This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in large-scale benchmarks that accelerate advancements in perception tasks for autonomous vehicles. The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. It also highlights the key challenges such as domain shift, sensor setup limitations, and gaps in dataset diversity and availability. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation. The conclusion underscores the necessity for comprehensive, globally accessible datasets and collaborative efforts from both technological and research communities to overcome these challenges and fully harness the potential of autonomous driving.

翻译：本综述全面审视了车-路协同（V2I）、车-车协同（V2V）及车-万物互联（V2X）场景下的协同感知数据集。文章重点阐述了近期大规模基准数据集的最新进展，这些数据集加速了自动驾驶感知任务的技术突破。本文系统分析了多种数据集，从多样性、传感器配置、数据质量、公开可用性及下游任务适用性等维度进行了横向比较。同时，重点讨论了域偏移、传感器配置局限、数据集多样性与可用性缺口等关键挑战。在数据共享与数据集创建过程中，强调了保护隐私与保障安全的重要性。最后，研究指出构建全面、全球可访问的数据集，并推动技术界与研究界的协同合作，是克服上述挑战、充分释放自动驾驶潜力的关键。