  Autonomous vehicles hold great promise in improving the future of
transportation. The driving models used in these vehicles are based on neural
networks, which can be difficult to validate. However, ensuring the safety of
these models is crucial. Traditional field tests can be costly, time-consuming,
and dangerous. To address these issues, scenario-based closed-loop simulations
can simulate many hours of vehicle operation in a shorter amount of time and
allow for specific investigation of important situations. Nonetheless, the
detection of relevant traffic scenarios that also offer substantial testing
benefits remains a significant challenge. To address this need, in this paper
we build an imitation learning based trajectory prediction for traffic
participants. We combine an image-based (CNN) approach to represent spatial
environmental factors and a graph-based (GNN) approach to specifically
represent relations between traffic participants. In our understanding, traffic
scenes that are highly interactive due to the network's significant utilization
of the social component are more pertinent for a validation process. Therefore,
we propose to use the activity of such sub networks as a measure of
interactivity of a traffic scene. We evaluate our model using a motion dataset
and discuss the value of the relationship information with respect to different
traffic situations.
