{"id":"https://openalex.org/W4285813839","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824617","title":"Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving","display_name":"Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving","publication_year":2022,"publication_date":"2022-05-30","ids":{"openalex":"https://openalex.org/W4285813839","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824617"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc55113.2022.9824617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824617","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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/A5004958526","display_name":"Gaith Rjoub","orcid":"https://orcid.org/0000-0002-7282-0687"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Gaith Rjoub","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE) Concordia University,Montreal,Canada","Concordia Institute for Information Systems Engineering (CIISE) Concordia University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE) Concordia University,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE) Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020978568","display_name":"Jamal Bentahar","orcid":"https://orcid.org/0000-0002-3136-4849"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jamal Bentahar","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE) Concordia University,Montreal,Canada","Concordia Institute for Information Systems Engineering (CIISE) Concordia University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE) Concordia University,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE) Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069036268","display_name":"Omar Abdel Wahab","orcid":"https://orcid.org/0000-0002-3991-4673"},"institutions":[{"id":"https://openalex.org/I319074055","display_name":"C\u00e9gep de l'Outaouais","ror":"https://ror.org/05rxd1q58","country_code":"CA","type":"education","lineage":["https://openalex.org/I319074055"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Omar Abdel Wahab","raw_affiliation_strings":["Universit&#x00E9; du Qu&#x00E9;bec en Outaouais,Department of Computer Science and Engineering,Gatineau,Canada"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; du Qu&#x00E9;bec en Outaouais,Department of Computer Science and Engineering,Gatineau,Canada","institution_ids":["https://openalex.org/I319074055"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004958526"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":3.1471,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.93253127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"318","last_page":"323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9948999881744385,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9948999881744385,"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":0.9918000102043152,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8885025978088379},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7883014678955078},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7677444219589233},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7398055791854858},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5962985754013062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5015182495117188},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4962514042854309},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49190330505371094},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4887840449810028},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4244592785835266},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3549916744232178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32772696018218994},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08982089161872864},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.08168870210647583}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8885025978088379},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883014678955078},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7677444219589233},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7398055791854858},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5962985754013062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5015182495117188},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4962514042854309},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49190330505371094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4887840449810028},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4244592785835266},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3549916744232178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32772696018218994},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08982089161872864},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08168870210647583},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc55113.2022.9824617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824617","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2884877436","https://openalex.org/W2946231253","https://openalex.org/W2989855190","https://openalex.org/W2999615587","https://openalex.org/W3001889225","https://openalex.org/W3003454091","https://openalex.org/W3014259914","https://openalex.org/W3016772285","https://openalex.org/W3033036004","https://openalex.org/W3035383473","https://openalex.org/W3037121310","https://openalex.org/W3046162336","https://openalex.org/W3099569940","https://openalex.org/W3110306513","https://openalex.org/W3112177083","https://openalex.org/W3124080842","https://openalex.org/W3127561923","https://openalex.org/W3134843574","https://openalex.org/W3136170095","https://openalex.org/W3164329692","https://openalex.org/W3165843660","https://openalex.org/W3195798528","https://openalex.org/W3198804470","https://openalex.org/W3199788090","https://openalex.org/W3211199052","https://openalex.org/W3214722986","https://openalex.org/W4214891036","https://openalex.org/W4220789466","https://openalex.org/W4223925960","https://openalex.org/W4288357622","https://openalex.org/W6762503580","https://openalex.org/W6773319185","https://openalex.org/W6791654091","https://openalex.org/W6795522205"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"Recently,":[0],"the":[1,9,16,87,90,111,116,133,143,170,177,190,200,207,214,228],"concept":[2],"of":[3,11,18,48,89,115,192],"autonomous":[4],"driving":[5],"became":[6],"prevalent":[7],"in":[8,34,149,160,176,213],"domain":[10],"intelligent":[12],"transportation":[13],"due":[14],"to":[15,37,109,141,168,188,199,226],"promises":[17],"increased":[19],"safety,":[20],"traffic":[21],"efficiency,":[22],"fuel":[23],"economy":[24],"and":[25,44,66,73,83,113,145,250],"reduced":[26],"travel":[27],"time.":[28],"Numerous":[29],"studies":[30],"have":[31],"been":[32],"conducted":[33],"this":[35,161,182],"area":[36],"help":[38,129],"newcomer":[39,120,126],"vehicles":[40],"plan":[41],"their":[42],"trajectory":[43,53,117,131,144],"velocity.":[45],"However,":[46],"most":[47],"these":[49,78,95],"proposals":[50],"only":[51],"consider":[52],"planning":[54],"using":[55,232],"conjunction":[56],"with":[57],"a":[58,125,137,150,217,233],"limited":[59],"data":[60],"set":[61],"(i.e.,":[62,246],"metropolis":[63],"areas,":[64],"highways,":[65],"residential":[67],"areas)":[68],"or":[69],"assume":[70],"fully":[71],"connected":[72],"automated":[74],"vehicle":[75,198],"environment.":[76],"Moreover,":[77],"approaches":[79],"are":[80],"not":[81],"explainable":[82],"lack":[84],"trust":[85,208],"regarding":[86],"contributions":[88],"participating":[91,155],"vehicles.":[92],"To":[93],"tackle":[94],"problems,":[96],"we":[97],"design":[98],"an":[99],"Explainable":[100],"Artificial":[101],"Intelligence":[102],"(XAI)":[103],"Federated":[104],"Deep":[105,247],"Reinforcement":[106],"Learning":[107],"model":[108,148,222],"improve":[110],"effectiveness":[112],"trustworthiness":[114],"decisions":[118],"for":[119,130,210],"Autonomous":[121],"Vehicles":[122],"(AVs).":[123],"When":[124],"AV":[127,212],"seeks":[128],"planning,":[132],"edge":[134],"server":[135],"launches":[136],"federated":[138,178],"learning":[139,179,221],"process":[140],"train":[142],"velocity":[146],"prediction":[147],"distributed":[151],"collaborative":[152],"fashion":[153],"among":[154],"AVs.":[156],"One":[157],"essential":[158],"challenge":[159],"approach":[162],"is":[163,185,223],"AVs":[164,172],"selection,":[165],"i.e.,":[166],"how":[167],"select":[169],"appropriate":[171],"that":[173,237],"should":[174],"participate":[175],"process.":[180],"For":[181],"purpose,":[183],"XAI":[184],"first":[186],"used":[187],"compute":[189,206],"contribution":[191],"each":[193,197,211],"feature":[194],"contributed":[195],"by":[196],"overall":[201],"solution.":[202],"This":[203],"helps":[204],"us":[205],"value":[209],"model.":[215],"Then,":[216],"trust-based":[218],"deep":[219],"reinforcement":[220],"put":[224],"forward":[225],"make":[227],"selection":[229],"decisions.":[230],"Experiments":[231],"real-life":[234],"dataset":[235],"show":[236],"our":[238],"solution":[239],"achieves":[240],"better":[241],"performance":[242],"than":[243],"benchmark":[244],"solutions":[245],"Q-Network":[248],"(DQN),":[249],"Random":[251],"Selection":[252],"(RS)).":[253]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":9}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
