  Interactive driving scenarios, such as lane changes, merges and unprotected
turns, are some of the most challenging situations for autonomous driving.
Planning in interactive scenarios requires accurately modeling the reactions of
other agents to different future actions of the ego agent. We develop
end-to-end models for conditional behavior prediction (CBP) that take as an
input a query future trajectory for an ego-agent, and predict distributions
over future trajectories for other agents conditioned on the query. Leveraging
such a model, we develop a general-purpose agent interactivity score derived
from probabilistic first principles. The interactivity score allows us to find
interesting interactive scenarios for training and evaluating behavior
prediction models. We further demonstrate that the proposed score is effective
for agent prioritization under computational budget constraints.
