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：近期协同感知数据集通过促进智能体间的信息交换，在推动智慧出行应用发展中发挥了关键作用，有助于克服遮挡等挑战并提升整体场景理解能力。尽管部分现有真实世界数据集同时包含车对车与车对基础设施的交互，但它们通常局限于单一交叉路口或单一车辆。目前仍缺乏涵盖多个交叉路口、包含多辆联网车辆及基础设施传感器的综合性感知数据集，这限制了对算法在多样化交通环境中的基准测试能力。因此，模型可能因交叉路口布局与交通参与者行为的相似性而出现过拟合，并表现出具有误导性的高精度。为填补这一空白，我们推出UrbanIng-V2X——首个支持车辆与基础设施传感器协同感知的大规模多模态数据集，其传感器部署于德国因戈尔施塔特市的三个城市交叉路口。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。所有序列均采集自三个交叉路口之一，涉及两辆车辆及最多三个基础设施传感器杆在协同场景下的数据。该数据集总计提供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及12个基础设施激光雷达的数据。所有序列以10赫兹频率标注了涵盖13个物体类别的3D边界框，全数据集标注实例约达71.2万个。我们采用前沿协同感知方法进行了全面评估，并公开了代码库、数据集、高精地图以及完整数据采集环境的数字孪生模型。