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.
De Caro, V.; Bano, S.; Machumilane, A.; Gotta, A.; Cassara, P.; Carta, A.; Semola, R.; Sardianos, C.; Chronis, C.; Varlamis, I.; Tserpes, K.; Lomonaco, Vincenzo; Gallicchio, C.; Bacciu, D.. (2022). AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving. In Proceedings of the 2022 IEEE International Conference on Pervasive Computing and Communications Workshops (pp. 91- 93). Isbn: 978-1-6654-1647-4. Doi: 10.1109/PerComWorkshops53856.2022.9767501.
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
Lomonaco V.;
2022
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 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.
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