Robust perception in automated driving requires reliable performance under adverse conditions, where sensors may be affected by partial failures or environmental occlusions. Although existing autonomous driving datasets inherently contain sensor noise and environmental variability, very few enable controlled, parameterised, and reproducible degradations across multiple sensing modalities. This gap limits the ability to systematically evaluate how perception and fusion architectures perform under well-defined adverse conditions. To address this limitation, we introduce the Occluded nuScenes Dataset, a novel extension of the widely used nuScenes benchmark. For the camera modality, we release both the full and mini versions with four types of occlusions, two adapted from public implementations and two newly designed. For radar and LiDAR, we provide parameterised occlusion scripts that implement three types of degradations each, enabling flexible and repeatable generation of occluded data. This resource supports consistent, reproducible evaluation of perception models under partial sensor failures and environmental interference. By releasing the first multi-sensor occlusion dataset with controlled and reproducible degradations, we aim to advance research on robust sensor fusion, resilience analysis, and safety-critical perception in automated driving.

翻译：自动驾驶中的鲁棒感知要求系统在传感器可能受部分故障或环境遮挡影响的不利条件下仍能可靠运行。尽管现有的自动驾驶数据集本身包含传感器噪声和环境变化，但极少有数据集能够实现跨多种传感模态的、可控的、参数化的且可复现的性能退化。这一空白限制了系统评估感知与融合架构在明确定义的不利条件下性能表现的能力。为弥补这一不足，我们推出了遮挡nuScenes数据集，这是对广泛使用的nuScenes基准测试的全新扩展。针对摄像头模态，我们发布了完整版和迷你版数据集，包含四种遮挡类型，其中两种改编自公开实现，两种为新设计。针对雷达和激光雷达，我们提供了参数化遮挡脚本，每种传感器分别实现三类退化，支持灵活且可重复地生成遮挡数据。该资源支持在部分传感器故障和环境干扰下对感知模型进行一致且可复现的评估。通过发布首个具备可控可复现退化功能的多传感器遮挡数据集，我们旨在推动自动驾驶领域在鲁棒传感器融合、弹性分析和安全关键感知方面的研究。