Special Session 6

Making machines learn how to solve problems rather than directly hard coding sequential instructions is a common research practice nowadays. Indeed, Artificial Intelligence (AI) has demonstrated the ability to outperform "classic" sequential algorithms in a variety of research areas, particularly when the system's precise model is unknown. In this context, AI represents a valuable opportunity for the automotive industry's future, as its capabilities extend far beyond the popular imagery of self-driving cars, with applications in design, manufacturing, supply chain, and customer experience. Smart, intelligent, and distributed measurement systems will undoubtedly play a key role in this context. On the production side, the Industry 4.0 paradigm has now introduced a novel integrated and flexible approach, in which measurements can be taken by precise IoT-based sensor networks, which can undoubtedly benefit from AI. In addition, AI can be used to improve other intelligent measurement systems (aiming, for example, to defects detection or additive manufacturing). Artificial intelligence may aid data analysis from a metrological standpoint, pointing to a more interconnected factory where levels of the CIM pyramid exchange information. Customer satisfaction analysis and demand prediction are two examples of applications that integrate design, supply chain, production, and customer service. Finally, AI-enhanced measurement systems onboard modern driver assistance systems provide a significant opportunity to improve safety, reliability, and driving experience.

The special session aims to analyze the usage of AI in measurement and metrology for automotive systems: directly in vehicles but also for production, design, supply chain, or customer experience. 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;
• Sensors for biometrical driver measurement;
• 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.

Dr. Tommaso Fedullo (Student Member, IEEE), 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 sensor 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. He has been a Track Chair of a Special Session for the 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive 2021), he serves in the TPC of MetroAutomotive 2022, and he is a Track Chair of a Special Session for the 2022 IEEE International Instrumentation & Measurement Technology Conference.

Dr. Alberto Morato National Research Council of Italy, Padova, Italy, received the Master degree in automation engineering from the University of Padova, Padova, Italy, in 2017. Currently, he is working toward the Ph.D. degree in Information Engineering at the University of Padova. Since October 2021, he is a research fellow with the National Research Council of Italy (CNR), Institute of Electronics and Computer and Telecommunications Engineering (IEIIT). His research activity is focused on Functional Safety Communication Networks, Real-Time Communications, and Industrial Internet of Things (IIoT). Recently his research interest has also extended to time-critical and smart measurement systems, wireless sensors networks, and Artificial Intelligence. He has professional expertise in a variety of fields, including real-time embedded systems based on DSPs, microcontrollers, and SoCs as well as the design and development of communication protocols. In 2021 he has been Track chair of a Special Session for the 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive 2021).