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Authors: Giuseppe Crincoli 1 ; Fabiana Fierro 2 ; Giacomo Iadarola 1 ; Piera Elena La Rocca 1 ; Fabio Martinelli 1 ; Francesco Mercaldo 1 and Antonella Santone 3

Affiliations: 1 Institute of Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy ; 2 Spike Reply, Milan, Italy ; 3 University of Molise, Campobasso, Italy

Keyword(s): Machine Learning, Deep Reinforcement Learning, Smart Cities, Automotive, Artificial Intelligence.

Abstract: Autonomous vehicles play a key role in the smart cities vision: they bring benefits and innovation, but also safety threats, especially if they suffer from vulnerabilities that can be easily exploited. In this paper, we propose a method that exploits Deep Reinforcement Learning to train autonomous vehicles with the purpose of preventing road accidents. The experimental results demonstrated that a single self-driving vehicle can help to optimise traffic flows and mitigate the number of collisions that would occur if there were no self-driving vehicles in the road network. Our results proved that the training progress is able to reduce the collision frequency from 1 collision every 32.40 hours to 1 collision every 53.55 hours, demonstrating the effectiveness of deep reinforcement learning in road accident prevention in smart cities.

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Paper citation in several formats:
Crincoli, G.; Fierro, F.; Iadarola, G.; Rocca, P.; Martinelli, F.; Mercaldo, F. and Santone, A. (2022). A Method for Road Accident Prevention in Smart Cities based on Deep Reinforcement Learning. In Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 513-518. DOI: 10.5220/0011146500003283

@conference{secrypt22,
author={Giuseppe Crincoli. and Fabiana Fierro. and Giacomo Iadarola. and Piera Elena La Rocca. and Fabio Martinelli. and Francesco Mercaldo. and Antonella Santone.},
title={A Method for Road Accident Prevention in Smart Cities based on Deep Reinforcement Learning},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT},
year={2022},
pages={513-518},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011146500003283},
isbn={978-989-758-590-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - A Method for Road Accident Prevention in Smart Cities based on Deep Reinforcement Learning
SN - 978-989-758-590-6
IS - 2184-7711
AU - Crincoli, G.
AU - Fierro, F.
AU - Iadarola, G.
AU - Rocca, P.
AU - Martinelli, F.
AU - Mercaldo, F.
AU - Santone, A.
PY - 2022
SP - 513
EP - 518
DO - 10.5220/0011146500003283
PB - SciTePress

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