This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.
@article{arxiv.2202.01645,
title = {AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving},
author = {Valerio De Caro and Saira Bano and Achilles Machumilane and Alberto Gotta and Pietro Cassará and Antonio Carta and Rudy Semola and Christos Sardianos and Christos Chronis and Iraklis Varlamis and Konstantinos Tserpes and Vincenzo Lomonaco and Claudio Gallicchio and Davide Bacciu},
journal= {arXiv preprint arXiv:2202.01645},
year = {2022}
}