{"id":"https://openalex.org/W2741746516","doi":"https://doi.org/10.1109/ivs.2017.7995827","title":"Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model","display_name":"Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2741746516","doi":"https://doi.org/10.1109/ivs.2017.7995827","mag":"2741746516"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2017.7995827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5048451997","display_name":"Junbo Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junbo Jing","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015550681","display_name":"Dimitar Filev","orcid":"https://orcid.org/0000-0001-7127-6782"},"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":"Dimitar Filev","raw_affiliation_strings":["Ford Motor Company, Dearborn, Michigan, USA"],"affiliations":[{"raw_affiliation_string":"Ford Motor Company, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113818322","display_name":"Arda Kurt","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arda Kurt","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068139498","display_name":"Engin \u00d6zatay","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":"Engin Ozatay","raw_affiliation_strings":["Ford Motor Company, Dearborn, Michigan, USA"],"affiliations":[{"raw_affiliation_string":"Ford Motor Company, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083447907","display_name":"John Michelini","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":"John Michelini","raw_affiliation_strings":["Ford Motor Company, Dearborn, Michigan, USA"],"affiliations":[{"raw_affiliation_string":"Ford Motor Company, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I1292974536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024559779","display_name":"\u00dcmi\u0307t \u00d6zg\u00fcner","orcid":"https://orcid.org/0000-0003-2241-7547"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Umit Ozguner","raw_affiliation_strings":["Ohio State University, Columbus, OH, US"],"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH, US","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5048451997"],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":2.8446,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.91350744,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"881","last_page":"886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9997000098228455,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.9959999918937683,"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/T10117","display_name":"Advanced Combustion Engine Technologies","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1507","display_name":"Fluid Flow and Transfer Processes"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.7590497732162476},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.679011344909668},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.6573633551597595},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.53480064868927},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4975135624408722},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4708161950111389},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.45275142788887024},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43849191069602966},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.43607765436172485},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4307108223438263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3737119734287262},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3482822775840759},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16416165232658386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1498827040195465},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1125846803188324}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.7590497732162476},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.679011344909668},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.6573633551597595},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.53480064868927},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4975135624408722},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4708161950111389},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.45275142788887024},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43849191069602966},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.43607765436172485},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4307108223438263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3737119734287262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3482822775840759},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16416165232658386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1498827040195465},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1125846803188324},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2017.7995827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995827","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306108","display_name":"U.S. Department of Transportation","ror":"https://ror.org/02xfw2e90"},{"id":"https://openalex.org/F4320307103","display_name":"Ford Motor Company","ror":"https://ror.org/00g2tkw06"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W808427616","https://openalex.org/W1506055530","https://openalex.org/W1582388925","https://openalex.org/W1585722679","https://openalex.org/W1949920343","https://openalex.org/W1978917517","https://openalex.org/W1998852372","https://openalex.org/W2001600328","https://openalex.org/W2003068901","https://openalex.org/W2005103999","https://openalex.org/W2020244582","https://openalex.org/W2038190934","https://openalex.org/W2047111123","https://openalex.org/W2090715271","https://openalex.org/W2109764844","https://openalex.org/W2113076747","https://openalex.org/W2155513194","https://openalex.org/W2570817651","https://openalex.org/W2590337499","https://openalex.org/W2612062588","https://openalex.org/W4253800125"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2546021431","https://openalex.org/W2581127593","https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2379651310","https://openalex.org/W2082284720","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697"],"abstract_inverted_index":{"Vehicle":[0],"speed":[1,26,36,43,85,126],"prediction":[2,103,127],"can":[3],"benefit":[4],"a":[5,20,149,165],"wide":[6],"range":[7],"of":[8,35,54,60,158],"vehicle":[9,23,69,84],"control":[10],"designs,":[11],"especially":[12],"for":[13,29],"fuel":[14],"economy":[15],"applications.":[16],"This":[17],"paper":[18],"shows":[19],"computationally":[21],"light":[22],"short":[24,87],"term":[25,88],"predictor":[27,40,145],"designed":[28],"on-board":[30],"implementation,":[31],"using":[32],"minimal":[33],"information":[34],"measurement":[37],"only.":[38],"The":[39,76,143],"generalizes":[41],"historical":[42],"data's":[44,86],"underlying":[45],"pattern":[46],"and":[47,74,90,116,155],"predicts":[48],"from":[49,130],"probability":[50],"aspect.":[51],"One":[52],"novelty":[53],"the":[55,58,65,92,102,110,131,156,159],"method":[56,77],"is":[57,128,146,162],"usage":[59],"fuzzy":[61,99,114,139],"modeling":[62],"to":[63,82,109],"eliminate":[64],"resolution":[66],"limitation":[67],"in":[68],"acceleration":[70,96,105,118],"state":[71,140],"definition,":[72],"classification,":[73],"prediction.":[75],"uses":[78],"Auto-regressive":[79],"(AR)":[80],"model":[81],"capture":[83],"dynamics,":[89],"classifies":[91],"data":[93],"into":[94],"multiple":[95],"states":[97,112,119],"by":[98,113,122,138,164],"membership.":[100],"In":[101],"process,":[104],"measurements":[106],"are":[107,120,136],"mapped":[108],"Markov":[111,123],"encoding,":[115],"future":[117],"predicted":[121],"transition.":[124],"Deterministic":[125],"calculated":[129],"trained":[132],"AR":[133],"models,":[134],"which":[135],"selected":[137],"membership":[141],"similarity.":[142],"developed":[144],"tested":[147],"with":[148],"vehicle's":[150],"real":[151],"urban":[152],"driving":[153],"data,":[154],"effectiveness":[157],"incorporated":[160],"techniques":[161],"verified":[163],"comparison":[166],"study.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
