  We introduce RedMotion, a transformer model for motion prediction in
self-driving vehicles that learns environment representations via redundancy
reduction. Our first type of redundancy reduction is induced by an internal
transformer decoder and reduces a variable-sized set of local road environment
tokens, representing road graphs and agent data, to a fixed-sized global
embedding. The second type of redundancy reduction is obtained by
self-supervised learning and applies the redundancy reduction principle to
embeddings generated from augmented views of road environments. Our experiments
reveal that our representation learning approach outperforms PreTraM, Traj-MAE,
and GraphDINO in a semi-supervised setting. Moreover, RedMotion achieves
competitive results compared to HPTR or MTR++ in the Waymo Motion Prediction
Challenge. Our open-source implementation is available at:
https://github.com/kit-mrt/future-motion
