Epona: Autoregressive Diffusion World Model for Autonomous Driving

TL;DR

Epona introduces an autoregressive diffusion model for autonomous driving that enables long-horizon, high-resolution world modeling and integrates motion planning, achieving state-of-the-art results in video prediction and real-time planning.

Contribution

The paper proposes a novel autoregressive diffusion framework with decoupled spatiotemporal modeling and modular prediction, advancing long-duration, flexible autonomous driving world modeling.

Findings

7.4% FVD improvement over prior methods

Longer prediction durations in autonomous driving scenarios

Outperforms end-to-end planners on NAVSIM benchmarks

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{https://github.com/Kevin-thu/Epona/}{https://github.com/Kevin-thu/Epona/}.