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Title	A Method For Road Accident Prevention In Smart Cities Based On Deep Reinforcement Learning
ID_Doc	2571
Authors	Crincoli G.; Fierro F.; Iadarola G.; La Rocca P.E.; Martinelli F.; Mercaldo F.; Santone A.
Year	2022
Published	Proceedings of the International Conference on Security and Cryptography, 1
DOI	http://dx.doi.org/10.5220/0011146500003283
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. © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Author Keywords	Artificial Intelligence; Automotive; Deep Reinforcement Learning; Machine Learning; Smart Cities

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