@inproceedings{, author = {Kr{\"{a}}mmer, Annkathrin and Sch{\"{o}}ller, Christoph and Gulati, Dhiraj and Lakshminarasimhan, Venkatnarayanan and Kurz, Franz and Rosenbaum, Dominik and Lenz, Claus and Knoll, Alois}, title = {Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation}, booktitle = {Journal of Field Robotics}, year = {2022}, month = jun, abstract = {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.}, } @inproceedings{Kraemmer2019a, author = {Kr{\"{a}}mmer, Annkathrin and Sch{\"{o}}ller, Christoph and Gulati, Dhiraj and Knoll, Alois}, title = {Providentia - A Large Scale Sensing System for the Assistance of Autonomous Vehicles}, booktitle = {Robotics Science and Systems Workshops ({RSS} Workshops)}, publisher = {RSS Foundation}, year = {2019}, month = jun, address = {Freiburg, Germany}, abstract = {The environmental perception of autonomous vehicles is not only limited by physical sensor ranges and algorithmic performance, but also occlusions degrade their understanding of the current traffic situation. This poses a great threat for safety, limits their driving speed and can lead to inconvenient maneuvers that decrease their acceptance. Intelligent Transportation Systems can help to alleviate these problems. By providing autonomous vehicles with additional detailed information about the current traffic in form of a digital model of their world, i.e. a digital twin, an Intelligent Transportation System can fill in the gaps in the vehicle's perception and enhance its field of view. However, detailed descriptions of implementations of such a system and working prototypes demonstrating its feasibility are scarce. In this work, we propose a hardware and software architecture to build such a reliable Intelligent Transportation System. We have implemented this system in the real world and show that it is able to create an accurate digital twin of an extended highway stretch. Furthermore, we provide this digital twin to an autonomous vehicle and demonstrate how it extends the vehicle's perception beyond the limits of its on-board sensors.}, keywords = {Intelligent Transportation Systems, Autonomous Driving, Robotics}, url = {https://sites.google.com/view/uad2019/accepted-posters}, } @inproceedings{buckl2014a, author = {Buckl, Christian and Geisinger, Michael and Gulati, Dhiraj and Ruiz-Bertol, Fran J.}, title = {CHROMOSOME: A Run-Time Environment for Plug\,&\,Play-Capable Embedded Real-Time Systems}, booktitle = {Sixth International Workshop on Adaptive and Reconfigurable Embedded Systems (APRES 2014)}, publisher = {ACM}, year = {2014}, month = apr, keywords = {chromosome, xme}, }