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.

提出一种名为Kraken的实时轨迹预测模型，能够近似模拟自主系统周围参与者之间的相互作用，并生成准确的边缘预测。在2021年十月份的Waymo Motion Prediction挑战中，Kraken在交互排行榜上获得第一名，运动排行榜上获得第二名。

Kraken: 利用模态变换器和贪婪模态处理实现联合轨迹预测