TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
This dataset addresses the need for high-resolution 4D radar data in autonomous driving research, but it is incremental as it primarily offers a new resource without major methodological breakthroughs.
The authors introduced TJ4DRadSet, a dataset with 7757 synchronized frames of 4D radar data for autonomous driving, providing 3D bounding box annotations and a baseline for 3D object detection to demonstrate deep learning effectiveness.
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.