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 emulation 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.

翻译：本文件介绍了在H20教学项目内开发的AI-as-A-服务工具包的概念实施证明,该工具包旨在根据自动驱动器压力识别算法的产出实施一个自主驱动个人化系统,两者都实现了网络物理系统系统;此外,我们实施了一个数据收集子系统,从不同传感器(即穿戴器和照相机)收集数据,以使压力识别自动化;该系统是用于测试驾驶模拟软件(CARLA)的,该软件允许以最低成本测试该方法的可行性,而不会给司机和乘客带来风险;在相关子系统的核心,使用深神经网络、经常性神经网络和强化学习等不同学习算法。