  The development of assessment methods for the performance of Automated
Vehicles (AVs) is essential to enable the deployment of automated driving
technologies, due to the complex operational domain of AVs. One candidate is
scenario-based assessment, in which test cases are derived from real-world road
traffic scenarios obtained from driving data. Because of the high variety of
the possible scenarios, using only observed scenarios for the assessment is not
sufficient. Therefore, methods for generating additional scenarios are
necessary.
  Our contribution is twofold. First, we propose a method to determine the
parameters that describe the scenarios to a sufficient degree without relying
on strong assumptions on the parameters that characterize the scenarios. By
estimating the probability density function (pdf) of these parameters,
realistic parameter values can be generated. Second, we present the Scenario
Representativeness (SR) metric based on the Wasserstein distance, which
quantifies to what extent the scenarios with the generated parameter values are
representative of real-world scenarios while covering the actual variety found
in the real-world scenarios.
  A comparison of our proposed method with methods relying on assumptions of
the scenario parametrization and pdf estimation shows that the proposed method
can automatically determine the optimal scenario parametrization and pdf
estimation. Furthermore, we demonstrate that our SR metric can be used to
choose the (number of) parameters that best describe a scenario. The presented
method is promising, because the parameterization and pdf estimation can
directly be applied to already available importance sampling strategies for
accelerating the evaluation of AVs.
