Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving

authored by: Colin Fischer, Monika Sester, Steffen Schön
Abstract: In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.
Organisation(s): Graduiertenkolleg 2159: Integrität und Kollaboration in dynamischen Sensornetzen
Institute of Geodesy
Institute of Cartography and Geoinformatics
Leibniz Research Centre FZ:GEO
Type: Article
Journal: ISPRS International Journal of Geo-Information
Volume: 9
ISSN: 2220-9964
Publication date: 25.10.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geography, Planning and Development, Computers in Earth Sciences, Earth and Planetary Sciences (miscellaneous)
Electronic version(s): https://doi.org/10.3390/ijgi9110626 (Access: Open)
https://doi.org/10.15488/12695 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{7e7fdac481944d8494d365a2389433ed,
title = "Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving",
abstract = "In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks. ",
keywords = "Data management, Internet GIS, Metadata, Spatial database, Spatio-temporal data infrastructure",
author = "Colin Fischer and Monika Sester and Steffen Sch{\"o}n",
note = "Funding Information: An example for such a complex research project is a research training group (RTG) funded by the German Science Foundation, entitled “Integrity and collaboration in dynamic sensor networks” (GRK2159). This RTG investigates concepts for ensuring the integrity of collaborative systems in dynamic sensor networks in the context of autonomous driving and environmental perception [2]. The exploitation of different—collaborating—sensors, in conjunction with new and advanced concepts of describing the integrity of measurements is considered an important key to ultimately allow a safe interplay of autonomous systems and human beings. The project relies on the assumption that the collaboration of diverse sensors and sensor systems leads to an improvement of the navigation and the sensing of the environment by an autonomous system. The project relies on large-scale Funding Information: This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (RTG2159). Acknowledgments: We thank the Leibniz University's Department for IT services (LUIS) for their ongoing support of our IT infrastructure. Funding Information: The RTG hosts nine PhD candidates at a time in 3-year periods over a maximum funding period of nine years, leading to nearly 30 PhD researchers funded by the program. One of the pillars of the RTG is the continuous collection of experimental data, leading to a large pool of spatio-temporal datasets that can be integrated in arbitrary ways, this way supporting a rich variety of different research",
year = "2020",
month = oct,
day = "25",
doi = "10.3390/ijgi9110626",
language = "English",
volume = "9",
journal = "ISPRS International Journal of Geo-Information",
issn = "2220-9964",
publisher = "MDPI AG",
number = "11",
}