Abstract
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.
Abstract (translated)
这项调查对车辆与基础设施（V2I）、车辆与车辆（V2V）和车辆与一切（V2X）背景下的协作感知数据集进行全面评估。它突出了在自动驾驶车辆感知任务方面推动进展的大型基准测试的最新发展。论文系统地分析了各种数据集，根据多样性、传感器设置、质量、公共可用性和它们对下游任务的适用性等方面进行比较。还强调了领域转移、传感器设置限制和数据集多样性和可用性之间的关键挑战。关于在数据集开发过程中解决隐私和安全问题的重要性进行了强调，涉及数据共享和数据创建。结论强调了在自动驾驶车辆的发展过程中，需要全面、全球可访问的数据和来自技术和研究社区的协作努力，以克服这些挑战并充分利用自动驾驶技术的潜力。
URL
https://arxiv.org/abs/2404.14022
