Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS-V2V, the first-of-its-kind real-world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time-synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100-clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi-view 30 Hz camera images, along with 750K anonymized yet high-precision RTK-fixed GNSS and IMU records. Correspondingly, we provide time-consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target-based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS-V2V, the largest-scale, most supportive, and highest-quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.

翻译：车对车（V2V）协同感知通过克服复杂不利交通场景（CATS）中的感知限制，在提升自动驾驶性能方面具有巨大潜力。同时，数据是现代自动驾驶人工智能的基础设施。然而，由于数据采集要求严格，现有数据集主要关注普通交通场景，限制了协同感知的效益。为应对这一挑战，我们推出了CATS-V2V，这是首个针对复杂不利交通场景下V2V协同感知的真实世界数据集。该数据集由两辆硬件时间同步的车辆采集，覆盖10个不同地点的10种天气和光照条件。该100段剪辑的数据集包含60K帧10Hz激光雷达点云和1.26M张多视角30Hz相机图像，以及750K条匿名化但高精度的RTK固定GNSS和IMU记录。相应地，我们提供了目标物体的时间一致3D边界框标注以及静态场景，以构建4D鸟瞰图表示。在此基础上，我们提出了一种基于目标的时间对齐方法，确保所有物体在所有传感器模态间精确对齐。我们希望CATS-V2V——迄今为止同类中规模最大、支持最全面、质量最高的数据集——能够有益于自动驾驶社区的相关任务。