{"id":"https://openalex.org/W2886913033","doi":"https://doi.org/10.23919/acc.2018.8431446","title":"A Comparative Study of Data-Driven Human Driver Lateral Control Models","display_name":"A Comparative Study of Data-Driven Human Driver Lateral Control Models","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2886913033","doi":"https://doi.org/10.23919/acc.2018.8431446","mag":"2886913033"},"language":"en","primary_location":{"id":"doi:10.23919/acc.2018.8431446","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2018.8431446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Annual American Control Conference (ACC)","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/A5000307667","display_name":"Kazuhide Okamoto","orcid":"https://orcid.org/0000-0003-4222-8356"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kazuhide Okamoto","raw_affiliation_strings":["School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077667229","display_name":"Panagiotis Tsiotras","orcid":"https://orcid.org/0000-0001-7563-4129"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panagiotis Tsiotras","raw_affiliation_strings":["School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":1.6937,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.8489335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3988","last_page":"3993"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9939000010490417,"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"}},"topics":[{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9939000010490417,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9688000082969666,"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/T10809","display_name":"Occupational Health and Safety Research","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.644040584564209},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.49672752618789673},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45970046520233154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22096514701843262},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08093491196632385}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.644040584564209},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.49672752618789673},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45970046520233154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22096514701843262},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08093491196632385}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/acc.2018.8431446","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2018.8431446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Annual American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307103","display_name":"Ford Motor Company","ror":"https://ror.org/00g2tkw06"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1513032107","https://openalex.org/W1746819321","https://openalex.org/W1969789555","https://openalex.org/W1972441921","https://openalex.org/W1978917517","https://openalex.org/W1986014385","https://openalex.org/W2007354440","https://openalex.org/W2013753693","https://openalex.org/W2058815839","https://openalex.org/W2084008601","https://openalex.org/W2084246939","https://openalex.org/W2140466491","https://openalex.org/W2154543878","https://openalex.org/W2157265953","https://openalex.org/W2162489854","https://openalex.org/W2168175751","https://openalex.org/W2337815039","https://openalex.org/W2587489229","https://openalex.org/W2734463498","https://openalex.org/W2736496279","https://openalex.org/W2765222405","https://openalex.org/W2901136733","https://openalex.org/W4211049957","https://openalex.org/W4300446496","https://openalex.org/W6642990224","https://openalex.org/W6744864927","https://openalex.org/W6756486208"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"To":[0],"reduce":[1],"human":[2,82],"driving":[3],"workload,":[4],"many":[5],"advanced":[6],"driver":[7,19,36,44,57],"assist":[8],"systems":[9],"(ADAS)":[10],"have":[11,52],"been":[12],"developed":[13],"using":[14,87],"a":[15],"single,":[16],"often":[17],"simple,":[18],"model":[20],"to":[21],"predict":[22],"human-driver":[23],"interaction":[24],"in":[25,54,75],"the":[26,62,77],"immediate":[27],"future.":[28],"However,":[29],"each":[30,85],"person":[31],"drives":[32],"differently,":[33],"necessitating":[34],"personalized":[35],"models":[37,51],"based":[38],"on":[39],"data":[40,93],"obtained":[41],"from":[42,61],"actual":[43],"actions.":[45,58],"Yet,":[46],"traditional":[47],"control-theoretic":[48],"and":[49,90,95],"physics-based":[50],"difficulty":[53],"accurately":[55],"predicting":[56,76],"Being":[59],"inspired":[60],"recent":[63],"achievements":[64],"of":[65,81],"machine-learning":[66],"(ML)":[67],"methods,":[68],"this":[69],"work":[70],"compares":[71],"several":[72],"ML-based":[73],"algorithms":[74],"lateral":[78],"control":[79],"actions":[80],"drivers,":[83],"evaluates":[84],"method":[86],"both":[88],"simulated":[89],"real":[91],"human-driving":[92],"sets,":[94],"discusses":[96],"their":[97],"performance.":[98]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}