构建可控的驾驶视觉退化数据集，评估自动驾驶系统鲁棒性。
DRIVE-C: A Controlled Corruption Dataset for Autonomous Driving
- 从真实驾驶视频生成12类、5级退化，保持像素对齐
- 含10段清晰片段与600段退化片段，带健康指数标注
- 适合做感知可靠性、传感器健康监测等研究
DRIVE-C 是一个用于评估自动驾驶系统视觉感知鲁棒性的可控退化数据集。数据源自白天、夜晚、城市、乡村、高速和停车场等多种环境的真实前向行车视频。原始视频通过局部人脸与车牌模糊实现匿名化，再施加基于物理的合成退化。数据集包含10段清晰视频和600段退化视频，覆盖12种相机退化类型，每种类型分五个严重程度等级，并附有每段视频的元数据和全局传感器健康指数（GSHI）标注。DRIVE-C 支持鲁棒性基准测试、退化感知建模、不确定性估计、分布外（OOD）检测及高级驾驶辅助系统（ADAS）的传感器健康监控。通过提供像素对齐的清晰与退化视频片段以及完全可复现的退化参数，该数据集为研究在可控相机退化下的感知可靠性提供了结构化测试平台。
原文摘要 · Abstract (English)
DRIVE-C is a controlled corruption dataset designed to evaluate visual perception robustness in autonomous driving systems. It is built from real-world forward-facing driving videos collected across daytime, nighttime, urban, rural, freeway, and parking environments. Clean clips are anonymized via localized face and license plate blurring, then transformed with physics-inspired synthetic degradations. The dataset contains 10 clean clips and 600 corrupted clips spanning 12 camera degradation types across five severity levels, with per-clip metadata and Global Sensor Health Index (GSHI) annotations. DRIVE-C supports robustness benchmarking, degradation-aware modeling, uncertainty estimation, out-of-distribution (OOD) detection, and sensor health monitoring for Advanced Driver Assistance Systems (ADAS). By providing pixel-aligned clean and degraded video clips with fully reproducible corruption parameters, DRIVE-C offers a structured testbed for studying perception reliability under controlled camera degradation.
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