{"id":"https://openalex.org/W4408611611","doi":"https://doi.org/10.1109/icaiic64266.2025.10920794","title":"Pedestrian Avoidance Simulation by Deep Reinforcement Learning Using Webots","display_name":"Pedestrian Avoidance Simulation by Deep Reinforcement Learning Using Webots","publication_year":2025,"publication_date":"2025-02-18","ids":{"openalex":"https://openalex.org/W4408611611","doi":"https://doi.org/10.1109/icaiic64266.2025.10920794"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic64266.2025.10920794","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic64266.2025.10920794","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5116697878","display_name":"Chalumpol Trararak","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chalumpol Trararak","raw_affiliation_strings":["Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025387024","display_name":"Trong-Thuc Hoang","orcid":"https://orcid.org/0000-0002-4078-0836"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Trong-Thuc Hoang","raw_affiliation_strings":["Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042912660","display_name":"Cong\u2010Kha Pham","orcid":"https://orcid.org/0000-0001-5255-4919"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pham Cong-Kha","raw_affiliation_strings":["Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Univ. of Electro-Communications,Dept. of Computer and Network Eng.,Tokyo,Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5116697878"],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":2.345,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.85971883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"0734","last_page":"0739"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.8772000074386597,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.8772000074386597,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.7785000205039978,"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/T13924","display_name":"Internet of Things and Social Network Interactions","score":0.7512999773025513,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7633053064346313},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6691440343856812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6484684944152832},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.45737576484680176},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.4343462586402893},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41104093194007874},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.39162975549697876},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2507619261741638},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.1917751431465149},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.07669180631637573}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7633053064346313},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6691440343856812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6484684944152832},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.45737576484680176},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.4343462586402893},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41104093194007874},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.39162975549697876},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2507619261741638},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.1917751431465149},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.07669180631637573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic64266.2025.10920794","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic64266.2025.10920794","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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":13,"referenced_works":["https://openalex.org/W2899072036","https://openalex.org/W3031287835","https://openalex.org/W3121052311","https://openalex.org/W3137986185","https://openalex.org/W3182641214","https://openalex.org/W3185091458","https://openalex.org/W3211103826","https://openalex.org/W4280518809","https://openalex.org/W4311237444","https://openalex.org/W4363647548","https://openalex.org/W4390678349","https://openalex.org/W4396955713","https://openalex.org/W4403609280"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W49697837"],"abstract_inverted_index":{"Road":[0],"accidents":[1],"involving":[2],"pedestrians":[3],"remain":[4],"a":[5,27,43,89,107],"leading":[6],"cause":[7],"of":[8,96],"mortality":[9],"worldwide.":[10],"As":[11],"autonomous":[12,50,84,93,115],"driving":[13,116],"technology":[14],"advances":[15],"to":[16,33,66,78,105],"address":[17,88],"this":[18],"issue,":[19],"Deep":[20],"Reinforcement":[21],"Learning":[22],"(DRL)":[23],"has":[24],"emerged":[25],"as":[26],"promising":[28],"approach":[29,109],"for":[30,46,110],"teaching":[31],"vehicles":[32,94],"navigate":[34],"safely,":[35],"particularly":[36],"in":[37,49,70,91,119,122],"avoiding":[38],"pedestrians.":[39],"This":[40,59],"paper":[41],"presents":[42],"DRL":[44,117],"algorithm":[45],"pedestrian":[47,80],"avoidance":[48,81],"four-wheeled":[51],"vehicles,":[52],"simulated":[53,72],"using":[54],"Webots":[55,120],"and":[56,63,87,98],"Deepbots":[57],"framework.":[58],"study":[60,75,102],"employs":[61],"stochastic":[62],"deterministic":[64],"algorithms":[65,118],"train":[67],"the":[68,123],"vehicle":[69,85],"specific":[71],"scenarios.":[73],"The":[74,101],"also":[76,103],"aims":[77,104],"enhance":[79],"capabilities,":[82],"advance":[83],"safety,":[86],"challenge":[90],"fully":[92],"capable":[95],"drivers'":[97],"pedestrians'":[99],"safety.":[100],"provide":[106],"fundamental":[108],"further":[111],"improvement":[112],"on":[113],"safer":[114],"simulators":[121],"future.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}