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...

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Vydáno v:2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) s. 493 - 498
Hlavní autoři: Zheng, Lianqing, Ma, Zhixiong, Zhu, Xichan, Tan, Bin, Li, Sen, Long, Kai, Sun, Weiqi, Chen, Sihan, Zhang, Lu, Wan, Mengyue, Huang, Libo, Bai, Jie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 08.10.2022
Témata:
Object detection
Point cloud compression
Radar
Radar detection
Radar measurements
Radar tracking
Three-dimensional displays
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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.
AbstractList 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.
Author Zhu, Xichan
Zhang, Lu
Wan, Mengyue
Li, Sen
Zheng, Lianqing
Ma, Zhixiong
Bai, Jie
Huang, Libo
Long, Kai
Chen, Sihan
Tan, Bin
Sun, Weiqi
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  givenname: Zhixiong
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  givenname: Libo
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  email: huangl@zucc.edu.cn
  organization: School of Information and Electricity, Zhejiang University City College,Hangzhou,Zhejiang,China
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  surname: Bai
  fullname: Bai, Jie
  email: baij@zucc.edu.cn
  organization: School of Information and Electricity, Zhejiang University City College,Hangzhou,Zhejiang,China
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Snippet The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential...
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StartPage 493
SubjectTerms Object detection
Point cloud compression
Radar
Radar detection
Radar measurements
Radar tracking
Three-dimensional displays
Title TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
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