发表机构
College of Engineering, University of Georgia（佐治亚大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员提出DriveDegrade基准测试，评估仅基于相机的端到端驾驶模型对各类图像退化的容忍度，发现不同退化对规划的影响差异显著，某视觉-语言-动作规划器被遮挡6个相机仅损失11.5%性能。
AI 中文摘要
仅基于相机的端到端（E2E）驾驶模型即将部署，而相机流可能会因模糊、噪声、弱光、天气、帧丢失和内存故障而退化。策略在驾驶性能失效前能承受多少退化尚不明确。现有的抗损坏基准测试针对的是检测或鸟瞰感知，而非驱动车辆的规划输出。我们提出了DriveDegrade，一个用于仅基于相机的端到端驾驶的图像退化容忍度基准测试。在图像加载器中实时注入16种损坏类别，每种类别分为5个严重程度，其中一种操作覆盖15种策略，我们在nuScenes和NAVSIM上评估开环规划，同时以CARLA作为闭环基准。研究发现：第一，轻度退化几乎不影响规划，导致规划失效的损坏类别在中等严重程度时有明确阈值；第二，脆弱性与损坏类型相关：模糊、JPEG和雨滴对规划的破坏最大，而天气和比特错误在很大程度上可被容忍；第三，平坦曲线具有歧义，因此我们将损坏分为降低图像质量和移除图像信息两类。读取相机的规划器在信息被移除时必然会损失精度，无论其在质量损失下的表现如何。在这两个维度上，规划器表现出明显差异，量化了自我状态捷径，且不会将无差异误判为鲁棒性。一个已发布的视觉-语言-动作规划器在两个维度上均呈平坦曲线，且其所有6个相机被完全遮挡仅导致11.5%的性能损失。
英文摘要
Camera-only end-to-end (E2E) driving models are nearing deployment, where the camera stream is degraded by blur, noise, low light, weather, frame loss, and memory faults. How much a policy tolerates before its driving breaks is unclear. Corruption-robustness benchmarks target detection or bird's-eye-view perception, not the planning output that drives the car. We present DriveDegrade, a benchmark for image-degradation tolerance in camera-only E2E driving. Sixteen corruption families at five severities are injected on the fly inside the image loader, one operator reaching fifteen policies, and we evaluate open-loop planning on nuScenes and NAVSIM plus a CARLA closed-loop anchor. First, mild degradation barely affects planning, and the families that break it have a clear threshold at mid severity. Second, fragility is corruption-dependent: blur, JPEG, and raindrop damage planning most, while weather and bit error are tolerated far into the range. Third, a flat curve is ambiguous, so we separate corruptions that degrade the image from those that remove it. A planner that reads its camera must lose accuracy when information is deleted, whatever it does under quality loss. On these two axes the planners separate sharply, quantifying the ego-status shortcut without mistaking indifference for robustness. A released vision-language-action planner is flat on both axes, and blinding all six of its cameras costs it only 11.5 percent.
