The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving. In this paper, we introduce a dataset named TJ4DRadSet with 4D radar points for autonomous driving research. The dataset was collected in various driving scenarios, with a total of 7757 synchronized frames in 44 consecutive sequences, which are well annotated with 3D bounding boxes and track ids. We provide a 4D radar-based 3D object detection baseline for our dataset to demonstrate the effectiveness of deep learning methods for 4D radar point clouds. The dataset can be accessed via the following link: https://github.com/TJRadarLab/TJ4DRadSet.
@article{arxiv.2204.13483,
title = {TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving},
author = {Lianqing Zheng and Zhixiong Ma and Xichan Zhu and Bin Tan and Sen Li and Kai Long and Weiqi Sun and Sihan Chen and Lu Zhang and Mengyue Wan and Libo Huang and Jie Bai},
journal= {arXiv preprint arXiv:2204.13483},
year = {2022}
}
Comments
2022 IEEE International Intelligent Transportation Systems Conference (ITSC 2022)