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.

翻译：2021年Waymo互动预测挑战引入了一个预测两个互动代理商未来轨迹和信心的问题。 我们开发了一个解决方案, 采用一个固定的边际运动预测模型, 并采用光化, 并增加其模型代理商互动。 我们通过使用一个光化图像来预测联合信任, 以突出自我代理商和互动代理商。 我们的解决方案在锚的卡通产品空间运作, 因此以$AIR2$为单位的“ $2 ” 。 我们的模型在头板上达到了最高 mAP( 主要的衡量标准 ) 。