{"_buckets": {"deposit": "53312ef8-5f32-4265-b20c-5f523cd8629d"}, "_deposit": {"id": "21704", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21704"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021704", "sets": ["314"]}, "author_link": ["64345", "64346", "64347", "64348", "64349", "64350", "64351", "64352", "64353", "64354", "64355", "64356"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Prediction of driving behavior using driver\u0027s gaze information", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2011-05-12", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "48", "bibliographicPageEnd": "110", "bibliographicPageStart": "105", "bibliographicVolumeNumber": "111", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. PRMU, パターン認識・メディア理解", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "近年,運転行動予測に基づく安全運転支援が強く求められている.一般的にドライバは,認知・判断・操作の手順を踏み自動車を運転する.ドライバは主に視覚からの情報により外界を認知するため,認知の段階に深く関わっている視線情報は,車両情報に先行して情報が得られる.そこで本研究では,ドライバの視線情報を利用し,運転行動を予測する手法を提案する.予測対象とした運転行動は,左折,右折,左車線変更,右車線変更,信号直進,信号停止の6種類である.提案手法は,学習段階と予測段階の2段階に分けられる.学習段階では,各運転行動が起きる前の視線情報から予測に利用できる特徴を抽出し,事前に学習を行う.そして,予測対象である視線情報から,学習段階と同様にして特徴を抽出し,各特徴を学習したSVMにより運転行動予測を行う.実際に一般道を走行して取得した視線データを用いて評価実験を行い,提案手法の有効性を確認した.", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In recent years, driving assistance systems based on the prediction of driving behavior are becoming important for safe driving. A driver typically drives a vehicle following the procedure of recognition, decision and operation. Because a driver mainly recognizes the outside world from visual information, the gaze information will reflect the driver\u0027s behavior earlier than the information obtained from the vehicle. Therefore, we propose a method of predicting a driving behavior using the driver\u0027s gaze information. This method tries to predict six behaviors: left turn, right turn, lane change from right to left, lane change from left to right, going straight at a traffic intersection and stopping for a red light. The proposed method consists of two phases, namely, learning phase and predicting phase. In the learning phase, the method extracts features from gaze information and constructs a SVM classifier. Then, the method extracts the features from gaze information during driving, and predict the driving behavior using the constructed classifier. We evaluated the method with the gaze information obtained on an open road, and we confirmed its effectiveness. 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{"subitem_subject": "視線情報", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Prediction of driving behavior", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Gaze information", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "ドライバの視線情報を利用した運転行動予測(一般セッション,医用画像処理分野における計測・認識・理解)", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "ドライバの視線情報を利用した運転行動予測(一般セッション,医用画像処理分野における計測・認識・理解)", "subitem_title_language": "ja"}]}, "item_type_id": "10", "owner": "1", "path": ["314"], "permalink_uri": "http://hdl.handle.net/2237/23853", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2016-03-16"}, "publish_date": "2016-03-16", "publish_status": "0", "recid": "21704", 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