Abstract
Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-based world models struggle with flexible-length, long-horizon predictions and integrating trajectory planning. This is because conventional video diffusion models rely on global joint distribution modeling of fixed-length frame sequences rather than sequentially constructing localized distributions at each timestep. In this work, we propose Epona, an autoregressive diffusion world model that enables localized spatiotemporal distribution modeling through two key innovations: 1) Decoupled spatiotemporal factorization that separates temporal dynamics modeling from fine-grained future world generation, and 2) Modular trajectory and video prediction that seamlessly integrate motion planning with visual modeling in an end-to-end framework. Our architecture enables high-resolution, long-duration generation while introducing a novel chain-of-forward training strategy to address error accumulation in autoregressive loops. Experimental results demonstrate state-of-the-art performance with 7.4\% FVD improvement and minutes longer prediction duration compared to prior works. The learned world model further serves as a real-time motion planner, outperforming strong end-to-end planners on NAVSIM benchmarks. Code will be publicly available at \href{this https URL}{this https URL}.
Abstract (translated)
扩散模型在视频生成方面展现了出色的视觉质量，因此它们在自主驾驶世界的建模中展现出巨大的潜力。然而，现有的基于视频扩散的世界模型在处理灵活长度和长时预测以及整合轨迹规划方面存在困难。这是因为传统的视频扩散模型依赖于固定长度帧序列的全局联合分布建模，而不是通过每个时间步逐次构建局部化分布。 为此，我们提出了一种名为Epona的自回归扩散世界模型，它通过两个关键创新实现了局部化的时空分布建模：1）解耦的时间空间因子分解，将动态过程建模与细粒度未来世界生成分离；2）模块化轨迹和视频预测，使运动规划能够无缝地与视觉建模相结合，在一个端到端的框架内。我们的架构不仅支持高分辨率、长时间的生成，并且引入了一种新颖的前向训练策略来解决自回归循环中的误差积累问题。 实验结果表明，Epona在FVD指标上提高了7.4%，并且预测时长比先前的工作更长几分钟。此外，所学习的世界模型还可作为实时运动规划器，在NAVSIM基准测试中超越了所有端到端的规划器性能。代码将在 [此链接](this https URL) 上公开提供。
URL
https://arxiv.org/abs/2506.24113
