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, the 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：相比广泛使用的摄像头和激光雷达，雷达在自动驾驶环境感知的恶劣场景中具有更强的适应性。与常用的3D雷达相比，最新的4D雷达具备精确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。目前仍缺乏对哪种滤波策略更有利于基于深度学习的自动驾驶感知算法的对比分析，主要原因之一是现有数据集仅采用单一类型的4D雷达，难以在同一场景中对不同4D雷达进行比较。为此，本文首次提出一种新颖的大规模多模态数据集，同时采集两种类型的4D雷达数据，从而为研究高效4D雷达感知算法奠定基础。该数据集包含151个连续序列，大多数序列持续20秒，共计10,007帧经过精心同步与标注的数据。此外，数据集采集了多种具有挑战性的驾驶场景，涵盖不同道路条件、天气状况、夜间与日间不同光照强度与时段。数据集对连续帧进行标注，可应用于三维目标检测与跟踪，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。该数据集发布于https://github.com/adept-thu/Dual-Radar。