Exploiting Supervised Machine Learning for Driver Detection in a Real-World Environment

The proliferation of info-entertainment systems in today's vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. Ultra-response connectivity networks with a latency below 10 milliseconds are providing the perfect infrastructure in which this information can be sent to improve safety and security. With the purpose of providing an architecture to increase safety and security in an automotive context, we in this paper propose a method for detecting the driver in real-time exploiting supervised machine learning techniques. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.