De Caro V., Bano S., Machumilane A., Gotta A., Cassarà P., Carta A., Semola R., Sardianos C., Chronis C., Varlamis I., Tserpes K., Lomonaco V., Gallicchio C., Bacciu D.
Recurrent neural networks AI-as-a-Service FOS: Computer and information sciences Artificial Intelligence (cs.AI) Human-Computer Interaction (cs.HC) Computer Science - Human-Computer Interaction Human state monitoring Autonomous driving Computer Science - Artificial Intelligence
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.
Source: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 91–93, Pisa, Italy, 21-25 March 2022
@inproceedings{oai:it.cnr:prodotti:471820, title = {AI-as-a-Service toolkit for human-centered intelligence in autonomous driving}, author = {De Caro V. and Bano S. and Machumilane A. and Gotta A. and Cassarà P. and Carta A. and Semola R. and Sardianos C. and Chronis C. and Varlamis I. and Tserpes K. and Lomonaco V. and Gallicchio C. and Bacciu D.}, doi = {10.1109/percomworkshops53856.2022.9767501 and 10.48550/arxiv.2202.01645}, booktitle = {PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 91–93, Pisa, Italy, 21-25 March 2022}, year = {2022} }
10.1109/percomworkshops53856.2022.9767501
10.48550/arxiv.2202.01645
TEACHING
A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence