Fault Trees and Crash Experience Data to Evaluate Highway Vehicle-Safety Interventions for an Automated Highway System

用于评估自动化公路系统的公路车辆安全干预措施的故障树和碰撞经验数据

• 批准号: 9515305
• 负责人: Yacov Haimes
• 金额: $ 16万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-04-15 至 1998-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9515305&HistoricalAwards=false
• 关键词: Fault; Trees; Crash; Experience; Data

This research is part of multi-stage program seeking to develop new methods in risk analysis building on multiobjective trade-off analysis of vehicle-safety interventions for evaluating changes in future vehicle design. In Phase I of this effort, fault-tree models for accident causation and consequence were developed that use historical accident databases to analyze the potential effectiveness of vehicle safety features. Phase II integrated the `100 Crashes` scenarios (a data-driven catalog of severe accident types), accident statistics from Michigan, and expert assessment of safety technologies to demonstrate a trade-off analysis of the benefits and costs of 15 prospective vehicle safety features. This funding supports Phase III, and adapts the fault trees for event-occurrence rates that are based on travel exposure, applies them to crash scenarios of automated urban interstates, and assesses the performance benefits of vehicle-safety technologies in order to reduce the losses of lives and property on automated highway systems. This research is funded under the Joint NSF/Private Sector Initiative with the continued strong collaboration of senior scientists from General Motors.

本研究是多阶段计划的一部分，旨在开发基于车辆安全干预措施的多目标权衡分析的风险分析新方法，以评估未来车辆设计的变化。 在第一阶段的工作中，事故原因和后果的故障树模型，使用历史事故数据库来分析车辆安全功能的潜在有效性。 第二阶段综合了“100起事故”情景（严重事故类型的数据驱动目录）、密歇根州的事故统计数据和安全技术的专家评估，以展示对15种潜在车辆安全功能的效益和成本的权衡分析。 这笔资金支持第三阶段，并根据旅行风险调整事件发生率的故障树，将其应用于自动化城市州际公路的碰撞场景，并评估车辆安全技术的性能优势，以减少自动化公路系统的生命和财产损失。 这项研究由NSF/私营部门联合倡议资助，并与通用汽车公司的高级科学家继续密切合作。