  The 2021 Waymo Interaction Prediction Challenge introduced a problem of
predicting the future trajectories and confidences of two interacting agents
jointly. We developed a solution that takes an anchored marginal motion
prediction model with rasterization and augments it to model agent interaction.
We do this by predicting the joint confidences using a rasterized image that
highlights the ego agent and the interacting agent. Our solution operates on
the cartesian product space of the anchors; hence the $"^2"$ in $AIR^2$. Our
model achieved the highest mAP (the primary metric) on the leaderboard.
