Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918166479765504 |
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| author | Kumar, Sanjay Brophy, Tim Mohandas, Reenu Grua, Eoin Martino Sistu, Ganesh Donzella, Valentina Eising, Ciaran |
| author_facet | Kumar, Sanjay Brophy, Tim Mohandas, Reenu Grua, Eoin Martino Sistu, Ganesh Donzella, Valentina Eising, Ciaran |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_18552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving Kumar, Sanjay Brophy, Tim Mohandas, Reenu Grua, Eoin Martino Sistu, Ganesh Donzella, Valentina Eising, Ciaran Computer Vision and Pattern Recognition 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. |
| title | Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.18552 |