mlsanityData to 7 Oct 2026

RedMotion: Motion Prediction via Redundancy Reduction

arXiv 2306.10840 ↗First posted 19 Jun 2023cs.CV cs.ROcited by 6

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Abstract (arXiv)

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

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 47 of its 57 references that are in the map. First posted on arXiv 19 Jun 2023.

Builds on

47 of 57 references.

References · 47

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35 more
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Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Words in Motion: Extracting Interpretable Control Vectors for Motion Transformerscited by 9
  2. 2024JointMotion: Joint Self-Supervision for Joint Motion Predictioncited by 7
  3. 2025Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Drivingcited by 5
  4. 2024SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecastscited by 4
  5. 2025Generative AI for Autonomous Driving: A Reviewcited by 3
  6. 2025RetroMotion: Retrocausal Motion Forecasting Models are Instructablecited by 2