| Project/Area Number |
06680398
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
社会システム工学
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| Research Institution | Tokyo University of Mercantile Marine |
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Principal Investigator |
SATO Yoshinobu Tokyo University of Mercantile Marine Department of Electronic an Mechanical Engineering Professor, 商船学部・交通電子制御工学講座, 教授 (60242319)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1995: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1994: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | Advanced Safety vehicle / ASV / Hazard-Control System / Safety-Assessment / Risk Analysis / Safety-Related System / A-C Model Structure Function Method / Fail-Safe System / A-Cモデル構造図数法 / 最適システム構成 / 社会的コスト |
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| Research Abstract |
Advanced safety vehicles (ASVs) are supposed to materialize hazard-control systems such as distance-warning systems and emergency-brake systems. These are essentially necessitated to annihilate traffic accidents caused by automobiles. It is important to explore the fail-safe/fail-operable structures of the hazard-control systems in order to optimize the systems.
The following results are obtained by the present research project :
1.The feasibility of fail-safe/fail-operable structures for the hazard-control systems for ASVs is defined.
2.A distance-warning system (DWS) measures the distance between a vehicle and the vehicle in front and gives warning to a driver in order to prevent rear-end collisions. It is estimated based on deductive methodology that the DWS will reduce the chance of rearend collisions by 70% and the emergency-brake system by 85%.
3.An approximating risk analysis method is necessary for complex systems with computers where accurate statistics such as hardware failure rates are not always available. An A-C model function method for risk analysis, which enable us to identify hazards and to estimate accident frequencies without accurate statistics, is proposed. Analyzes of systems involving ASVs demonstrate the effectiveness of the new method.
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