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  • Summary & Details

Near-miss Incident Data Classification Relating to Active Safety Measures

ドライブレコーダによるヒヤリハットデータの予防安全対策候補別分類

Detailed Information

Author(J)1) 御室 哲志, 2) 宇野 章則, 3) 宇野 仁, 4) 里見 洋平, 5) 猿渡 英敏, 6) 関口 守, 7) 塚原 俊哉, 8) 三浦 泰彦, 9) 吉田 稔, 10) 茂呂 克己, 11) 鎌田 実, 12) 永井 正夫
Author(E)1) Tetsushi Mimuro, 2) Akinori Uno, 3) Hitoshi Uno, 4) Yohei Satomi, 5) Hidetoshi Saruwatari, 6) Mamoru Sekiguchi, 7) Toshiya Tsukahara, 8) Yasuhiko Miura, 9) Minoru Yoshida, 10) Katsumi Moro, 11) Minoru Kamata, 12) Masao Nagai
Affiliation(J)1) 秋田県立大学, 2) スズキ, 3) 日産自動車, 4) トヨタ自動車, 5) 本田技術研究所, 6) 富士重工業, 7) 三菱自動車工業, 8) マツダ, 9) ダイハツ工業, 10) 自動車技術会, 11) 東京大学大学院, 12) 東京農工大学大学院
Affiliation(E)1) Akita Prefectural University, 2) Suzuki Motor Corporation, 3) Nissan Motor Co. Ltd., 4) Toyota Motor Corporation, 5) Honda R&D Co. Ltd., 6) Fuji Heavy Industries Ltd., 7) Mitsubishi Motors Corporation, 8) Mazda Motor Corporation, 9) Daihatsu Motor Co. Ltd., 10) Society of Automotive Engineers of Japan, 11) University of Tokyo, 12) Tokyo University of Agriculture and Technology
Abstract(J)高ヒヤリ度のヒヤリハットデータ800ケースを使用し,車両予防安全対策候補別に分類を行った.ヒヤリハットデータは,個別イベントにおけるエラー状況を具体的に把握できるため,予防安全装置の作動条件との対応付けが可能である.分類データに見られる主要特徴と,予防安全装置の効果評価適用事例を示す.

Translation

Abstract(E)800 cases of emergent near-miss data collected by incident data recorder were classified relating to vehicle active safety measures. Because the human errors can be observed in each incident, it is able to evaluate the effectiveness of related active safety equipment in reference to the operating conditions. Main features seen in the classified data are introduced as well as some analytical applications on evaluating active safety equipment.

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