Epona: Autoregressive Diffusion World Model for Autonomous Driving

Engineering notes

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 realtime motion planner, outperforming strong end-to-end planners on NAVSIM benchmarks.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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-toend 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 realtime motion planner, outperforming strong end-to-end planners on NAVSIM benchmarks.

Links and sources

• Official / arXiv page
• PDF from original source

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