The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data.
翻译:对自主车辆的环境认识受到限制,因为其物理感应范围和算法性能,以及使其对当前交通状况的理解不尽人意的封闭性,这不仅对安全构成重大威胁,限制驾驶速度,而且可能导致不便的操作。智能基础设施系统有助于缓解这些问题。智能基础设施系统可以填补车辆感知的空白,扩大其视野,以数字模型的形式,以目前交通状况(即数字双胞胎)的形式,提供关于其周遭的额外详细信息。然而,对这种系统和工作原型的详细说明表明其可行性是很少的。我们在本文件中提出一个硬件和软件结构,使这样一个可靠的智能基础设施系统能够建立起来。我们已在现实世界中实施了这一系统,并展示了该系统在扩大的公路承载线上创建准确数字双子的能力,从而提高了自主车辆超越其机载感应的视野。此外,我们通过使用航空图像和地球观测方法生成地面真相数据,评估数字双的准确性和可靠性。