{"id":"https://openalex.org/W4400644626","doi":"https://doi.org/10.1109/iv55156.2024.10588826","title":"AF-DQN: A Large-Scale Decision-Making Method at Unsignalized Intersections with Safe Action Filter and Efficient Exploratory Training Strategy","display_name":"AF-DQN: A Large-Scale Decision-Making Method at Unsignalized Intersections with Safe Action Filter and Efficient Exploratory Training Strategy","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400644626","doi":"https://doi.org/10.1109/iv55156.2024.10588826"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","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/A5115597574","display_name":"Kaifeng Wang","orcid":"https://orcid.org/0009-0003-4676-2529"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaifeng Wang","raw_affiliation_strings":["Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100666050","display_name":"Qi Liu","orcid":"https://orcid.org/0000-0001-5172-0989"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Liu","raw_affiliation_strings":["Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054063853","display_name":"Xueyuan Li","orcid":"https://orcid.org/0000-0003-1112-5610"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueyuan Li","raw_affiliation_strings":["Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033379854","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0001-7734-5224"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5115597574"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64548095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"337","last_page":"344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9642000198364258,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9642000198364258,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9593999981880188,"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":0.9528999924659729,"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/training","display_name":"Training (meteorology)","score":0.7576236724853516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6637851595878601},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5933529734611511},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5437953472137451},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5185475945472717},{"id":"https://openalex.org/keywords/exploratory-research","display_name":"Exploratory research","score":0.4210284352302551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40161561965942383},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1395198404788971}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.7576236724853516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6637851595878601},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5933529734611511},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5437953472137451},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5185475945472717},{"id":"https://openalex.org/C85973986","wikidata":"https://www.wikidata.org/wiki/Q1091731","display_name":"Exploratory research","level":2,"score":0.4210284352302551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40161561965942383},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1395198404788971},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv55156.2024.10588826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W341403209","https://openalex.org/W1848501634","https://openalex.org/W2145339207","https://openalex.org/W2487175822","https://openalex.org/W2903709398","https://openalex.org/W2989730386","https://openalex.org/W2990380406","https://openalex.org/W3086918446","https://openalex.org/W3114358046","https://openalex.org/W3114647763","https://openalex.org/W3118172322","https://openalex.org/W3119924655","https://openalex.org/W3121095832","https://openalex.org/W3167586364","https://openalex.org/W3183449896","https://openalex.org/W3208122016","https://openalex.org/W3208891370","https://openalex.org/W3209773989","https://openalex.org/W3210614280","https://openalex.org/W4283169335","https://openalex.org/W4285813144","https://openalex.org/W4308079958","https://openalex.org/W6682889407"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2996078371"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1,183],"is":[2,23,78,104,118,158],"an":[3,110,154],"advanced":[4],"field":[5],"that":[6,140,205],"attracts":[7],"significant":[8,75],"attention":[9],"and":[10,39,74,131,151,197,213,230],"engages":[11],"numerous":[12],"researchers.":[13],"However,":[14,58],"relying":[15],"solely":[16],"on":[17,46,54,83],"a":[18,136],"single":[19],"autonomous":[20,40],"vehicle":[21],"(AV)":[22],"insufficient":[24],"to":[25,80,123,160,173,193,232],"meet":[26],"the":[27,35,84,95,162,195,200,209,218,222],"demand":[28],"of":[29,37,86,98,199,227],"future":[30],"transportation":[31],"systems.":[32],"This":[33],"necessitates":[34],"application":[36],"connected":[38],"vehicles":[41],"(CAVs),":[42],"whose":[43],"operation":[44],"relies":[45],"multiagent":[47],"decision-making":[48,85,97],"technology.":[49],"Currently,":[50],"research":[51,82],"primarily":[52],"focuses":[53],"simple":[55,178],"traffic":[56,70,233],"scenarios.":[57,188],"unsignalized":[59,89,102],"intersections":[60,103],"are":[61,191],"frequently":[62],"encountered":[63],"in":[64,106,177,185,225],"rural":[65],"areas,":[66],"characterized":[67],"by":[68],"high":[69],"volume,":[71],"complex":[72,182,187],"interactions,":[73],"risks.":[76],"It":[77],"crucial":[79],"conduct":[81],"CAVs":[87],"at":[88,101],"intersections.":[90],"To":[91],"address":[92],"these":[93],"issues,":[94],"lane-changing":[96,129],"large-scale":[99],"AVs":[100,122],"studied":[105],"this":[107],"paper.":[108],"First,":[109],"action":[111],"filter-based":[112],"deep":[113,164],"Q-network":[114],"method":[115,220,224],"named":[116],"AF-DQN":[117,219],"proposed,":[119],"which":[120],"enables":[121],"effectively":[124],"filter":[125],"out":[126],"potentially":[127],"hazardous":[128],"actions":[130],"execute":[132],"safe":[133],"actions.":[134],"Additionally,":[135],"multi-objective":[137],"reward":[138],"function":[139],"considers":[141],"multiple":[142],"factors":[143],"has":[144],"been":[145],"designed,":[146],"including":[147],"safety,":[148,228],"task":[149],"achievement,":[150],"compliance.":[152],"Moreover,":[153,217],"exploratory":[155,206],"training":[156,207,211,215],"strategy":[157,170],"introduced":[159],"train":[161],"multi-agent":[163],"reinforcement":[165],"learning":[166],"network":[167],"model.":[168],"The":[169],"facilitates":[171],"agents":[172],"learn":[174],"through":[175],"exploration":[176],"scenarios":[179],"before":[180],"solving":[181],"tasks":[184],"more":[186],"Finally,":[189],"experiments":[190],"conducted":[192],"validate":[194],"effectiveness":[196],"superiority":[198],"proposed":[201],"method.":[202],"Results":[203],"show":[204],"accelerates":[208],"model\u2019s":[210],"speed":[212],"improves":[214],"effectiveness.":[216],"outperforms":[221],"baseline":[223],"terms":[226],"efficiency,":[229],"adherence":[231],"rules.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}