AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This is an incremental implementation for autonomous driving personalization using stress recognition, aimed at improving safety and comfort for drivers and passengers.
The paper presents an AI-as-a-Service toolkit for autonomous driving that personalizes driving based on automatic driver stress recognition, implementing it as a Cyber-Physical System of Systems with data collection from wearables and cameras. The system was tested in the CARLA driving simulator using deep neural networks, recurrent neural networks, and reinforcement learning algorithms.
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.