  Several scenario-based frameworks exist to aid in vehicle system development
and safety assurance. However, there is a need for approaches that combine
different types of datasets that offer varying levels of case severity, data
richness, and representativeness. This study presents an integrated
scenario-based analysis approach that encompasses scenario definition, fusion,
parametrization, and test case generation. For this process, ten years of fatal
and non-fatal national crash data from the United States are combined with over
34 million miles of naturalistic driving data. An illustrative example
scenario, "turns at intersection", is chosen to demonstrate this approach.
First, scenario definitions are established from both record-based and
continuous time series data. Second, a frequency analysis is performed to
understand how often events from the same scenario occur at different
severities across datasets. Third, an analysis is performed to show the key
factors relevant to the scenario and the distribution of various parameters.
Finally, a method to combine both types of data into representative test case
scenarios is presented. These techniques improve scenario representativeness in
two major ways: first, they populate an entire spectrum of cases ranging from
routine events to fatal crashes; and second, they provide context-rich,
multi-year data by combining large-scale national and naturalistic datasets.
