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:由于在认识和与复杂的交通环境互动方面能力有限,自动驾驶的单一车辆智能的进步遇到了重大挑战。合作驾驶自动化~(CDA)被认为是下一代自动化驾驶和智能运输的关键解决办法。虽然CDA吸引了学术界和工业界的极大关注,但其潜力的探索仍然处于萌芽阶段。在工业方面,公司倾向于建立内部数据收集管道和研究工具,以适应其需要和保护知识产权。然而,重新发明轮子,浪费资源,并限制已开发的相机方法的通用性,因为没有标准化基准。另一方面,在学术界,由于缺乏真实世界交通数据和计算资源,研究人员经常在简化和大多模拟的环境中调查CDA专题,将研究成果推广到现实世界情景的可能性受到限制。因此,迫切需要建立开放源生态系统~(OSE)以满足不同社区对CDA研究的需求,特别是在早期探索研究阶段,并提供桥梁,以确保综合开发和测试各不同社区可以共享的管道。在本文中,我们介绍了OCDA研究模型的模型、大规模SA研究情景,我们用OSE模型演示了O-SA模型的模型模型, 并展示了OIS-SA模型的模型的模型模型模型模型模型,我们用了O-SE-SE-SA的模型模型模型模型的模型的模型的模型模型的模型的模型模型模型模型的模型的模型的模型的模型的模型的模型模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型,还展示了。