TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

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 res...

Full description

Saved in:

Bibliographic Details
Published in:	2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) pp. 493 - 498
Main Authors:	Zheng, Lianqing, Ma, Zhixiong, Zhu, Xichan, Tan, Bin, Li, Sen, Long, Kai, Sun, Weiqi, Chen, Sihan, Zhang, Lu, Wan, Mengyue, Huang, Libo, Bai, Jie
Format:	Conference Proceeding
Language:	English
Published:	IEEE 08.10.2022
Subjects:	Object detection Point cloud compression Radar Radar detection Radar measurements Radar tracking Three-dimensional displays
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
DOI:	10.1109/ITSC55140.2022.9922539