GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving

Ziying Song; Caiyan Jia; Lin Liu; Lei Yang; Shengkai Zhang; Feiyang Jia; Fengda Zhao; Peiliang Wu; Shaoqing Xu; Chen Lv; Yadan Luo

arXiv · 2606.16274

GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving

Ziying Song·Caiyan Jia·Lin Liu·Lei Yang·Shengkai Zhang·Feiyang Jia·Fengda Zhao·Peiliang Wu·Shaoqing Xu·Chen Lv·Yadan Luo

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Abstract

End-to-end autonomous driving has made significant progress by unifying perception, prediction, and planning within a single learning framework, achieving strong performance in short-horizon decision making. However, most existing E2E-AD methods remain confined to short-horizon planning and lack the ability to model long-term temporal dependencies, which severely limits their generalization and security in complex and highly interactive driving scenarios. In this work, we propose GraphWorld, an E2E-AD framework that explicitly enhances long-horizon planning through latent world modeling. We introduce an Ego-Centric Interaction Graph, which adaptively models critical neighboring agents based on spatial proximity, and propagates relational context to planning queries via cross-node cross-attention. We present a World-State-Conditioned Planning that learns ego-centric latent world representations by modeling interactions between an ego vehicle and surrounding agents. This latent world state captures key interaction dynamics and safety-relevant semantics, and serves as a conditioning signal to guide long-horizon, safety-aware trajectory planning. Extensive experiments on Bench2Drive, NAVSIMv1/2, and nuScenes demonstrate that GraphWorld significantly reduces collision rates and improves long-horizon planning performance, validating its effectiveness in complex driving environments.

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Ziying Song, Caiyan Jia, Lin Liu, Lei Yang, Shengkai Zhang, Feiyang Jia, Fengda Zhao, Peiliang Wu, Shaoqing Xu, Chen Lv, Yadan Luo. 2026-06-15. GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving. https://arxiv.org/abs/2606.16274

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