Extraction of Potentially Hazardous Situations based on Inducement of Risky Behavior: Development of Decision Support System for Assessment of the Situation
| Project/Area Number |
25330303
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent robotics
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| Research Institution | Chiba University |
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Principal Investigator |
ARAI Sachiyo 千葉大学, 工学(系)研究科(研究院), 教授 (10372575)
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| Co-Investigator(Kenkyū-buntansha) |
SUZUKI Hironori 日本工業大学, 工学部, 教授 (20426258)
MARUYAMA Yoshihisa 千葉大学, 大学院工学研究科, 准教授 (70397024)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2013: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | マルチエージェントシミュレーション / 意思決定 / 強化学習 / 認知モデル / パターン分類 / ベイジャンネットワーク / エージェントベースシミュレーション / 自転車運転特性 / 特徴抽出 / 認知エラー / ヒヤリ・ハット |
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| Outline of Final Research Achievements |
In recent years, traffic accident of bicycle against vehicle accounts for more than 85 percent of the whole accidents. For such occasions, it is important to specify where and why accident will be happened. However, in the present circumstances, it depends on the experience without utilizing evidential data of driver’s operation.
Therefore, our study firstly analyzed logging data of human’s which were collected by using a bicycle simulator made by Honda Motor Co., Ltd., and classified them into some typical patterns. Secondly, using these patterns, we construct the Bayesian network to represent cause-and-effect of accident to implement into the multiagent simulator MATES, which can realize a mixed traffic flow, and reproduce the bicycle-caused accident scenarios. The main contribution of this study is that our constructed system can detect the unexpected accident pattern and find the causal relation via observing long period of their operation, before and after the accident.
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Report
(4 results)
Research Products
(28 results)
