TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

• URL: http://arxiv.org/abs/2204.13483v2
• Date: Sat, 30 Apr 2022 06:15:11 GMT
• Title: TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
• Authors: 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 summary: We introduce an autonomous driving dataset named TJ4DRadSet, including multi-modal sensors that are 4D radar, lidar, camera and sequences with about 40K frames in total. 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.
• Score: 16.205201694162092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The new generation of 4D high-resolution imaging radar provides not only a huge amount of point cloud but also additional elevation measurement, which has a great potential of 3D sensing in autonomous driving. In this paper, we introduce an autonomous driving dataset named TJ4DRadSet, including multi-modal sensors that are 4D radar, lidar, camera and GNSS, with about 40K frames in total. 7757 frames within 44 consecutive sequences in various driving scenarios are well annotated with 3D bounding boxes and track id. 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.

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