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LiDARs are one of the main sensors used for autonomous driving applications, providing accurate depth estimation regardless of lighting conditions. However, they are severely affected by adverse weather conditions such as rain, snow, and fog.

This dataset provides semantic labels for a subset of the Road Spray dataset, which contains scenes of vehicles traveling at different speeds on wet surfaces, creating a trailing spray effect. We provide semantic labels for over 200 dynamic scenes, labeling each point in the LiDAR point clouds as background (road, vegetation, buildings, ...), foreground (moving vehicles), and noise (spray, LiDAR artifacts).

Citation

@ARTICLE{10143263,
  author={Piroli, Aldi and Dallabetta, Vinzenz and Kopp, Johannes and Walessa, Marc and Meissner, Daniel and Dietmayer, Klaus},
  journal={IEEE Robotics and Automation Letters}, 
  title={Energy-Based Detection of Adverse Weather Effects in LiDAR Data}, 
  year={2023},
  volume={8},
  number={7},
  pages={4322-4329},
  doi={10.1109/LRA.2023.3282382}}