DriveWeave-Policy: Weaving Sparse Future Imagination and Trajectory Planning through Aligned Denoising

A central challenge for driving world-action models (WAMs) is how to effectively leverage visual generative priors for trajectory planning. Yet driving scenes are highly dynamic, and densely generating future visual observation imposes a substantial modeling burden on trajectory planning. Separate diffusion branches offer a straightforward way to reuse pretrained generative models in driving WAMs. However, coordinating these branches through LLM-mediated feature exchange alone may be insufficient to ensure consistency between imagined futures and planned actions. In fact, imagination and planning reflect the same underlying scene dynamics, and we accordingly introduce DriveWeave. Unlike existing frame-by-frame autoregressive unified WAMs, DriveWeave is designed to better harness visual generative priors at a lower sampling frequency. Within this framework, we compare three training paradigms for learning and transferring cross-modal priors: Imagine-then-Plan, Plan-then-Imagine, and Coupled Denoising. We find that sparse anchored imagination and coupled denoising best match the tight perception–action loop of driving. Furthermore, we introduce a learnable trajectory encoding method that brings the distributions of low-dimensional trajectory features and high-dimensional visual features closer together in a shared latent space. We coordinate visual and trajectory denoising through two complementary mechanisms: cross-modal information exchange and dynamic alignment. For information exchange, shared attention enables bidirectional conditioning at every denoising step. For dynamic alignment, we reformulate the reverse-diffusion dynamics with a consistency-driven coupling term that adjusts modality-specific updates based on agreement between predicted futures. On NAVSIM, DriveWeave achieves a PDMS of 91.3, the best performance among the evaluated methods.