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

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，开发了事故因果的故障树模型，该模型使用历史事故数据库来分析车辆安全特征的潜在有效性。第二阶段整合了“100次碰撞”场景（数据驱动的严重事故类型目录）、密歇根州的事故统计数据和安全技术专家评估，以展示15种潜在车辆安全功能的收益和成本权衡分析。这笔资金将用于第三阶段，并根据交通事故发生率调整故障树，将其应用于自动城市州际公路的碰撞场景，并评估车辆安全技术的性能效益，以减少自动公路系统的生命和财产损失。这项研究由美国国家科学基金会/私营部门联合倡议资助，并得到了通用汽车公司高级科学家的持续有力合作。

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.