JointMotion: Joint Self-Supervision for Joint Motion Prediction

Understand

We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles.

• Our method jointly optimizes a scene-level objective connecting motion and environments, and an instance-level objective to refine learned representations.
• Scene-level representations are learned via non-contrastive similarity learning of past motion sequences and environment context.
• At the instance level, we use masked autoencoding to refine multimodal polyline representations.