DRIVE-C: A Controlled Corruption Dataset for Autonomous Driving

TL;DR

DRIVE-C is a dataset of real-world driving videos with controlled synthetic corruptions, designed to evaluate and improve the robustness of autonomous driving perception systems.

Contribution

It introduces a comprehensive, reproducible dataset with diverse corruptions and annotations for benchmarking perception robustness in autonomous driving.

Findings

Contains 10 clean and 600 corrupted clips across 12 degradation types.

Supports benchmarking, uncertainty estimation, and sensor health monitoring.

Provides pixel-aligned clean and degraded videos with reproducible corruption parameters.

Abstract

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.