The progressive development toward highly automated driving poses major challenges for the release and validation process in the automotive industry, because the immense number of test kilometers that have to be covered with the vehicle cannot be tackled to any extent with established test methods, which are highly focused on the real vehicle. For this reason, new methodologies are required. Simulation-based testing and, in particular, virtual driving tests will play an important role in this context. A basic prerequisite for achieving a significant reduction in the test effort with the real vehicle through these simulations are realistic test scenarios. For this reason, this article presents a novel approach for generating relevant traffic situations based on a traffic flow simulation in SUMO and a vehicle dynamics simulation in CarMaker. The procedure is shown schematically for an emergency braking function. A driving function under test faces the major challenges when the other road users commit driving errors. Therefore, the driving behavior models in this traffic flow simulation are modified in such a way that critical scenarios can arise because of these driving errors. In order to be able to make a statement about the correct behavior of the driving function under test in these traffic situations, objective criteria are necessary to evaluate the triggering behavior and the handling of the traffic situations. Based on the performance evaluation of the driving function under test, characteristic test scenarios are then identified that evenly cover the test space. The comparison of the deviations in covering this test space with full and the reduced dataset is small except in areas where there are no scenarios in both datasets. Finally, these selected scenarios are used to perform an application of the driving function under test. The procedure is exemplified for the triggering time and the maximum deceleration of an emergency braking function. When comparing the distributions, it is shown that the performance in both datasets improves in the same way when parameters are optimized. For example, the mean performance of the driving function increases by more than 0.3 in each case when optimizing the triggering time. Thus, it is no longer necessary to use all scenarios for parameterization in virtual driving tests.