Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation.

翻译：单车智能自动驾驶技术在感知与复杂交通环境交互方面因能力有限而面临显著挑战。协同驾驶自动化（CDA）被视为下一代自动驾驶与智能交通的关键解决方案。尽管CDA已引起学术界和工业界的广泛关注，但其潜力探索仍处于初级阶段。在工业界，企业倾向于构建内部数据采集流程和研究工具以满足自身需求并保护知识产权。然而，重复开发浪费资源且因缺乏标准化基准而限制了所开发方法的可推广性。另一方面，在学术界，由于缺乏真实交通数据和计算资源，研究者通常仅在简化且多为模拟的环境中进行CDA课题研究，这阻碍了研究成果向现实场景的规模化应用。因此，亟需建立开源生态系统（OSE），以满足不同社区在CDA研究（尤其是早期探索性研究阶段）中的需求，并搭建桥梁确保各社区能够共享集成化开发与测试流程。本文介绍了OpenCDA研究生态系统——一个统一的OSE，集成模型库、多分辨率驾驶模拟器套件、大规模真实及模拟数据集、用于基准训练/测试的完整开发工具包以及场景数据库/生成器。我们还通过示例用例（包括协同3D激光雷达检测、协同并道、基于摄像头的协同地图预测及对抗场景生成）展示了OpenCDA OSE的有效性。