RainSense: An Autonomous Driving Environmental Perception Dataset with Rain Intensity Annotations
Abstract
Rainfall, as a common trigger condition in the Safety of the Intended Functionality (SOTIF) framework, can impair autonomous driving perception systems, leading to unexpected functional failures. However, studies focusing on sensor performance degradation under natural rainfall conditions are limited, primarily due to the lack of datasets with detailed rainfall information. To address this gap, this study present RainSense, a multi-sensor autonomous driving dataset collected under natural rainfall conditions, featuring fine-grained rainfall intensity annotations. RainSense was recorded at nine representative intersection scenarios in the campus, where a single dummy target was placed at various distances as a detection target. A laser-optical disdrometer was deployed to continuously measure rainfall intensity (mm/h), while camera images, lidar point clouds, and 4D radar data were synchronously collected under different rainfall levels. In total, the dataset comprises 728 cases, including 145 with clear condition, 214 with light rain, 204 with moderate rain, 98 with heavy rain, and 67 with torrential rain. Each case is segmented into 10-second windows and includes 2D and 3D bounding box labels of the dummy target. To investigate how rainfall affects different perception modalities, perception metrics were applied to each sensor type. Results reveal that under heavy and torrential rain, camera images suffer from blur, while lidar experiences sparse and weakened point returns, both leading to substantial perception degradation. In contrast, radar shows minimal variation across all rain levels, maintaining stable signal characteristics and demonstrating strong resilience to adverse weather conditions. The dataset and benchmark suite will be released open-source at: https://github.com/IVtest-Lab/RainSense.git.
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Research works printed but not linked: 1
- Zhang , H. , Li , F. , Liu , S. et al. DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection The Eleventh International Conference on Learning Representations
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Xia, T., Zhao, X., Ye, S., et al. (2026) · arXiv.org · doi:10.48550/arxiv.2606.11989
“The real-rainfall reference data used in this paper include a real-rainfall raindrop-distribution reference library and the RainSense real-rainfall perception dataset [44].”
Self-citation
- Authors
- Xia, T., Yang, X., Chen, T., Zhang, L., Ye, S., & Chen, J.
- Affiliations
- Tongji University (CN)
- Tongji University
- Venue
- SAE Technical Paper Series
- Publisher
- SAE International
- Published
- 2025-12-31
- Volume
- 1
- ISSN
- 0148-7191 (ISSN-L)
- 2688-3627
- OpenAlex
- W7118993095
- Semantic Scholar
- a8b2320dc6a4d039a0f3fc96ac388b51673ce22c
- Cited by
- 0
- References
- 25
- FWCI
- 0.00
- Type
- conference paper
- Open access
- closed
- Locations
- SAE technical papers on CD-ROM/SAE technical paper series (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Physics and Astronomy › Instrumentation › Advanced Optical Sensing Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- autonomous driving, rainfall, rainfall intensity, environmental perception, sensor performance degradation, safety of the intended functionality, LiDAR, radar, disdrometer, bounding box annotation, adverse weather, benchmark dataset
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