$AIR^2$ for Interaction Prediction
This work addresses interaction prediction for autonomous vehicles, but it is incremental as it augments an existing marginal motion prediction model.
The paper tackled the problem of jointly predicting future trajectories and confidences for two interacting agents in the Waymo Interaction Prediction Challenge, and their model achieved the highest mAP on the leaderboard.
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.