Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset. We provide comprehensive evaluations using state-of-the-art cooperative perception methods and publicly release the codebase, dataset, HD map, and a digital twin of the complete data collection environment.

翻译：近期的协同感知数据集通过促进智能体之间的信息交换，在推进智能出行应用中发挥了关键作用，有助于克服诸如遮挡等挑战并提升整体场景理解。尽管一些现有的真实世界数据集同时包含了车对车和车对基础设施的交互，但它们通常局限于单个交叉路口或单一车辆。一个包含多个互联车辆和基础设施传感器、跨越数个交叉路口的综合性感知数据集仍然缺失，这限制了算法在多样化交通环境中的基准测试。因此，可能出现模型过拟合，并且由于交叉路口布局和交通参与者行为的相似性，模型可能表现出误导性的高性能。为弥补这一空白，我们推出了UrbanIng-V2X，这是首个支持协同感知的大规模多模态数据集，涉及部署在德国因戈尔施塔特三个城市交叉路口的车辆和基础设施传感器。UrbanIng-V2X包含34条时间对齐且空间校准的传感器序列，每条序列持续20秒。所有序列均包含来自三个交叉路口之一的记录，涉及两辆车和多达三个基础设施安装的传感器杆在协同场景下的运行。总体而言，UrbanIng-V2X提供了来自12个车载RGB摄像头、2个车载激光雷达、17个基础设施热成像摄像头和12个基础设施激光雷达的数据。所有序列均以10 Hz的频率进行标注，包含13个物体类别的3D边界框，整个数据集共计约71.2万个标注实例。我们使用最先进的协同感知方法进行了全面评估，并公开了代码库、数据集、高清地图以及完整数据采集环境的数字孪生。