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.
