SMART-Rain: A Degradation Evaluation Dataset for Autonomous Driving in Rain
Chen Zhang, Zefan Huang, Hongliang Guo, Lei Qin, Marcelo H. Ang, Daniela Rus
Abstract
Autonomous driving in the rain remains a challenge. One main problem is performance degradation caused by rain. This work introduces a new dataset to study this problem. Our dataset is collected from a full-scale vehicle equipped with a 3D LiDAR sensor and multiple forward-facing cameras under various rainy conditions. In addition, rainfall intensity is recorded in real-time from a rain sensor. The combination of sensor and rainfall intensity measurement is designed for studying algorithm performance under different levels of rainfall. In this work, in addition to presenting dataset creation details, we also introduce three degradation evaluation tasks with baseline results, including rainfall intensity estimation, LiDAR degradation estimation, and 2D object detection evaluation. This dataset, development kit, and baseline codes will be made available at https://smart-rain-dataset.github.io/
BibTeX
@inproceedings{iros2023_smartrainadegrad,
title = {SMART-Rain: A Degradation Evaluation Dataset for Autonomous Driving in Rain},
author = {Chen Zhang and Zefan Huang and Hongliang Guo and Lei Qin and Marcelo H. Ang and Daniela Rus},
booktitle = {IROS 2023},
year = {2023}
}