RedMotion: Motion Prediction via Redundancy Reduction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916668635086848 |
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| author | Wagner, Royden Tas, Omer Sahin Klemp, Marvin Fernandez, Carlos Stiller, Christoph |
| author_facet | Wagner, Royden Tas, Omer Sahin Klemp, Marvin Fernandez, Carlos Stiller, Christoph |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_10840 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RedMotion: Motion Prediction via Redundancy Reduction Wagner, Royden Tas, Omer Sahin Klemp, Marvin Fernandez, Carlos Stiller, Christoph Computer Vision and Pattern Recognition Robotics 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 |
| title | RedMotion: Motion Prediction via Redundancy Reduction |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2306.10840 |