SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
This addresses a data gap for researchers and developers working on perception systems in autonomous vehicles, but it is incremental as it builds on existing dataset efforts.
The authors tackled the lack of multimodal labeled datasets for autonomous driving in adverse weather by introducing SemanticSpray++, a dataset with camera, LiDAR, and radar data for wet surface conditions, providing labels and baseline evaluations.
Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both camera and LiDAR-based perception systems. Currently, it is difficult to evaluate the performance of these methods due to the lack of publicly available datasets containing multimodal labeled data. To address this limitation, we propose the SemanticSpray++ dataset, which provides labels for camera, LiDAR, and radar data of highway-like scenarios in wet surface conditions. In particular, we provide 2D bounding boxes for the camera image, 3D bounding boxes for the LiDAR point cloud, and semantic labels for the radar targets. By labeling all three sensor modalities, the SemanticSpray++ dataset offers a comprehensive test bed for analyzing the performance of different perception methods when vehicles travel on wet surface conditions. Together with comprehensive label statistics, we also evaluate multiple baseline methods across different tasks and analyze their performances. The dataset will be available at https://semantic-spray-dataset.github.io .