Uni-World VLA：自主驾驶中的交织世界建模与规划
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
AI总结：
本文提出Uni-World VLA模型，通过交织未来帧预测与轨迹规划提升自主驾驶决策能力，结合单目深度信息增强场景预测。
摘要：
自动驾驶需要推理环境演变并规划相应动作。现有基于世界模型的方法通常先预测未来场景再规划，导致开环想象可能偏离实际决策过程。本文提出Uni-World VLA，一种统一的视觉-语言-动作（VLA）模型，紧密交织未来帧预测与轨迹规划。不同于先生成完整世界预测再规划，该模型交替生成未来帧和自我动作，使规划决策持续依赖于想象的未来观测。这种交织生成形成世界建模与控制之间的闭环交互，使动态交通场景中的决策更适应。此外，我们将单目深度信息融入帧中，为世界建模提供更强的几何线索，提升长周期场景预测。在NAVSIM基准测试中，我们的方法在闭环规划性能方面具有竞争力，同时产生高保真的未来帧预测。这些结果表明，紧密耦合世界预测与规划是可扩展VLA驾驶系统的重要方向。
英文摘要：
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes first and plan afterwards, resulting in open-loop imagination that may drift from the actual decision process. In this paper, we present Uni-World VLA, a unified vision-language-action (VLA) model that tightly interleaves future frame prediction and trajectory planning. Instead of generating a full world rollout before planning, our model alternates between predicting future frames and ego actions step by step, allowing planning decisions to be continuously conditioned on the imagined future observations. This interleaved generation forms a closed-loop interaction between world modeling and control, enabling more adaptive decision-making in dynamic traffic scenarios. In addition, we incorporate monocular depth information into frames to provide stronger geometric cues for world modeling, improving long-horizon scene prediction. Experiments on the NAVSIM benchmark show that our approach achieves competitive closed-loop planning performance while producing high-fidelity future frame predictions. These results demonstrate that tightly coupling world prediction and planning is a promising direction for scalable VLA driving systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
