A Degradation-Tolerance Benchmark for Camera-Only End-to-End Driving

2026-08-28

AI Summary

研究针对仅使用摄像头的端到端驾驶模型在图像降质情况下的容忍度问题，通过DriveDegrade基准测试评估了不同降质类型和程度对驾驶策略的影响。

Abstract

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.

Problem

• Camera-only E2E driving
• Image degradation
• Robustness
• Policy tolerance
• Benchmark

Innovation

• DriveDegrade
• image-degradation tolerance
• camera-only E2E driving
• corruption-robustness
• planning performance