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Authors: Daniel Bogdoll 1 ; 2 ; Felix Schreyer 2 and J. Marius Zöllner 1 ; 2

Affiliations: 1 FZI Research Center for Information Technology, Germany ; 2 Karlsruhe Institute of Technology, Germany

Keyword(s): Autonomous Driving, Data Set, Overview, Collection.

Abstract: Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this development. All the more striking is the fact that researchers do not have a tool available that provides a quick, comprehensive and up-to-date overview of data sets and their features in the domain of autonomous driving. In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets. The tool enables users to sort and filter the data sets according to currently 16 different categories. ad-datasets is an open-source project with community contributions. It is in constant development, ensuring that the content stays up-to-date.

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Paper citation in several formats:
Bogdoll, D.; Schreyer, F. and Zöllner, J. (2022). Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-573-9; ISSN 2184-495X, SciTePress, pages 46-56. DOI: 10.5220/0011001900003191

@conference{vehits22,
author={Daniel Bogdoll. and Felix Schreyer. and J. Marius Zöllner.},
title={Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2022},
pages={46-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011001900003191},
isbn={978-989-758-573-9},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving
SN - 978-989-758-573-9
IS - 2184-495X
AU - Bogdoll, D.
AU - Schreyer, F.
AU - Zöllner, J.
PY - 2022
SP - 46
EP - 56
DO - 10.5220/0011001900003191
PB - SciTePress