Learning to Drive from a World Model
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913810104713216 |
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| author | Goff, Mitchell Hogan, Greg Hotz, George Locmaria, Armand du Parc Raczy, Kacper Schäfer, Harald Shihadeh, Adeeb Zhang, Weixing Yousfi, Yassine |
| author_facet | Goff, Mitchell Hogan, Greg Hotz, George Locmaria, Armand du Parc Raczy, Kacper Schäfer, Harald Shihadeh, Adeeb Zhang, Weixing Yousfi, Yassine |
| contents | Most self-driving systems rely on hand-coded perception outputs and engineered driving rules. Learning directly from human driving data with an end-to-end method can allow for a training architecture that is simpler and scales well with compute and data.
In this work, we propose an end-to-end training architecture that uses real driving data to train a driving policy in an on-policy simulator. We show two different methods of simulation, one with reprojective simulation and one with a learned world model. We show that both methods can be used to train a policy that learns driving behavior without any hand-coded driving rules. We evaluate the performance of these policies in a closed-loop simulation and when deployed in a real-world advanced driver-assistance system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Drive from a World Model Goff, Mitchell Hogan, Greg Hotz, George Locmaria, Armand du Parc Raczy, Kacper Schäfer, Harald Shihadeh, Adeeb Zhang, Weixing Yousfi, Yassine Computer Vision and Pattern Recognition Robotics Most self-driving systems rely on hand-coded perception outputs and engineered driving rules. Learning directly from human driving data with an end-to-end method can allow for a training architecture that is simpler and scales well with compute and data. In this work, we propose an end-to-end training architecture that uses real driving data to train a driving policy in an on-policy simulator. We show two different methods of simulation, one with reprojective simulation and one with a learned world model. We show that both methods can be used to train a policy that learns driving behavior without any hand-coded driving rules. We evaluate the performance of these policies in a closed-loop simulation and when deployed in a real-world advanced driver-assistance system. |
| title | Learning to Drive from a World Model |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2504.19077 |