{"id":"https://openalex.org/W2124984887","doi":"https://doi.org/10.1109/itsc.2008.4732700","title":"Towards a Driver Model: Preliminary Study of Lane Change Behavior","display_name":"Towards a Driver Model: Preliminary Study of Lane Change Behavior","publication_year":2008,"publication_date":"2008-10-01","ids":{"openalex":"https://openalex.org/W2124984887","doi":"https://doi.org/10.1109/itsc.2008.4732700","mag":"2124984887"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2008.4732700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2008.4732700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 11th International IEEE Conference on Intelligent Transportation Systems","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/A5008172097","display_name":"U. Dogan","orcid":null},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ueruen Dogan","raw_affiliation_strings":["Institut f\u00fc Neuroinformatik, Ruhr Universit\u00e4t Bochum, Bochum, Germany","Inst. fur Neuroinformatik, Ruhr-Univ. Bochum, Bochum"],"affiliations":[{"raw_affiliation_string":"Institut f\u00fc Neuroinformatik, Ruhr Universit\u00e4t Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]},{"raw_affiliation_string":"Inst. fur Neuroinformatik, Ruhr-Univ. Bochum, Bochum","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055318790","display_name":"H. Edelbrunner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hannes Edelbrunner","raw_affiliation_strings":["Technology Center Ruhr, NISYS GmbH, Bochum, Germany","NISYS GmbH, Technol. Center Ruhr, Bochum"],"affiliations":[{"raw_affiliation_string":"Technology Center Ruhr, NISYS GmbH, Bochum, Germany","institution_ids":[]},{"raw_affiliation_string":"NISYS GmbH, Technol. Center Ruhr, Bochum","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084075127","display_name":"Ioannis Iossifidis","orcid":"https://orcid.org/0000-0002-9876-4396"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ioannis Iossifidis","raw_affiliation_strings":["Institut f\u00fc Neuroinformatik, Ruhr Universit\u00e4t Bochum, Bochum, Germany","Inst. fur Neuroinformatik, Ruhr-Univ. Bochum, Bochum"],"affiliations":[{"raw_affiliation_string":"Institut f\u00fc Neuroinformatik, Ruhr Universit\u00e4t Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]},{"raw_affiliation_string":"Inst. fur Neuroinformatik, Ruhr-Univ. Bochum, Bochum","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008172097"],"corresponding_institution_ids":["https://openalex.org/I904495901"],"apc_list":null,"apc_paid":null,"fwci":4.527,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.9403467,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"931","last_page":"937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994999766349792,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.790674090385437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7123502492904663},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6465508341789246},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5260474681854248},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.4827636778354645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4623689651489258},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.4382195472717285},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4197882413864136},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4118083417415619},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3696863651275635},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32142943143844604}],"concepts":[{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.790674090385437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7123502492904663},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6465508341789246},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5260474681854248},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.4827636778354645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4623689651489258},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.4382195472717285},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4197882413864136},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4118083417415619},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3696863651275635},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32142943143844604},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc.2008.4732700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2008.4732700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 11th International IEEE Conference on Intelligent Transportation Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.874.9647","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.874.9647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.researchgate.net/profile/Ioannis_Iossifidis/publication/224364986_Towards_a_Driver_Model_Preliminary_Study_of_Lane_Change_Behavior/links/09e4151065b9d28526000000.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1518969363","https://openalex.org/W1560724230","https://openalex.org/W1602265563","https://openalex.org/W1755734830","https://openalex.org/W1973058638","https://openalex.org/W2002011878","https://openalex.org/W2076944915","https://openalex.org/W2110485445","https://openalex.org/W2111204826","https://openalex.org/W2114528843","https://openalex.org/W2115557648","https://openalex.org/W2119821739","https://openalex.org/W2121553911","https://openalex.org/W2124776405","https://openalex.org/W2131076267","https://openalex.org/W2149748474","https://openalex.org/W2162419813","https://openalex.org/W2163541453","https://openalex.org/W2167615167","https://openalex.org/W2168382721","https://openalex.org/W4239510810","https://openalex.org/W6676391964","https://openalex.org/W6684678765"],"related_works":["https://openalex.org/W2568858292","https://openalex.org/W1515964938","https://openalex.org/W2389381914","https://openalex.org/W2376528221","https://openalex.org/W196800607","https://openalex.org/W2359428812","https://openalex.org/W3181296946","https://openalex.org/W2015705630","https://openalex.org/W2355368334","https://openalex.org/W2073313993"],"abstract_inverted_index":{"The":[0,94,140],"presented":[1],"work":[2],"formulates":[3],"an":[4],"framework":[5,145],"in":[6,28,70,102],"which":[7,151],"early":[8],"prediction":[9,134],"of":[10,23,50,53,116,129,136],"drivers":[11,24],"lane":[12,25,79,132],"change":[13,26,80,133],"behavior":[14,27],"is":[15],"realized.":[16],"We":[17],"aim":[18],"to":[19,30,33,84,90,99],"build":[20],"a":[21,38,42,104,108,114,131,144,155,158],"representation":[22],"order":[29],"recognize":[31],"and":[32,73,88,113,149],"predict":[34],"driver's":[35],"intentions":[36],"as":[37,154],"first":[39],"step":[40],"towards":[41],"realistic":[43],"driver":[44,160],"model.":[45,161],"In":[46,120],"the":[47,51,57,71,85,121,125],"test":[48,123],"bed":[49],"Institute":[52],"Neuroinformatik,":[54],"based":[55],"on":[56],"traffic":[58],"simulator":[59],"NISYS":[60],"TRS":[61],"<sup":[62],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[63],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[64],",":[65],"10":[66],"individuals":[67],"have":[68],"driven":[69],"experiments":[72],"they":[74],"performed":[75],"more":[76],"then":[77],"150":[78],"maneuvers.":[81],"Lane-offset,":[82],"distance":[83],"front":[86],"car":[87],"time":[89,135],"contact,":[91],"were":[92],"recorded.":[93],"acquired":[95],"data":[96],"was":[97,127],"used":[98],"train":[100],"-":[101],"parallel-":[103],"recurrent":[105],"neural":[106,111],"network,":[107],"feed":[109],"forward":[110],"network":[112],"set":[115],"support":[117],"vector":[118],"machines.":[119],"followed":[122],"drives":[124],"system":[126],"able":[128],"performing":[130],"1.5":[137],"sec":[138],"beforehand.":[139],"proposed":[141],"approach":[142],"describes":[143],"for":[146,157],"lane-change":[147],"detection":[148],"prediction,":[150],"will":[152],"serve":[153],"prerequisite":[156],"successful":[159]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}