{"created":"2025-01-19T01:12:26.709829+00:00","updated":"2025-01-19T17:51:40.876650+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211260","sets":["1164:2836:10501:10573"]},"path":["10573"],"owner":"44499","recid":"211260","title":["近隣車情報を用いた強化学習による自動運転制御～報酬付与法の検討と制御挙動の分析～"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-05-20"},"_buckets":{"deposit":"25c6a74d-9a2b-4b8b-8b4c-bf131bea73bb"},"_deposit":{"id":"211260","pid":{"type":"depid","value":"211260","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"近隣車情報を用いた強化学習による自動運転制御～報酬付与法の検討と制御挙動の分析～","author_link":["536403","536404","536405","536406"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"近隣車情報を用いた強化学習による自動運転制御～報酬付与法の検討と制御挙動の分析～"},{"subitem_title":"Autonomous Driving Control by Reinforcement Learning Using Neighboring Vehicle Information―A Study on Reward Design and Behavior Analysis―","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-05-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京都立大学システムデザイン学部"},{"subitem_text_value":"東京工科大学コンピュータサイエンス学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of System Design, Tokyo Metropolitan University","subitem_text_language":"en"},{"subitem_text_value":"School of Computer Science, Tokyo University of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/211260/files/IPSJ-DPS21187014.pdf","label":"IPSJ-DPS21187014.pdf"},"date":[{"dateType":"Available","dateValue":"2023-05-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPS21187014.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"330da521-2902-4495-b56e-8b768b51fe29","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"原田, 智広"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"服部, 聖彦"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomohiro, Harada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kiyohiko, Hattori","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10116224","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8906","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，AI 技術やセンサ性能の向上に伴い，自動運転の研究が盛んに行われている．本研究では，複数車が同時に走行する環境を想定し，車々間での通信によって能動的に取得される近接車情報を考慮した自動運転制御を考える．具体的には，機械学習の一つである強化学習を用いた自動制御の獲得を目指す．本研究では，協調的な自動制御を実現するための報酬付与法の検討と，強化学習によって得られた制御の挙動を分析する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, research on automatic driving has been actively conducted with the improvement of AI technology and sensor performance. In this study, we consider automatic driving control that uses actively acquired information through communication between neighbor vehicles in an environment where multiple vehicles are driving simultaneously. Specifically, we aim to obtain automatic control using deep reinforcement learning, a machine learning method. This study investigates rewarding methods for achieving cooperative automatic control and analyzes the behavior of the control obtained by reinforcement learning.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告マルチメディア通信と分散処理（DPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-05-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"14","bibliographicVolumeNumber":"2021-DPS-187"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":211260,"links":{}}