Challenges in Designing Datasets and Validation for Autonomous Driving

Michal Uřičář, David Hurych, Pavel Křížek, Senthil Yogamani

2019

Abstract

Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy (International, 2017). There are many public datasets in the academic community. However, they are far away from what a robust industrial production system needs. There is a large gap between academic and industrial setting and a substantial way from a research prototype, built on public datasets, to a deployable solution which is a challenging task. In this paper, we focus on bad practices that often happen in the autonomous driving from an industrial deployment perspective. Data design deserves at least the same amount of attention as the model design. There is very little attention paid to these issues in the scientific community, and we hope this paper encourages better formalization of dataset design. More specifically, we focus on the datasets design and validation scheme for autonomous driving, where we would like to highlight the common problems, wrong assumptions, and steps towards avoiding them, as well as some open problems.

Paper Citation

in Harvard Style

Uřičář M., Hurych D., Křížek P. and Yogamani S. (2019). Challenges in Designing Datasets and Validation for Autonomous Driving. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 653-659. DOI: 10.5220/0007690706530659

in Bibtex Style

@conference{visapp19,
author={Michal Uřičář and David Hurych and Pavel Křížek and Senthil Yogamani},
title={Challenges in Designing Datasets and Validation for Autonomous Driving},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={653-659},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007690706530659},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Challenges in Designing Datasets and Validation for Autonomous Driving
SN - 978-989-758-354-4
AU - Uřičář M.
AU - Hurych D.
AU - Křížek P.
AU - Yogamani S.
PY - 2019
SP - 653
EP - 659
DO - 10.5220/0007690706530659
PB - SciTePress