Despite rapid progress, autonomous driving algorithms remain notoriously fragile under Out-of-Distribution (OOD) conditions. We identify a critical decoupling failure in current research: the lack of distinction between appearance-based shifts, such as weather and lighting, and structural scene changes. This leaves a fundamental question unanswered: Is the planner failing because of complex road geometry, or simply because it is raining? To resolve this, we establish navdream, a high-fidelity robustness benchmark leveraging generative pixel-aligned style transfer. By creating a visual stress test with negligible geometric deviation, we isolate the impact of appearance on driving performance. Our evaluation reveals that existing planning algorithms often show significant degradation under OOD appearance conditions, even when the underlying scene structure remains consistent. To bridge this gap, we propose a universal perception interface leveraging a frozen visual foundation model (DINOv3). By extracting appearance-invariant features as a stable interface for the planner, we achieve exceptional zero-shot generalization across diverse planning paradigms, including regression-based, diffusion-based, and scoring-based models. Our plug-and-play solution maintains consistent performance across extreme appearance shifts without requiring further fine-tuning. The benchmark and code will be made available.

翻译：尽管进展迅速，自动驾驶算法在分布外（OOD）条件下的脆弱性依然众所周知。我们发现了当前研究中的一个关键解耦失败：未能明确区分基于外观的偏移（如天气和光照）与场景结构变化。这导致一个根本性问题悬而未决：规划器失效是由于复杂的道路几何结构，还是仅仅因为正在下雨？为解决此问题，我们建立了navdream——一个利用生成式像素对齐风格迁移的高保真鲁棒性基准。通过创建几何偏差可忽略的视觉压力测试，我们分离了外观对驾驶性能的影响。评估结果表明，即使底层场景结构保持一致，现有规划算法在OOD外观条件下仍常表现出显著性能退化。为弥合这一差距，我们提出了一种利用冻结视觉基础模型（DINOv3）的通用感知接口。通过提取外观不变特征作为规划器的稳定接口，我们在多种规划范式中实现了卓越的零样本泛化能力，包括基于回归、扩散和评分的模型。我们的即插即用解决方案在极端外观变化下保持稳定性能，且无需进一步微调。基准测试与代码将公开提供。