Special Session 12

Nowadays, making machines learn how to solve a problem, rather than directly hard coding sequential instructions, is a widespread research practice. In fact, Artificial Intelligence (AI) demonstrated the ability to outperform classic sequential algorithms in several different research areas, especially when a precise model of the system is unknown. AI hence represents a valuable opportunity for the automotive industry of the future: its capabilities extend far beyond the popular imagery of autonomous cars, with significant applications on the whole product life cycle. In this context, smart, intelligent, and distributed measurement systems play a key role. A novel integrated and flexible approach on the production side has been recently introduced with the Industry 4.0 paradigm, where measurement may be taken through accurate IoT-based sensor networks, possibly enhanced by AI technologies. Other intelligent measurement systems aiming, for instance, to defects detection or additive manufacturing, may benefit from AI as well. Pointing to more interconnected factories, AI may help to analyze data from a metrological perspective. Applications, among others, are customer satisfaction analysis and demand prediction, thus integrating design, supply chain, production, and customer assistance. Finally, onboard AI-enhanced measurement systems open a great opportunity to improve safety, reliability, and driving experience on modern driver assistance systems.

This special session would represent a host for the research about the adoption of AI-powered measurement systems for automotive systems and the automotive industry of the future at large.

Topics of interests comprise, but are not limited to:

• Computer Vision measurement systems for automotive;
• Predictive maintenance systems for automotive;
• Advanced driver assistance systems (park assistant, cruise control…);
• Obstacle detection and collision avoidance systems;
• Battery and fuel consumption optimization;
• AI solutions for an in-vehicle IoT ecosystem;
• AI-based measurements systems for smart manufacturing in Automotive Industry 4.0;
• Computer Vision techniques for defects detection;
• Intelligent Measurement systems for additive manufacturing;
• Metrology and data analysis for Demand prediction algorithms, design, and supply chain.

Tommaso Fedullo received the Master degree in Mechatronics Engineering from the University of Padova, Vicenza, Italy, in 2019. He is now a Ph.D. student in Mechatronics and Product Innovation Engineering with the Department of Management and Engineering, University of Padova, Vicenza, Italy. Moreover, he is currently working towards his research activity in the Measurements, Instrumentation and Sensors research group with the University of Modena and Reggio Emilia, in the OptoLab laboratory, Modena, Italy. His research interests include wireless sensors networks, real-time communication and Industrial Internet of Things (IIoT), applied to smart, intelligent and distributed measurement systems. Furthermore, he is particularly concerned with the application of Artificial Intelligence methods to enhance the performances of distributed measurement systems.

Alberto Morato received the Master degree in automation engineering from the University of Padova, Padova, Italy, in 2017. Since October 2018, he is with CMZ Sistemi Elettronici s.r.l. (Vascon di Carbonera, Treviso, Italy) working toward the Ph.D. degree in Information Engineering at University of Padova. His research interests include wireless sensors networks, Artificial Intelligence, Machine Learning, real-time communications and Industrial Internet of Things (IIoT) systems for smart measurement systems.

Federico Tramarin is an Associate Professor at the "Enzo Ferrari" Engineering Department of the University of Modena and Reggio Emilia, Italy. Previously, he was an Assistant Professor at the Department of Management and Engineering at the University of Padova, Italy. From 2013 to 2018, he held a post-doctoral position at the Institute of Electronics and Computer and Telecommunications (IEIIT) of the National Research Council of Italy (CNR). He obtained the Dr. Eng. degree in Electronic Engineering and the Ph.D. degree in Information Engineering from the University of Padova, Italy, in 2008 and in 2012, respectively. His main fields of interest are performance analysis and measurements on network systems, with particular focus on real-time industrial real-time wired/wireless communications, Time Sensitive Networks (TSN) and on Cyber-Physical Systems. He currently serves in the Editorial Board of several renowned international journals. He contributed to the organization, and also serves in the TPC of several IEEE international conferences, including I2MTC, ETFA, INDIN, IECON, DASC, M&N and MetroAutomotive. He is an active member of the IEEE Instrumentation & Measurement Society TC-37 on Measurement&Networking, and Industrial Electronics Society TCs on Factory Automation and Industrial Informatics.