  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.
