{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00224875","sets":["1164:3980:11162:11163"]},"path":["11163"],"owner":"44499","recid":"224875","title":["PERCLOSと車両の操作情報を用いたドライバ眠気推定における推論モデルとデータ分割方法による推論精度の比較"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-01"},"_buckets":{"deposit":"16b00596-0aa6-441d-9ff8-8c19622385f1"},"_deposit":{"id":"224875","pid":{"type":"depid","value":"224875","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"PERCLOSと車両の操作情報を用いたドライバ眠気推定における推論モデルとデータ分割方法による推論精度の比較","author_link":["594019","594020","594021"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"PERCLOSと車両の操作情報を用いたドライバ眠気推定における推論モデルとデータ分割方法による推論精度の比較"}]},"item_type_id":"4","publish_date":"2023-03-01","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学文理学部情報科学科"},{"subitem_text_value":"日本大学文理学部情報科学科"},{"subitem_text_value":"株式会社エイチアイ"}]},"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/224875/files/IPSJ-ITS23092001.pdf","label":"IPSJ-ITS23092001.pdf"},"date":[{"dateType":"Available","dateValue":"2025-03-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ITS23092001.pdf","filesize":[{"value":"837.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"37"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a05c4a94-04d1-41fa-adeb-3977be255dab","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"宮本, 亨紀"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"谷, 聖一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"村上, 雅彦"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11515904","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":"2188-8965","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"日本での交通死亡事故の原因としてドライバの漫然運転が数多く報告されており，要因となるドライバの覚醒水準を認識するシステムの必要性が高まっている．PERCLOS は閉眼時間の割合を表す生体情報であり，眠気推定において有効な特徴量であるという報告が多い．また，測定に関して非拘束でありドライバへの負担が少ないことからも有効な特徴量であると考えられる．以前の研究発表では，試作した簡易なドライビングシミュレータによる高速道路の直線走行を模した走行実験によって得られた PERCLOS と車両の操作情報から，同時に収集したカロリンスカ眠気尺度と北島らの顔表情に基づく眠気尺度による眠気評定値の推論モデル（線形回帰，ニューラルネットワーク）による推論精度の比較を行った．本稿では，推論モデルに LightGBM を加え，訓練データとテストデータの分割方法による推論精度の比較について議論する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告高度交通システムとスマートコミュニティ（ITS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023-ITS-92"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:24:26.121045+00:00","updated":"2025-01-19T12:59:25.972910+00:00","id":224875}