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Authors: Justin Bescop ; Nicolas Goeman ; Amel Aissaoui ; Benjamin Allaert and Jean-Philippe Vandeborre

Affiliation: IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Digital Systems, F-59000 Lille, France

Keyword(s): Autonomous Driving, Dynamic Risk Assessment, Deep Learning, Railway Dataset.

Abstract: While the automotive industry has made significant contributions to vision-based dynamic risk assessment, progress has been limited in the railway domain. This is mainly due to the lack of data and to the unavailability of security-based annotation for the existing datasets. This paper proposes the first annotation framework for the railway domain that takes into account the different components that significantly contribute to the vision-based risk estimation in driving scenarios, thus enabling an accurate railway risk assessment. A first baseline based on neural network is performed to prove the consistency of the risk-based annotation. The performances show promising results for vision-based risk assessment according to different levels of risk.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bescop, J. ; Goeman, N. ; Aissaoui, A. ; Allaert, B. and Vandeborre, J. (2024). SMART-RD: Towards a Risk Assessment Framework for Autonomous Railway Driving. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 804-811. DOI: 10.5220/0012474300003660

@conference{visapp24,
author={Justin Bescop and Nicolas Goeman and Amel Aissaoui and Benjamin Allaert and Jean{-}Philippe Vandeborre},
title={SMART-RD: Towards a Risk Assessment Framework for Autonomous Railway Driving},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={804-811},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012474300003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - SMART-RD: Towards a Risk Assessment Framework for Autonomous Railway Driving
SN - 978-989-758-679-8
IS - 2184-4321
AU - Bescop, J.
AU - Goeman, N.
AU - Aissaoui, A.
AU - Allaert, B.
AU - Vandeborre, J.
PY - 2024
SP - 804
EP - 811
DO - 10.5220/0012474300003660
PB - SciTePress

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