The DAWN of World-Action Interactive Models

TL;DR

The paper introduces DAWN, a novel latent generative model for autonomous driving that couples world prediction with action denoising, enabling recursive refinement and improved long-horizon planning.

Contribution

It formalizes World-Action Interactive Models (WAIMs) and instantiates them in DAWN, a simple yet effective latent model coupling world prediction with action denoising for autonomous driving.

Findings

DAWN achieves strong planning performance on autonomous driving benchmarks.

DAWN produces favorable safety-related results.

DAWN effectively supports long-horizon trajectory generation.

Abstract

A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action generation as either isolated parallel branches or rigid predict-then-plan pipelines. We formalize this perspective as World-Action Interactive Models (WAIMs), and instantiate it in autonomous driving with \textbf{DAWN} (\textbf{D}enoising \textbf{A}ctions and \textbf{W}orld i\textbf{N}teractive model), a simple yet strong latent generative baseline. DAWN operates in a compact semantic latent space and couples a \emph{World Predictor} with a \emph{World-Conditioned Action Denoiser}: the predicted world hypothesis conditions action denoising, while the denoised action hypothesis is fed back to update the world prediction, so that both are recursively refined during inference. Rather than eliminating test-time world evolution altogether or rolling out the full future in pixel space, DAWN performs a short explicit latent rollout that is sufficient to support long-horizon trajectory generation in complex interactive scenes. Experiments show that DAWN achieves strong planning performance and favorable safety-related results across multiple autonomous driving benchmarks. More broadly, our results suggest that interactive world-action generation is a principled path toward truly actionable world models.