{"id":"https://openalex.org/W3045995653","doi":"https://doi.org/10.1109/icc40277.2020.9148831","title":"Reinforcement Learning in V2I Communication Assisted Autonomous Driving","display_name":"Reinforcement Learning in V2I Communication Assisted Autonomous Driving","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3045995653","doi":"https://doi.org/10.1109/icc40277.2020.9148831","mag":"3045995653"},"language":"en","primary_location":{"id":"doi:10.1109/icc40277.2020.9148831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","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/A5100441049","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-0205-9212"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["Queen Mary University of London, London, UK"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London, London, UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076863392","display_name":"Yuanwei Liu","orcid":"https://orcid.org/0000-0002-6389-8941"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuanwei Liu","raw_affiliation_strings":["Queen Mary University of London, London, UK"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London, London, UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029240873","display_name":"Yue Chen","orcid":"https://orcid.org/0000-0003-1070-7192"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yue Chen","raw_affiliation_strings":["Queen Mary University of London, London, UK"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London, London, UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057155774","display_name":"Luhan Wang","orcid":"https://orcid.org/0000-0002-7056-5416"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luhan Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101520824","display_name":"Zhaoming Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoming Lu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100441049"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":0.3082,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56495422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10524","display_name":"Traffic control and management","score":0.9988999962806702,"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":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7973650097846985},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7966964244842529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700452983379364},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5949758291244507},{"id":"https://openalex.org/keywords/selection-algorithm","display_name":"Selection algorithm","score":0.5516749024391174},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5277928709983826},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.5264901518821716},{"id":"https://openalex.org/keywords/base-station","display_name":"Base station","score":0.4639318585395813},{"id":"https://openalex.org/keywords/action-selection","display_name":"Action selection","score":0.42129606008529663},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37064486742019653},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34175199270248413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2611052989959717},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.13470900058746338}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7973650097846985},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7966964244842529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700452983379364},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5949758291244507},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.5516749024391174},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5277928709983826},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.5264901518821716},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.4639318585395813},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.42129606008529663},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37064486742019653},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34175199270248413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2611052989959717},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.13470900058746338},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icc40277.2020.9148831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/349453","is_oa":false,"landing_page_url":"https://hub.hku.hk/handle/10722/349453","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2031575274","https://openalex.org/W2161061923","https://openalex.org/W2219431285","https://openalex.org/W2294480821","https://openalex.org/W2530849036","https://openalex.org/W2746553466","https://openalex.org/W2895847534","https://openalex.org/W2897522585","https://openalex.org/W2901453561","https://openalex.org/W2903261989","https://openalex.org/W2906647810","https://openalex.org/W2920448302","https://openalex.org/W2952736579","https://openalex.org/W2991448601","https://openalex.org/W3005769373","https://openalex.org/W4301501993","https://openalex.org/W6677067356","https://openalex.org/W6756079571","https://openalex.org/W6758033894","https://openalex.org/W6760260384"],"related_works":["https://openalex.org/W2015051472","https://openalex.org/W2168501056","https://openalex.org/W2120009678","https://openalex.org/W2037601570","https://openalex.org/W2912947802","https://openalex.org/W2123856982","https://openalex.org/W2384373317","https://openalex.org/W4311180247","https://openalex.org/W1570809589","https://openalex.org/W2124364062"],"abstract_inverted_index":{"A":[0],"novel":[1],"framework":[2],"is":[3,36,73,133,149],"proposed":[4,37,70,102,122,138],"for":[5,38,98],"enhancing":[6,109],"the":[7,18,43,48,58,64,69,77,85,101,110,121,127,137,146,161],"driving":[8,111,142],"safety":[9,112],"and":[10,45,91,113],"fuel":[11,114,158],"economy":[12,115],"of":[13,20,47,75,80,108,116,151,155,157],"autonomous":[14],"vehicles":[15],"(AVs)":[16],"with":[17],"aid":[19],"vehicle-to-infrastructure":[21],"(V2I)":[22],"communication":[23],"networks.":[24],"To":[25],"solve":[26],"this":[27],"pertinent":[28],"problem,":[29],"a":[30],"double":[31],"deep":[32,66],"Q-network":[33,67],"(DDQN)":[34],"algorithm":[35,72,125,148],"making":[39],"collision-free":[40],"decisions.":[41],"Thus,":[42],"trajectory":[44,103],"velocity":[46],"AV":[49],"are":[50,96,106],"determined":[51],"by":[52,83],"receiving":[53],"real-time":[54],"traffic":[55],"information":[56],"from":[57,145],"base":[59],"stations":[60],"(BSs).":[61],"Compared":[62],"to":[63],"conventional":[65],"algorithm,":[68],"DDQN":[71,123],"capable":[74,107,150],"overcoming":[76],"large":[78],"overestimation":[79],"action":[81,89,92],"values":[82],"decomposing":[84],"max-Q-value":[86],"operation":[87],"into":[88],"selection":[90],"evaluation.":[93],"Numerical":[94],"results":[95],"provided":[97],"demonstrating":[99],"that":[100,120,136],"design":[104],"algorithms":[105],"AVs.":[117],"We":[118],"demonstrate":[119],"based":[124,129,141],"outperforms":[126],"DQN":[128],"algorithm.":[130],"Additionally,":[131],"it":[132],"also":[134],"demonstrated":[135],"fuel-economy":[139],"(FE)":[140],"policy":[143],"derived":[144],"DRL":[147],"achieving":[152],"in":[153],"excess":[154],"24%":[156],"savings":[159],"over":[160],"benchmarks.":[162]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
