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

Kraken: enabling joint trajectory prediction by utilizing Mode  Transformer and Greedy Mode Processing

As autonomous driving systems mature, motion forecasting has received
increasing attention as a critical requirement for planning. Of particular
importance are interactive situations such as merges, unprotected turns, etc.,
where predicting individual object motion is not sufficient. Joint predictions
of multiple objects are required for effective route planning. There has been a
critical need for high-quality motion data that is rich in both interactions
and annotation to develop motion planning models. In this work, we introduce
the most diverse interactive motion dataset to our knowledge, and provide
specific labels for interacting objects suitable for developing joint
prediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, our
new dataset contains more than 570 hours of unique data over 1750 km of
roadways. It was collected by mining for interesting interactions between
vehicles, pedestrians, and cyclists across six cities within the United States.
We use a high-accuracy 3D auto-labeling system to generate high quality 3D
bounding boxes for each road agent, and provide corresponding high definition
3D maps for each scene. Furthermore, we introduce a new set of metrics that
provides a comprehensive evaluation of both single agent and joint agent
interaction motion forecasting models. Finally, we provide strong baseline
models for individual-agent prediction and joint-prediction. We hope that this
new large-scale interactive motion dataset will provide new opportunities for
advancing motion forecasting models.

该研究介绍了当前最全面的交互式动作数据集，包含超过 100,000 个场景，用于发展动作规划模型与提供强大的基线模型来对单独或多个交互式对象进行预测，并提供了新的度量方法。