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：雷达在自动驾驶环境感知中相比广泛采用的摄像头和激光雷达具有更强的恶劣场景适应性。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和更高的点云密度，使其成为复杂环境感知中极具前景的自动驾驶传感器。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成反比关系。目前缺乏针对何种滤波策略更有利于自动驾驶中基于深度学习的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，难以在同一场景下比较不同4D雷达的性能。为此，本文首次提出一个新型大规模多模态数据集，该数据集同步采集两种类型的4D雷达数据，为深入研究有效4D雷达感知算法提供了基础。本数据集包含151个连续序列，大多数序列持续20秒，共计10,007帧经过严格同步标注的数据。此外，数据集覆盖了多种具有挑战性的驾驶场景，包括不同道路条件、天气状况以及不同光照强度和时段的夜间与日间场景。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有效性，为研究不同类型4D雷达提供了有价值的结果。该数据集已在 https://github.com/adept-thu/Dual-Radar 发布。