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The development of assessment methods for the performance of AutomatedVehicles (AVs) is essential to enable the deployment of automated drivingtechnologies, due to the complex operational domain of AVs. One candidate isscenario-based assessment, in which test cases are derived from real-world roadtraffic scenarios obtained from driving data. Because of the high variety ofthe possible scenarios, using only observed scenarios for the assessment is notsufficient. Therefore, methods for generating additional scenarios arenecessary.  Our contribution is twofold. First, we propose a method to determine theparameters that describe the scenarios to a sufficient degree without relyingon strong assumptions on the parameters that characterize the scenarios. Byestimating the probability density function (pdf) of these parameters,realistic parameter values can be generated. Second, we present the ScenarioRepresentativeness (SR) metric based on the Wasserstein distance, whichquantifies to what extent the scenarios with the generated parameter values arerepresentative of real-world scenarios while covering the actual variety foundin the real-world scenarios.  A comparison of our proposed method with methods relying on assumptions ofthe scenario parametrization and pdf estimation shows that the proposed methodcan automatically determine the optimal scenario parametrization and pdfestimation. Furthermore, we demonstrate that our SR metric can be used tochoose the (number of) parameters that best describe a scenario. The presentedmethod is promising, because the parameterization and pdf estimation candirectly be applied to already available importance sampling strategies foraccelerating the evaluation of AVs.

Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles