UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
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| _version_ | 1866910008390713344 |
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| author | Liu, Shuai Ren, Siheng Zhu, Xiaoyao Liang, Quanmin Li, Zefeng Li, Qiang Hu, Xin Huang, Kai |
| author_facet | Liu, Shuai Ren, Siheng Zhu, Xiaoyao Liang, Quanmin Li, Zefeng Li, Qiang Hu, Xin Huang, Kai |
| contents | Achieving reliable and efficient planning in complex driving environments requires a model that can reason over the scene's geometry, appearance, and dynamics. We present UniDWM, a unified driving world model that advances autonomous driving through multifaceted representation learning. UniDWM constructs a structure- and dynamic-aware latent world representation that serves as a physically grounded state space, enabling consistent reasoning across perception, prediction, and planning. Specifically, a joint reconstruction pathway learns to recover the scene's structure, including geometry and visual texture, while a collaborative generation framework leverages a conditional diffusion transformer to forecast future world evolution within the latent space. Furthermore, we show that our UniDWM can be deemed as a variation of VAE, which provides theoretical guidance for the multifaceted representation learning. Extensive experiments demonstrate the effectiveness of UniDWM in trajectory planning, 4D reconstruction and generation, highlighting the potential of multifaceted world representations as a foundation for unified driving intelligence. The code will be publicly available at https://github.com/Say2L/UniDWM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01536 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning Liu, Shuai Ren, Siheng Zhu, Xiaoyao Liang, Quanmin Li, Zefeng Li, Qiang Hu, Xin Huang, Kai Robotics Computer Vision and Pattern Recognition Achieving reliable and efficient planning in complex driving environments requires a model that can reason over the scene's geometry, appearance, and dynamics. We present UniDWM, a unified driving world model that advances autonomous driving through multifaceted representation learning. UniDWM constructs a structure- and dynamic-aware latent world representation that serves as a physically grounded state space, enabling consistent reasoning across perception, prediction, and planning. Specifically, a joint reconstruction pathway learns to recover the scene's structure, including geometry and visual texture, while a collaborative generation framework leverages a conditional diffusion transformer to forecast future world evolution within the latent space. Furthermore, we show that our UniDWM can be deemed as a variation of VAE, which provides theoretical guidance for the multifaceted representation learning. Extensive experiments demonstrate the effectiveness of UniDWM in trajectory planning, 4D reconstruction and generation, highlighting the potential of multifaceted world representations as a foundation for unified driving intelligence. The code will be publicly available at https://github.com/Say2L/UniDWM. |
| title | UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.01536 |