  In this study, we propose a novel approach to enrich the training data for
automated driving by using a self-designed driving simulator and two human
drivers to generate safety-critical corner cases in a short period of time, as
already presented in~\cite{kowol22simulator}. Our results show that
incorporating these corner cases during training improves the recognition of
corner cases during testing, even though, they were recorded due to visual
impairment. Using the corner case triggering pipeline developed in the previous
work, we investigate the effectiveness of using expert models to overcome the
domain gap due to different weather conditions and times of day, compared to a
universal model from a development perspective. Our study reveals that expert
models can provide significant benefits in terms of performance and efficiency,
and can reduce the time and effort required for model training. Our results
contribute to the progress of automated driving, providing a pathway for safer
and more reliable autonomous vehicles on the road in the future.
