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The environmental perception of an autonomous vehicle is limited by itsphysical sensor ranges and algorithmic performance, as well as by occlusionsthat degrade its understanding of an ongoing traffic situation. This not onlyposes a significant threat to safety and limits driving speeds, but it can alsolead to inconvenient maneuvers. Intelligent Infrastructure Systems can help toalleviate these problems. An Intelligent Infrastructure System can fill in thegaps in a vehicle's perception and extend its field of view by providingadditional detailed information about its surroundings, in the form of adigital model of the current traffic situation, i.e. a digital twin. However,detailed descriptions of such systems and working prototypes demonstratingtheir feasibility are scarce. In this paper, we propose a hardware and softwarearchitecture that enables such a reliable Intelligent Infrastructure System tobe built. We have implemented this system in the real world and demonstrate itsability to create an accurate digital twin of an extended highway stretch, thusenhancing an autonomous vehicle's perception beyond the limits of its on-boardsensors. Furthermore, we evaluate the accuracy and reliability of the digitaltwin by using aerial images and earth observation methods for generating groundtruth data.

Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation