Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception. However, due to the much higher noise than LiDAR, manufacturers choose different filtering strategies, resulting in an inverse ratio between noise level and point cloud density. There is still a lack of comparative analysis on which method is beneficial for deep learning-based perception algorithms in autonomous driving. One of the main reasons is that current datasets only adopt one type of 4D radar, making it difficult to compare different 4D radars in the same scene. Therefore, in this paper, we introduce a novel large-scale multi-modal dataset featuring, for the first time, two types of 4D radars captured simultaneously. This dataset enables further research into effective 4D radar perception algorithms.Our dataset consists of 151 consecutive series, most of which last 20 seconds and contain 10,007 meticulously synchronized and annotated frames. Moreover, our dataset captures a variety of challenging driving scenarios, including many road conditions, weather conditions, nighttime and daytime with different lighting intensities and periods. Our dataset annotates consecutive frames, which can be applied to 3D object detection and tracking, and also supports the study of multi-modal tasks. We experimentally validate our dataset, providing valuable results for studying different types of 4D radars. This dataset is released on https://github.com/adept-thu/Dual-Radar.

翻译：在自动驾驶环境感知中，相较于广泛采用的相机与激光雷达，雷达在恶劣场景下具有更强的适应性。与常见的3D雷达相比，最新的4D雷达具备精准的垂直分辨率与更高的点云密度，使其成为复杂环境感知中极具潜力的传感器。然而，由于噪声水平远高于激光雷达，制造商采用不同的滤波策略，导致噪声水平与点云密度呈反比关系。目前尚缺乏针对哪种方法更有利于基于深度学习的自动驾驶感知算法的对比分析，主要原因在于现有数据集仅采用单一种类的4D雷达，难以在同一场景下对不同4D雷达进行比较。为此，本文首次提出一个新型大规模多模态数据集，同步采集两种类型的4D雷达数据，为有效4D雷达感知算法的进一步研究提供了基础。本数据集包含151个连续序列，其中大部分持续20秒，总计10,007帧经精细同步与标注的数据。此外，数据集涵盖多种具有挑战性的驾驶场景，包括不同路况、天气条件、夜间与白天以及不同光照强度与时段。数据集对连续帧进行标注，可应用于3D目标检测与跟踪，同时支持多模态任务研究。我们通过实验验证了该数据集的价值，为不同类型4D雷达的研究提供了重要参考。本数据集已发布于https://github.com/adept-thu/Dual-Radar。