AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

TL;DR

This paper introduces an AI-as-a-Service toolkit for autonomous driving that personalizes the experience based on stress recognition, utilizing sensor data and advanced learning algorithms within a simulated environment.

Contribution

It develops a novel AI toolkit integrating stress recognition and personalization for autonomous driving, tested in a cost-effective simulation environment.

Findings

Feasibility demonstrated in CARLA simulation

Effective stress recognition from sensor data

Personalization system adapts to driver stress levels

Abstract

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.