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Authors: Nesrine Triki 1 ; Mohamed Ksantini 1 and Mohamed Karray 2

Affiliations: 1 National School of Engineers of Sfax, University of Sfax, Sfax, Tunisia ; 2 ESME Sudria, The Embedded and Electronic Systems Lab, Ivry Sur Seine, France

Keyword(s): Advanced Driver Assistance Systems (ADAS), Autonomous Vehicles, Traffic Sign Recognition, Belief Functions, Artificial Intelligence, Machine Learning, Image Processing.

Abstract: Advanced Driver Assistance Systems (ADAS) have a strong interest in road safety. This type of assistance can be very useful for collision warning systems, blind spot detection and track maintenance assistance. Traffic Sign Recognition system is one of the most important ADAS technologies based on artificial intelligence methodologies where we obtain efficient solutions that can alert and assist the driver and, in specific cases, accelerate, slow down or stop the vehicle. In this work, we will improve the effectiveness and the efficiency of machine learning classifiers on traffic signs recognition process in order to satisfy ADAS reliability and safety standards. Hence, we will use MLP, SVM, Random Forest (RF) and KNN classifiers on our traffic sign dataset first, each classifier apart then, by fusing them using the Dempster-Shafer (DS) theory of belief functions. Experimental results confirm that by combining machine learning classifiers we obtain a significant improvement of accurac y rate compared to using classifiers independently. (More)

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Paper citation in several formats:
Triki, N.; Ksantini, M. and Karray, M. (2021). Traffic Sign Recognition System based on Belief Functions Theory. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 775-780. DOI: 10.5220/0010239807750780

@conference{icaart21,
author={Nesrine Triki. and Mohamed Ksantini. and Mohamed Karray.},
title={Traffic Sign Recognition System based on Belief Functions Theory},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={775-780},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010239807750780},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Traffic Sign Recognition System based on Belief Functions Theory
SN - 978-989-758-484-8
IS - 2184-433X
AU - Triki, N.
AU - Ksantini, M.
AU - Karray, M.
PY - 2021
SP - 775
EP - 780
DO - 10.5220/0010239807750780
PB - SciTePress