{"id":"https://openalex.org/W2890824111","doi":"https://doi.org/10.1109/tits.2018.2863939","title":"Aware of Scene Vehicles\u2014Probabilistic Modeling of Car-Following Behaviors in Real-World Traffic","display_name":"Aware of Scene Vehicles\u2014Probabilistic Modeling of Car-Following Behaviors in Real-World Traffic","publication_year":2018,"publication_date":"2018-09-12","ids":{"openalex":"https://openalex.org/W2890824111","doi":"https://doi.org/10.1109/tits.2018.2863939","mag":"2890824111"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2863939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2863939","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","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/A5102997561","display_name":"Donghao Xu","orcid":"https://orcid.org/0000-0003-0568-8740"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Donghao Xu","raw_affiliation_strings":["Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060007938","display_name":"Huijing Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huijing Zhao","raw_affiliation_strings":["Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030400009","display_name":"Franck Guillemard","orcid":null},"institutions":[{"id":"https://openalex.org/I268709337","display_name":"PSA Peugeot Citro\u00ebn (France)","ror":"https://ror.org/04zgvy449","country_code":"FR","type":"company","lineage":["https://openalex.org/I268709337"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Franck Guillemard","raw_affiliation_strings":["Groupe PSA, Velizy, France"],"affiliations":[{"raw_affiliation_string":"Groupe PSA, Velizy, France","institution_ids":["https://openalex.org/I268709337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068084915","display_name":"St\u00e9phane G\u00e9ronimi","orcid":null},"institutions":[{"id":"https://openalex.org/I268709337","display_name":"PSA Peugeot Citro\u00ebn (France)","ror":"https://ror.org/04zgvy449","country_code":"FR","type":"company","lineage":["https://openalex.org/I268709337"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Stephane Geronimi","raw_affiliation_strings":["Groupe PSA, Velizy, France"],"affiliations":[{"raw_affiliation_string":"Groupe PSA, Velizy, France","institution_ids":["https://openalex.org/I268709337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113835152","display_name":"Fran\u00e7ois Aioun","orcid":null},"institutions":[{"id":"https://openalex.org/I268709337","display_name":"PSA Peugeot Citro\u00ebn (France)","ror":"https://ror.org/04zgvy449","country_code":"FR","type":"company","lineage":["https://openalex.org/I268709337"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Francois Aioun","raw_affiliation_strings":["Groupe PSA, Velizy, France"],"affiliations":[{"raw_affiliation_string":"Groupe PSA, Velizy, France","institution_ids":["https://openalex.org/I268709337"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102997561"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.1036,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.78992654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"20","issue":"6","first_page":"2136","last_page":"2148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9991999864578247,"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/T10370","display_name":"Traffic and Road Safety","score":0.9988999962806702,"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/acceleration","display_name":"Acceleration","score":0.6029890775680542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5719893574714661},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5596400499343872},{"id":"https://openalex.org/keywords/distraction","display_name":"Distraction","score":0.5563452243804932},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4623059034347534},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4456932842731476},{"id":"https://openalex.org/keywords/vehicle-dynamics","display_name":"Vehicle dynamics","score":0.4447489380836487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43027007579803467},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.37848132848739624},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3269917368888855},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.25370481610298157},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21761393547058105},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10107859969139099}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.6029890775680542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5719893574714661},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5596400499343872},{"id":"https://openalex.org/C2776378700","wikidata":"https://www.wikidata.org/wiki/Q3030775","display_name":"Distraction","level":2,"score":0.5563452243804932},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4623059034347534},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4456932842731476},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.4447489380836487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43027007579803467},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.37848132848739624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3269917368888855},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25370481610298157},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21761393547058105},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10107859969139099},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2018.2863939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2863939","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G2908900390","display_name":null,"funder_award_id":"61573027","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1964781907","https://openalex.org/W1965455100","https://openalex.org/W1968719911","https://openalex.org/W1972441921","https://openalex.org/W1996878604","https://openalex.org/W2000232169","https://openalex.org/W2013863636","https://openalex.org/W2047834101","https://openalex.org/W2050803817","https://openalex.org/W2062843776","https://openalex.org/W2077148019","https://openalex.org/W2077197253","https://openalex.org/W2079150870","https://openalex.org/W2083651391","https://openalex.org/W2089080831","https://openalex.org/W2091278882","https://openalex.org/W2094039233","https://openalex.org/W2094113849","https://openalex.org/W2110559222","https://openalex.org/W2124298315","https://openalex.org/W2126270075","https://openalex.org/W2143625905","https://openalex.org/W2185149728","https://openalex.org/W2567948266","https://openalex.org/W2754582354","https://openalex.org/W2911964244","https://openalex.org/W3151287925","https://openalex.org/W4249848180","https://openalex.org/W6679987583"],"related_works":["https://openalex.org/W1984342691","https://openalex.org/W2470048815","https://openalex.org/W2324884046","https://openalex.org/W641612223","https://openalex.org/W2028203774","https://openalex.org/W2784113758","https://openalex.org/W2174006685","https://openalex.org/W3175944335","https://openalex.org/W2062829680","https://openalex.org/W2516638161"],"abstract_inverted_index":{"Heterogeneity":[0],"exists":[1],"in":[2,39,46,64,104,151,202],"car":[3],"following":[4],"behaviors":[5,27],"due":[6],"to":[7,41,57,61,145],"the":[8,42,71,101,116,133,141,181,184,191,214],"driver's":[9,67],"habit,":[10],"fatigue,":[11],"distraction,":[12],"or":[13],"surrounding":[14],"traffic.":[15],"This":[16],"research":[17],"proposes":[18],"a":[19,30,50,66,77,85,161,169,203,219],"method":[20],"of":[21,88,160,165,186,222],"modeling":[22,207],"and":[23,70,124,127,172,208,213],"reasoning":[24,209],"heterogeneous":[25,62,210],"car-following":[26,117,211],"based":[28,179],"on":[29,100,168,174,180,218],"stochastic":[31],"system,":[32],"where":[33,106],"scene":[34,166,197],"vehicles":[35,167,198],"are":[36,82,111],"involved":[37],"explicitly":[38,195],"addition":[40],"traditional":[43],"leader-follower":[44],"pair":[45],"characterizing":[47,115],"driving":[48,59,90,96,224],"situations,":[49],"hidden":[51],"variable":[52],"(driver":[53],"state)":[54],"is":[55,74,143,177,190,216],"introduced":[56],"conjugate":[58],"situations":[60],"models":[63],"predicting":[65],"acceleration":[68,139],"control,":[69],"dynamic":[72,78],"procedure":[73,118],"described":[75],"using":[76,84,132],"Bayesian":[79],"network.":[80],"Experiments":[81],"conducted":[83],"large":[86,220],"set":[87,221],"naturalistic":[89,223],"data":[91],"that":[92,194],"were":[93],"collected":[94],"by":[95],"an":[97],"instrumented":[98],"vehicle":[99],"multi-lane":[102],"motorways":[103],"Beijing,":[105],"four":[107],"distinctive":[108],"driver":[109,170],"states":[110],"learnt":[112],"from":[113],"data,":[114],"with":[119,154],"normal,":[120],"slow":[121],"responsive,":[122,126],"strong":[123],"prompt":[125],"unresponsive":[128],"behavioral":[129],"styles.":[130],"By":[131],"proposed":[134],"scene-aware":[135],"multi-state":[136],"model":[137],"for":[138,206],"prediction,":[140],"error":[142],"reduced":[144],"0.19":[146],"m/s":[147,156],"<sup":[148,157],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[149,158],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[150,159],"average":[152],"compared":[153],"0.29":[155],"single-state":[162],"model.":[163],"Influence":[164],"state":[171],"subsequently":[173],"velocity":[175],"control":[176],"verified":[178],"data.":[182,225],"To":[183],"best":[185],"our":[187],"knowledge,":[188],"this":[189],"first":[192],"work":[193],"incorporates":[196],"as":[199],"influential":[200],"factors":[201],"probabilistic":[204],"approach":[205],"behaviors,":[212],"performance":[215],"demonstrated":[217]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}