  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.
