA"],"raw_orcid":"https://orcid.org/0000-0001-6123-073X","affiliations":[{"raw_affiliation_string":"Department of Industrial and Manufacturing Systems Engineering, The University of Michigan, Dearborn, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027784898","display_name":"Areen Alsaid","orcid":"https://orcid.org/0000-0003-2852-9750"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Areen Alsaid","raw_affiliation_strings":["Department of Industrial and Systems Engineering, The University of Wisconsin, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, The University of Wisconsin, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050195403","display_name":"Mike Blommer","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mike Blommer","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082733995","display_name":"Reates Curry","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Reates Curry","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069887812","display_name":"Radhakrishnan Swaminathan","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Radhakrishnan Swaminathan","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004773847","display_name":"Dev S. Kochhar","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dev Kochhar","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060808424","display_name":"Walter Talamonti","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Walter Talamonti","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037151938","display_name":"Louis Tijerina","orcid":null},"institutions":[{"id":"https://openalex.org/I1292974536","display_name":"Ford Motor Company (United States)","ror":"https://ror.org/00g2tkw06","country_code":"US","type":"company","lineage":["https://openalex.org/I1292974536"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Louis Tijerina","raw_affiliation_strings":["Ford Motor Company Research and Advanced Engineering, Dearborn, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Motor Company Research and Advanced Engineering, Dearborn, USA","institution_ids":["https://openalex.org/I1292974536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5047702220"],"corresponding_institution_ids":["https://openalex.org/I4210130704"],"apc_list":null,"apc_paid":null,"fwci":2.9097,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.90739459,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"38","issue":"8","first_page":"719","last_page":"729"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9995999932289124,"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":0.9995999932289124,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.967199981212616,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/mean-squared-error","display_name":"Mean squared error","score":0.6818848252296448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5120783448219299},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5096125602722168},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5073859095573425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5065089464187622},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.4756459891796112},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46090540289878845},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.45937785506248474},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42572855949401855},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41393330693244934},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3647174835205078},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3111152946949005},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24117562174797058}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6818848252296448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5120783448219299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5096125602722168},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5073859095573425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5065089464187622},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4756459891796112},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46090540289878845},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.45937785506248474},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42572855949401855},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41393330693244934},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3647174835205078},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3111152946949005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24117562174797058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10447318.2021.1965774","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10447318.2021.1965774","pdf_url":null,"source":{"id":"https://openalex.org/S165559636","display_name":"International Journal of Human-Computer Interaction","issn_l":"1044-7318","issn":["1044-7318","1532-7590"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Human\u2013Computer Interaction","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W37159462","https://openalex.org/W164299287","https://openalex.org/W2014925481","https://openalex.org/W2024081693","https://openalex.org/W2032616808","https://openalex.org/W2032977109","https://openalex.org/W2052604480","https://openalex.org/W2075000159","https://openalex.org/W2094726855","https://openalex.org/W2118851874","https://openalex.org/W2134738818","https://openalex.org/W2153639503","https://openalex.org/W2161638362","https://openalex.org/W2165878663","https://openalex.org/W2170498360","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2323127344","https://openalex.org/W2556398113","https://openalex.org/W2562008018","https://openalex.org/W2562982737","https://openalex.org/W2565699923","https://openalex.org/W2594475271","https://openalex.org/W2612690371","https://openalex.org/W2759836560","https://openalex.org/W2791470693","https://openalex.org/W2890022903","https://openalex.org/W2892741787","https://openalex.org/W2894818097","https://openalex.org/W2898665421","https://openalex.org/W2904607875","https://openalex.org/W2907378463","https://openalex.org/W2910705748","https://openalex.org/W2933441117","https://openalex.org/W2941513864","https://openalex.org/W2974515736","https://openalex.org/W2984904339","https://openalex.org/W2999615587","https://openalex.org/W2999995229","https://openalex.org/W3000998105","https://openalex.org/W3003266444","https://openalex.org/W3005933620","https://openalex.org/W3044413741","https://openalex.org/W3094387283","https://openalex.org/W3095669736","https://openalex.org/W3102476541","https://openalex.org/W3124080842","https://openalex.org/W3135835076","https://openalex.org/W3161692923","https://openalex.org/W4225555528","https://openalex.org/W4236133481"],"related_works":["https://openalex.org/W2757711895","https://openalex.org/W3159367627","https://openalex.org/W4300642372","https://openalex.org/W1490710791","https://openalex.org/W4385577504","https://openalex.org/W2974764284","https://openalex.org/W2917200448","https://openalex.org/W4383560912","https://openalex.org/W2944883203","https://openalex.org/W3206297887"],"abstract_inverted_index":{"Research":[0],"indicates":[1],"that":[2,113],"monotonous":[3],"automated":[4,192,204],"driving":[5,193,205],"increases":[6],"the":[7,71,85,94,118,150,157,163,167,199],"incidence":[8],"of":[9,50,74,81,166],"fatigued":[10],"driving.":[11,208],"Although":[12],"many":[13],"prediction":[14,183],"models":[15,41,128],"based":[16],"on":[17],"advanced":[18],"machine":[19,39,126],"learning":[20,40,127],"techniques":[21],"were":[22],"proposed":[23,47],"to":[24,60,69,116,148,189,206],"monitor":[25],"driver":[26,62,75,101,181],"fatigue,":[27,76],"especially":[28],"in":[29,67,191],"manual":[30,207],"driving,":[31],"little":[32],"is":[33],"known":[34],"about":[35],"how":[36,188],"these":[37],"black-box":[38,158],"work.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46,77,98,145],"a":[48,100],"combination":[49],"Gaussian":[51],"Process":[52],"Boosting":[53],"(GPBoost)":[54],"and":[55,91,108,141,155,173],"SHapley":[56],"Additive":[57],"exPlanations":[58],"(SHAP)":[59],"predict":[61],"fatigue":[63,102,182],"with":[64,111,129],"explanations.":[65],"First,":[66],"order":[68],"obtain":[70],"ground":[72],"truth":[73],"used":[78],"PERCLOS":[79],"(percentage":[80],"eyelid":[82],"closure":[83],"over":[84,87],"pupil":[86],"time)":[88],"between":[89],"0":[90],"100":[92],"as":[93,197],"response":[95],"variable.":[96],"Second,":[97],"built":[99],"regression":[103],"model":[104,122,160,184],"using":[105],"both":[106],"physiological":[107],"behavioral":[109],"measures":[110],"GPBoost":[112,159],"was":[114],"able":[115],"address":[117],"within-subjects":[119],"correlations.":[120],"This":[121],"outperformed":[123],"other":[124],"selected":[125],"root-mean-squared":[130],"error":[131,137],"(RMSE)":[132],"=":[133,139],"2.965,":[134],"mean":[135],"absolute":[136],"(MAE)":[138],"1.407,":[140],"adjusted":[142],"R2=0.996.":[143],"Third,":[144],"employed":[146],"SHAP":[147],"identify":[149],"most":[151,168],"important":[152,169],"predictor":[153,170],"variables":[154,171],"uncovered":[156],"by":[161],"showing":[162],"main":[164],"effects":[165],"globally":[172],"explaining":[174],"individual":[175],"predictions":[176],"locally.":[177],"Such":[178],"an":[179],"explainable":[180],"offered":[185],"insights":[186],"into":[187],"intervene":[190],"when":[194],"necessary,":[195],"such":[196],"during":[198],"takeover":[200],"transition":[201],"period":[202],"from":[203]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
