{"id":"https://openalex.org/W3115649690","doi":"https://doi.org/10.1109/itsc45102.2020.9294244","title":"Traffic Impact Analysis of a Deep Reinforcement Learning-based Multi-lane Freeway Vehicle Control","display_name":"Traffic Impact Analysis of a Deep Reinforcement Learning-based Multi-lane Freeway Vehicle Control","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3115649690","doi":"https://doi.org/10.1109/itsc45102.2020.9294244","mag":"3115649690"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","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/A5061753926","display_name":"Yuta Kataoka","orcid":"https://orcid.org/0000-0001-6144-6477"},"institutions":[{"id":"https://openalex.org/I4210137853","display_name":"Toyota Motor Corporation (Japan)","ror":"https://ror.org/02zqm6r10","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yuta Kataoka","raw_affiliation_strings":["Toyota Motor Corporation, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Motor Corporation, Aichi, Japan","institution_ids":["https://openalex.org/I4210137853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008119418","display_name":"Hao Yang","orcid":"https://orcid.org/0000-0003-0814-6058"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hao Yang","raw_affiliation_strings":["McMaster University, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"McMaster University, Ontario, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038132951","display_name":"Shalini Keshavamurthy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor Corporation (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shalini Keshavamurthy","raw_affiliation_strings":["Toyota Motor North America, California, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Motor North America, California, USA","institution_ids":["https://openalex.org/I4210093665"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044181339","display_name":"Ippei Nishitani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137853","display_name":"Toyota Motor Corporation (Japan)","ror":"https://ror.org/02zqm6r10","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ippei Nishitani","raw_affiliation_strings":["Toyota Motor Corporation, Aichi, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Motor Corporation, Aichi, Japan","institution_ids":["https://openalex.org/I4210137853"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025075066","display_name":"Kentaro Oguchi","orcid":"https://orcid.org/0000-0001-9724-6434"},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor Corporation (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kentaro Oguchi","raw_affiliation_strings":["Toyota Motor North America, California, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Motor North America, California, USA","institution_ids":["https://openalex.org/I4210093665"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061753926"],"corresponding_institution_ids":["https://openalex.org/I4210137853"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18575654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2129","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9901000261306763,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9513000249862671,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.901396632194519},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.6517965197563171},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6450504660606384},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5552765130996704},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.47673049569129944},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.4170213043689728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3380624055862427},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22380533814430237},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10930252075195312}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.901396632194519},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.6517965197563171},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6450504660606384},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5552765130996704},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.47673049569129944},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.4170213043689728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3380624055862427},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22380533814430237},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10930252075195312},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W329322042","https://openalex.org/W2046033161","https://openalex.org/W2132479764","https://openalex.org/W2145339207","https://openalex.org/W2155968351","https://openalex.org/W2201581102","https://openalex.org/W2605096448","https://openalex.org/W2746553466","https://openalex.org/W2886764981","https://openalex.org/W2920836636","https://openalex.org/W2945095794","https://openalex.org/W2951799221","https://openalex.org/W2952890072","https://openalex.org/W2971514685","https://openalex.org/W2989958156","https://openalex.org/W6685444567","https://openalex.org/W6687681856","https://openalex.org/W6735722269","https://openalex.org/W6753670138","https://openalex.org/W6763002283","https://openalex.org/W6767358069"],"related_works":["https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856","https://openalex.org/W2145821588","https://openalex.org/W2086122291","https://openalex.org/W1987513656"],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1,21,55],"is":[2,33],"one":[3,74],"of":[4,23,26,49,73,75],"the":[5,47,71,76,123,135],"methods":[6],"that":[7,95,107,127],"has":[8],"been":[9],"used":[10],"to":[11,83,121],"realize":[12],"optimal":[13],"driving.":[14],"Most":[15],"studies":[16],"have":[17],"focused":[18],"on":[19,56,63,138],"evaluating":[20],"performance":[22],"a":[24,64],"fraction":[25],"vehicles":[27,38,51,80,97,110,132],"controlled":[28,37,52,79,96,131],"by":[29,53],"reinforcement":[30,54],"learning.":[31],"It":[32],"unclear":[34],"how":[35],"these":[36],"influence":[39],"other":[40],"vehicles.":[41,103],"We":[42,92],"conducted":[43],"several":[44],"experiments":[45],"examining":[46],"impact":[48,137],"multiple":[50,109],"traffic":[57,118,140],"flow.":[58,119],"The":[59,78],"simulations":[60],"were":[61,81,111],"performed":[62],"three-lane":[65],"freeway":[66],"with":[67],"lane":[68],"regulation":[69],"at":[70],"end":[72],"lanes.":[77],"trained":[82],"drive":[84,133],"as":[85,87],"fast":[86],"possible":[88],"and":[89],"run":[90,99,112],"non-cooperatively.":[91],"found":[93],"out":[94],"could":[98],"faster":[100],"than":[101],"human-driven":[102],"Moreover,":[104],"we":[105],"anticipated":[106],"if":[108,129],"selfishly,":[113,134],"it":[114],"would":[115,141],"adversely":[116],"affect":[117],"Contrary":[120],"expectations,":[122],"experimental":[124],"results":[125],"showed":[126],"even":[128],"numerous":[130],"negative":[136],"overall":[139],"be":[142],"small.":[143]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
