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.
