  Self-driving cars face complex driving situations with a large amount of
agents when moving in crowded cities. However, some of the agents are actually
not influencing the behavior of the self-driving car. Filtering out unimportant
agents would inherently simplify the behavior or motion planning task for the
system. The planning system can then focus on fewer agents to find optimal
behavior solutions for the ego~agent. This is helpful especially in terms of
computational efficiency. In this paper, therefore, the research topic of
importance filtering with driving risk models is introduced. We give an
overview of state-of-the-art risk models and present newly adapted risk models
for filtering. Their capability to filter out surrounding unimportant agents is
compared in a large-scale experiment. As it turns out, the novel trajectory
distance balances performance, robustness and efficiency well. Based on the
results, we can further derive a novel filter architecture with multiple filter
steps, for which risk models are recommended for each step, to further improve
the robustness. We are confident that this will enable current behavior
planning systems to better solve complex situations in everyday driving.
