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

Krämmer, Annkathrin and Schöller, Christoph and Gulati, Dhiraj and Lakshminarasimhan, Venkatnarayanan and Kurz, Franz and Rosenbaum, Dominik and Lenz, Claus and Knoll, Alois (2022) Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation. Journal of Field Robotics. Wiley. ISSN 1556-4959.

[img] PDF - Preprint version (submitted draft)


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.

Item URL in elib:https://elib.dlr.de/135631/
Document Type:Article
Title:Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Krämmer, AnnkathrinFortissUNSPECIFIED
Schöller, ChristophFortissUNSPECIFIED
Gulati, DhirajFortissUNSPECIFIED
Lakshminarasimhan, VenkatnarayananFortissUNSPECIFIED
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004
Rosenbaum, DominikDominik.Rosenbaum (at) dlr.deUNSPECIFIED
Lenz, Clauslenz (at) cognitionfactory.comUNSPECIFIED
Knoll, AloisTechnische Universität MünchenUNSPECIFIED
Date:February 2022
Journal or Publication Title:Journal of Field Robotics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:—Intelligent infrastructure system, autonomous driving, sensor system, data fusion, digital twin, extended environmental perception, evaluation with aerial images
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Kurz, Dr.-Ing. Franz
Deposited On:14 Dec 2021 11:07
Last Modified:01 Mar 2022 03:00

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