Kraken: enabling joint trajectory prediction by utilizing Mode
  Transformer and Greedy Mode Processing
Daniil S. Antonenko Stepan Konev Yuriy Biktairov Boris Yangel
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.