Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autononous Driving.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-13 DOI:10.1038/s41597-025-04698-2

Xinyu Zhang, Li Wang, Jian Chen, Cheng Fang, Guangqi Yang, Yichen Wang, Lei Yang, Ziying Song, Lin Liu, Xiaofei Zhang, Bin Xu, Zhiwei Li, Qingshan Yang, Jun Li, Zhenlin Zhang, Weida Wang, Shuzhi Sam Ge

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引用次数: 0

Abstract

4D radar has higher point cloud density and precise vertical resolution than conventional 3D radar, making it promising for adverse scenarios in the environmental perception of autonomous driving. However, 4D radar is more noisy than LiDAR and requires different filtering strategies that affect the point cloud density and noise level. Comparative analyses of different point cloud densities and noise levels are still lacking, mainly because the available datasets use only one type of 4D radar, making it difficult to compare different 4D radars in the same scenario. We introduce a novel large-scale multi-modal dataset that captures both types of 4D radar, consisting of 151 sequences, most of which are 20 seconds long and contain 10,007 synchronized and annotated frames. Our dataset captures a variety of challenging driving scenarios, including multiple road conditions, weather conditions, different lighting intensities and periods. It supports 3D object detection and tracking as well as multi-modal tasks. We experimentally validate the dataset, providing valuable insights for studying different types of 4D radar.

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与传统的 3D 雷达相比，4D 雷达具有更高的点云密度和精确的垂直分辨率，因此有望用于自动驾驶环境感知中的不利场景。然而，4D 雷达比激光雷达噪声更大，需要采用不同的滤波策略，从而影响点云密度和噪声水平。目前还缺乏对不同点云密度和噪声水平的比较分析，这主要是因为现有的数据集只使用了一种类型的四维雷达，因此很难对同一场景中的不同四维雷达进行比较。我们介绍了一种新型大规模多模态数据集，该数据集可捕捉两种类型的四维雷达，由 151 个序列组成，其中大部分序列长 20 秒，包含 10,007 个同步和注释帧。我们的数据集捕捉了各种具有挑战性的驾驶场景，包括多种路况、天气条件、不同的照明强度和时段。它支持三维物体检测和跟踪以及多模式任务。我们对数据集进行了实验验证，为研究不同类型的 4D 雷达提供了宝贵的见解。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.