| Project/Area Number |
23656180
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
NAGAI Masao 東京農工大学, 大学院・工学研究院, 教授 (10111634)
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| Co-Investigator(Renkei-kenkyūsha) |
PONGSATHORN Raksincharoensak 東京農工大学, 大学院・工学研究院, 准教授 (30397012)
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| Project Period (FY) |
2011 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2011: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | ドライバモデル / 予防安全研究 / 個別適合 / 運転行動分析 / 人間機械ｼｽﾃﾑ / 予防安全 / 自動車工学 / ｼﾐｭﾚｰｼｮﾝ工学 / モデル化 |
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| Research Abstract |
This research proposes a theoretical method of by driver-vehicle system model parameter analysis and driving operational behavior analysis especially in drowsiness driving state and inattention states. This research potentially contributes to the further reduction of rear-end collision accidents which accounts for 30% of all accidents in Japan. The following research issues were conducted during the research term:
(1) The method to extract driving behavior parameters which relate to the velocity control based on experiments using Driving Simulator and a test car.(2) Public expressway driving data collection between Chofu city and Kawaguchiko town using a number of subject drivers.(3) Drowsiness driving behavior analysis by comparing the driving data with the driver model.(4) Verification of the effectiveness of the drowsiness detection method by comparing with the subjective evaluation results using driver face camera image.The velocity-control driver model used in this study is expressed as a spring-mass-damper system based on risk potential theory.
Furthermore, by including the lateral-control driver model, the higher accuracy of drowsiness driving detection can be obtained. The achievement from this researchis expected to be applied to active safety systems to prevent accidents.
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