Accurate and reliable motion prediction is essential for safe urban autonomy. The most prominent motion prediction approaches are based on modeling the distribution of possible future trajectories of each actor in autonomous system's vicinity. These "independent" marginal predictions might be accurate enough to properly describe casual driving situations where the prediction target is not likely to interact with other actors. They are, however, inadequate for modeling interactive situations where the actors' future trajectories are likely to intersect. To mitigate this issue we propose Kraken -- a real-time trajectory prediction model capable of approximating pairwise interactions between the actors as well as producing accurate marginal predictions. Kraken relies on a simple Greedy Mode Processing technique allowing it to convert a factorized prediction for a pair of agents into a physically-plausible joint prediction. It also utilizes the Mode Transformer module to increase the diversity of predicted trajectories and make the joint prediction more informative. We evaluate Kraken on Waymo Motion Prediction challenge where it held the first place in the Interaction leaderboard and the second place in the Motion leaderboard in October 2021.

翻译：准确可靠的运动预测对于安全的城市自动驾驶至关重要。当前最主流的运动预测方法基于对自动驾驶系统周边每个参与者未来可能轨迹分布进行建模。这种"独立"的边际预测在预测目标与其他参与者不太可能发生交互的常规驾驶场景中足够精确。然而，当参与者未来轨迹可能相交的交互场景建模时，这些方法存在不足。为解决此问题，我们提出Kraken——一种能够近似参与者间成对交互并生成精准边际预测的实时轨迹预测模型。Kraken采用简单的贪婪模式处理技术，将两个智能体的因式化预测转化为物理可行的联合预测。同时，它利用模式变换器模块增加预测轨迹的多样性，使联合预测更具信息量。我们在Waymo运动预测挑战赛中对Kraken进行了评估，该模型于2021年10月在该竞赛的交互榜单和运动榜单中分别取得第一名和第二名的成绩。