TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
Lianqing Zheng
Zhixiong Ma
Xichan Zhu
Bin Tan
Sen Li
Kai Long
Weiqi Sun
Sihan Chen
Lu Zhang
Mengyue Wan
Libo Huang
Jie Bai

Abstract
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.
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