{"id":"https://openalex.org/W2090894570","doi":"https://doi.org/10.1109/ivs.2012.6232177","title":"An improved driver-behavior model with combined individual and general driving characteristics","display_name":"An improved driver-behavior model with combined individual and general driving characteristics","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2090894570","doi":"https://doi.org/10.1109/ivs.2012.6232177","mag":"2090894570"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2012.6232177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5026807873","display_name":"Pongtep Angkititrakul","orcid":"https://orcid.org/0000-0001-9739-2052"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pongtep Angkititrakul","raw_affiliation_strings":["Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108524583","display_name":"Chiyomi Miyajima","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chiyomi Miyajima","raw_affiliation_strings":["Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042118446","display_name":"Kazuya Takeda","orcid":"https://orcid.org/0000-0002-0330-1787"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuya Takeda","raw_affiliation_strings":["Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Media Science, University of Nagoya, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan","institution_ids":["https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60134161"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"426","last_page":"431"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10524","display_name":"Traffic control and management","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9858999848365784,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9828000068664551,"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":0.6965916156768799},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.654877245426178},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6190879344940186},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.6104879379272461},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6042726039886475},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5016648769378662},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49036192893981934},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.478031724691391},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.33563002943992615},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.32821354269981384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2924472689628601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6965916156768799},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.654877245426178},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6190879344940186},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.6104879379272461},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6042726039886475},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5016648769378662},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49036192893981934},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.478031724691391},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.33563002943992615},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.32821354269981384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2924472689628601},{"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2012.6232177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1972441921","https://openalex.org/W2009083767","https://openalex.org/W2080972498","https://openalex.org/W2083463163","https://openalex.org/W2092399389","https://openalex.org/W2094039233","https://openalex.org/W2112008099","https://openalex.org/W2120636621","https://openalex.org/W2121416338","https://openalex.org/W2128002512","https://openalex.org/W2140466491","https://openalex.org/W2157180464","https://openalex.org/W2170003873","https://openalex.org/W2488678869","https://openalex.org/W3150904112","https://openalex.org/W4248996458","https://openalex.org/W6678007500"],"related_works":["https://openalex.org/W4386716613","https://openalex.org/W4293482264","https://openalex.org/W1614270630","https://openalex.org/W1605972624","https://openalex.org/W1833498382","https://openalex.org/W3194589442","https://openalex.org/W1602151161","https://openalex.org/W1992295166","https://openalex.org/W2972093345","https://openalex.org/W1854524123"],"abstract_inverted_index":{"In":[0,58],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,36,69,74],"stochastic":[6],"driver-behavior":[7,124],"modeling":[8],"framework":[9],"which":[10,40],"takes":[11],"into":[12],"account":[13],"both":[14,87],"individual":[15,26,119],"and":[16],"general":[17,60,109],"driving":[18,27,63,97,110],"characteristics":[19,98],"as":[20],"one":[21],"aggregate":[22,91],"model.":[23],"Patterns":[24],"of":[25,46,53,78,99,114,146],"styles":[28],"are":[29,65],"modeled":[30],"using":[31,73],"Dirichlet":[32],"process":[33],"mixture":[34,71],"model,":[35],"nonparametric":[37],"Bayesian":[38],"approach":[39],"automatically":[41],"selects":[42],"the":[43,90,139,147,151],"optimal":[44],"number":[45],"model":[47,72,93,125,149,153],"components":[48],"to":[49,107,128],"fit":[50],"sparse":[51],"observations":[52],"each":[54,100],"particular":[55,101],"driver's":[56],"behavior.":[57],"addition,":[59],"or":[61],"background":[62],"patterns":[64],"also":[66,104],"captured":[67],"with":[68],"Gaussian":[70],"reasonably":[75],"large":[76],"amount":[77],"development":[79],"observed":[80],"data":[81],"from":[82,118],"several":[83,136],"drivers.":[84],"By":[85],"combining":[86],"probability":[88],"distributions,":[89],"driver-dependent":[92],"can":[94],"better":[95],"emphasize":[96],"driver,":[102],"while":[103],"backing":[105],"off":[106],"exploit":[108],"behavior":[111,131],"in":[112],"cases":[113],"unmatched":[115],"parameter":[116],"spaces":[117],"training":[120],"observations.":[121],"The":[122,141],"proposed":[123],"was":[126],"employed":[127],"anticipate":[129],"pedal-operation":[130],"during":[132],"car-following":[133],"maneuvers":[134],"involving":[135],"drivers":[137],"on":[138],"road.":[140],"experimental":[142],"results":[143],"showed":[144],"advantages":[145],"combined":[148],"over":[150],"adapted":[152],"previously":[154],"proposed.":[155]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
