| Project/Area Number |
18H01663
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 25010:Social systems engineering-related
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| Research Institution | Japan Automobile Research Institute |
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Principal Investigator |
Uchida Nobuyuki 一般財団法人日本自動車研究所, 安全研究部, 研究員 (40426250)
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| Co-Investigator(Kenkyū-buntansha) |
永井 正夫 一般財団法人日本自動車研究所, その他部局等, その他 (10111634)
毛利 宏 東京農工大学, 工学(系)研究科(研究院), 教授 (50585552)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2020: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
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| Keywords | 運転支援 / 交通事故防止 / ヒューマンエラー / 予防安全 / 高度運転支援 / ドライバモデル / 拡張現実 |
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| Outline of Final Research Achievements |
The purpose of this study is to clarify the mechanism of intersection accidents, which account for a large proportion of serious accidents, and to empirically find an appropriate driving support method according to the accident occurrence pattern using an actual driving simulator. In particular, we analyzed the causes of traffic accidents with vulnerable people at urban intersections, which are prone to serious accidents, and clarified scenario patterns in which human errors such as unconfirmed safety are likely to occur. Furthermore, we created a system that reproduces intersection accidents using AR, found specific human errors, and conducted demonstration experiments on effective driving support functions as countermeasures. From the above, we obtained the knowledge about future advanced driving support / autonomous driving necessary to prevent accidents at intersections.
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| Academic Significance and Societal Importance of the Research Achievements |
重大事故に占める割合が高い交差点での車両対自転車事故の発生メカニズムを研究対象とし，ニアミスデータ分析からヒューマンエラーの発生パターンを明らかにした．その上で，ARによって交差点事故を再現するシステムを作成し，ヒューマンエラーパターンの検証と有効な運転支援機能を実証的に見いだした点に学術的かつ社会的意義がある．
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