  Data driven approaches for decision making applied to automated driving
require appropriate generalization strategies, to ensure applicability to the
world's variability. Current approaches either do not generalize well beyond
the training data or are not capable to consider a variable number of traffic
participants. Therefore we propose an invariant environment representation from
the perspective of the ego vehicle. The representation encodes all necessary
information for safe decision making. To assess the generalization capabilities
of the novel environment representation, we train our agents on a small subset
of scenarios and evaluate on the entire set. Here we show that the agents are
capable to generalize successfully to unseen scenarios, due to the abstraction.
In addition we present a simple occlusion model that enables our agents to
navigate intersections with occlusions without a significant change in
performance.
