  We present JointMotion, a self-supervised learning method for joint motion
prediction in autonomous driving. Our method includes a scene-level objective
connecting motion and environments, and an instance-level objective to refine
learned representations. Our evaluations show that these objectives are
complementary and outperform recent contrastive and autoencoding methods as
pre-training for joint motion prediction. Furthermore, JointMotion adapts to
all common types of environment representations used for motion prediction
(i.e., agent-centric, scene-centric, and pairwise relative), and enables
effective transfer learning between the Waymo Open Motion and the Argoverse 2
Forecasting datasets. Notably, our method improves the joint final displacement
error of Wayformer, Scene Transformer, and HPTR by 3%, 7%, and 11%,
respectively.
