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Title	Traffic Sign Recognition Robustness In Autonomous Vehicles Under Physical Adversarial Attacks
ID_Doc	58667
Authors	Apostolidis K.D.; Gkouvrikos E.V.; Vrochidou E.; Papakostas G.A.
Year	2023
Published	Studies in Computational Intelligence, 1118
DOI	http://dx.doi.org/10.1007/978-3-031-44127-1_13
Abstract	Nowadays we are all witnesses of the technological development of the so-called 4th industrial revolution (Industry 4.0). In this context, a daily living environment of smart cities is formed in which artificial intelligence applications play a dominant role. Autonomous (pilotless) vehicles are a shining example of the application of artificial intelligence, based on which vehicles are allowed to move autonomously in both residential and rural areas. The proposed article examines the robustness, in adversarial attacks in the physical layer, of the deep learning models used in autonomous vehicles for the recognition of road traffic signals. As a case study the roads of Greece, having traffic signs highly contaminated not on purpose, is considered. Towards investigating this direction, a novel dataset with clear and attacked images of traffic signs is proposed and used in the evaluation of popular deep learning models. This study investigates the level of readiness of autonomous vehicles to perform in noisy environments that affect their ability to recognize road signs. This work highlights the need for more robust deep learning models in order to make the use of autonomous vehicles a reality with maximum safety for citizens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Author Keywords	Artificial intelligence; Autonomous vehicles; Computer vision; Deep learning; Self-driving cars; Traffic sign recognition

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