Predicting multimodal future behavior of traffic participants is essential
for robotic vehicles to make safe decisions. Existing works explore to directly
predict future trajectories based on latent features or utilize dense goal
candidates to identify agent's destinations, where the former strategy
converges slowly since all motion modes are derived from the same feature while
the latter strategy has efficiency issue since its performance highly relies on
the density of goal candidates. In this paper, we propose Motion TRansformer
(MTR) framework that models motion prediction as the joint optimization of
global intention localization and local movement refinement. Instead of using
goal candidates, MTR incorporates spatial intention priors by adopting a small
set of learnable motion query pairs. Each motion query pair takes charge of
trajectory prediction and refinement for a specific motion mode, which
stabilizes the training process and facilitates better multimodal predictions.
Experiments show that MTR achieves state-of-the-art performance on both the
marginal and joint motion prediction challenges, ranking 1st on the
leaderboards of Waymo Open Motion Dataset. The source code is available at
this https URL

本文提出了一个 Motion TRansformer (MTR) 框架，将运动预测看作全局意向定位和局部运动细化的联合优化问题，通过可学习的少量运动查询对来引入空间意向先验，实现了更好的多模态预测。实验表明，该方法在多个运动预测挑战上均达到了最先进的水平，并在 Waymo 开放式运动数据集的排行榜上排名第一。