DA-WAM: Decision-Aligned Future Latents for Driving World Models
Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making remains unrealized. The critical challenge lies in ensuring that future modeling is not merely predictive, but decision-informative: the predicted future must directly shape which trajectory is selected. Existing approaches decouple future representation learning from planning optimization, or share predicted states across trajectory candidates, thereby diluting the action-specific consequences that ought to guide selection. To bridge this gap, we propose DA-WAM, a framework that unifies predictive representation learning, action-conditione...
| Authors | Ruiguo Zhong, Benshan Ma, Xiaolong Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 August 2026 |
| Last updated at source | 20 August 2026 |
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