{"id":"https://openalex.org/W4210865858","doi":"https://doi.org/10.1109/tvt.2022.3148972","title":"Game-Theoretic Lane-Changing Decision Making and Payoff Learning for Autonomous Vehicles","display_name":"Game-Theoretic Lane-Changing Decision Making and Payoff Learning for Autonomous Vehicles","publication_year":2022,"publication_date":"2022-02-07","ids":{"openalex":"https://openalex.org/W4210865858","doi":"https://doi.org/10.1109/tvt.2022.3148972"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2022.3148972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3148972","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5059736491","display_name":"Victor G. Lopez","orcid":"https://orcid.org/0000-0003-3989-4091"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Victor Lopez","raw_affiliation_strings":["UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA","Leibniz University Hanover, Institute of Automatic Control, Hanover, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3989-4091","affiliations":[{"raw_affiliation_string":"UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA","institution_ids":[]},{"raw_affiliation_string":"Leibniz University Hanover, Institute of Automatic Control, Hanover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016137188","display_name":"Frank L. Lewis","orcid":"https://orcid.org/0000-0003-4074-1615"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frank Lewis","raw_affiliation_strings":["UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-4074-1615","affiliations":[{"raw_affiliation_string":"UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059355181","display_name":"Mushuang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mushuang Liu","raw_affiliation_strings":["Electrical Engineering Department, University of Texas, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-2678-4678","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of Texas, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029213376","display_name":"Yan Wan","orcid":"https://orcid.org/0000-0002-7508-5833"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Wan","raw_affiliation_strings":["Electrical Engineering Department, University of Texas, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-7508-5833","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of Texas, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061604841","display_name":"Subramanya Nageshrao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subramanya Nageshrao","raw_affiliation_strings":["Ford Greenfield Labs, Palo Alto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ford Greenfield Labs, Palo Alto, CA, USA","institution_ids":[]}]},{"author_position":"last","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 Research and Innovation Center, Dearborn, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-7127-6782","affiliations":[{"raw_affiliation_string":"Ford Research and Innovation Center, Dearborn, MI, USA","institution_ids":["https://openalex.org/I1292974536"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.7515,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.98538033,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"71","issue":"4","first_page":"3609","last_page":"3620"},"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":0.9988999962806702,"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":0.984000027179718,"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/stochastic-game","display_name":"Stochastic game","score":0.7655811905860901},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7608282566070557},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.6281554698944092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5628946423530579},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5204210877418518},{"id":"https://openalex.org/keywords/fictitious-play","display_name":"Fictitious play","score":0.5172187089920044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5066395401954651},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.5053372979164124},{"id":"https://openalex.org/keywords/potential-game","display_name":"Potential game","score":0.5004642009735107},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4887552261352539},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.4501863420009613},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.4404147267341614},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.430378794670105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24171361327171326},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.18993332982063293},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10913801193237305}],"concepts":[{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.7655811905860901},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7608282566070557},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.6281554698944092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5628946423530579},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5204210877418518},{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.5172187089920044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5066395401954651},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.5053372979164124},{"id":"https://openalex.org/C2778079155","wikidata":"https://www.wikidata.org/wiki/Q288500","display_name":"Potential game","level":3,"score":0.5004642009735107},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4887552261352539},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.4501863420009613},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.4404147267341614},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.430378794670105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24171361327171326},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.18993332982063293},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10913801193237305},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2022.3148972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2022.3148972","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1544016679","https://openalex.org/W1592601589","https://openalex.org/W1833552892","https://openalex.org/W1981864420","https://openalex.org/W1998622835","https://openalex.org/W2011507340","https://openalex.org/W2058989303","https://openalex.org/W2061437468","https://openalex.org/W2067175469","https://openalex.org/W2091265430","https://openalex.org/W2100146203","https://openalex.org/W2120846115","https://openalex.org/W2124770562","https://openalex.org/W2485934471","https://openalex.org/W2580211036","https://openalex.org/W2896524782","https://openalex.org/W2897007254","https://openalex.org/W2910320401","https://openalex.org/W2917421989","https://openalex.org/W2963898834","https://openalex.org/W2972723147","https://openalex.org/W2990474833","https://openalex.org/W2991448601","https://openalex.org/W3022042946","https://openalex.org/W3089594962","https://openalex.org/W3092477836","https://openalex.org/W3105200428","https://openalex.org/W3126661293","https://openalex.org/W3127561923","https://openalex.org/W3140277255","https://openalex.org/W3149715667","https://openalex.org/W3154085103","https://openalex.org/W3163701808","https://openalex.org/W3165519255","https://openalex.org/W4252284432","https://openalex.org/W6678168664","https://openalex.org/W6776859178"],"related_works":["https://openalex.org/W4287644830","https://openalex.org/W3092557414","https://openalex.org/W4226086326","https://openalex.org/W4302081789","https://openalex.org/W3175268978","https://openalex.org/W4287266326","https://openalex.org/W3119014777","https://openalex.org/W2122010238","https://openalex.org/W3212428133","https://openalex.org/W4301668927"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"the":[3,29,33,53,81,99,105],"problem":[4],"of":[5,24,52,62,67,104],"decision":[6],"making":[7],"for":[8,22,32,46],"autonomous":[9],"vehicles":[10],"changing":[11],"lanes":[12],"is":[13,96,112],"addressed":[14],"by":[15],"formulating":[16],"multiple":[17],"games":[18,54],"in":[19,98,108],"normal":[20],"form":[21],"pairs":[23],"agents.":[25,48],"This":[26],"formulation":[27],"generates":[28],"optimal":[30,78],"action":[31],"Ego":[34],"vehicle":[35],"at":[36],"a":[37,59,109],"given":[38],"state":[39],"and":[40,89],"does":[41],"not":[42],"consider":[43],"global":[44],"optimality":[45],"all":[47],"The":[49,64,102],"payoff":[50],"matrices":[51],"are":[55,70],"designed":[56],"based":[57],"on":[58],"user-defined":[60],"set":[61],"rules.":[63],"constant":[65],"parameters":[66],"these":[68],"payoffs":[69],"then":[71],"adjusted":[72],"using":[73],"neural":[74],"learning":[75,88],"to":[76],"generate":[77],"behavior":[79],"among":[80],"vehicles.":[82],"An":[83],"algorithm":[84],"integrating":[85],"deep":[86],"reinforcement":[87],"game":[90],"theory,":[91],"regarded":[92],"as":[93],"Nash":[94],"Q-learning,":[95],"included":[97],"decision-making":[100],"scheme.":[101],"applicability":[103],"proposed":[106],"method":[107],"lane-changing":[110],"scenario":[111],"tested":[113],"via":[114],"simulation.":[115]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":5}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}