Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation
This addresses safety and efficiency issues for autonomous vehicles by extending their field of view through infrastructure-based sensing.
The authors tackled the problem of limited environmental perception in autonomous vehicles by proposing and implementing a hardware and software architecture for an Intelligent Infrastructure System that creates an accurate digital twin of a highway stretch, enhancing vehicle perception beyond on-board sensor limits.
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.