SPECIAL SESSION #03

Antonio Affanni

University of Udine, Italy

• antonio.affanni@uniud.it

Gianluca Ciattaglia

Polytechnic University of Marche, Italy

• g.ciattaglia@staff.univpm.it

Susanna Spinsante

Polytechnic University of Marche, Italy

• s.spinsante@staff.univpm.it

In recent years, scientific literature has shown a remarkable increase in interest regarding the assessment of driver behavior and their effective interaction with advanced onboard vehicle technology. This focus aims for improved safety and a better driving experience, evidenced by the diverse sensing technologies and methods proposed to measure driver stress. Similarly, for the automotive industry, it is crucial during the design phase to evaluate different car setups to match driver preferences, for example in terms of acoustic comfort, and instill confidence. Furthermore, with the advent of Advanced Driver-Assistance Systems (ADAS), it's important to gauge how users perceive the reliability and safety of ADAS algorithms.

Another significant area of research is the automatic measurement and detection of driver drowsiness, which includes, but is not limited to, the measurement of driver bio-signals. Over the past decade, driver attention, fatigue, drowsiness, arousal, and stress have been quantified using eye-tracking systems (pupil diameter, eye blink, and eyelid), electrocardiograms (ECG), electrodermal activity (EDA), contactless techniques based, for example, on radar sensors, and also through strategic combinations of sensors that capture how the driver interacts with the vehicle. More recently, many studies combine these signals to train artificial intelligence (AI) or machine learning (ML) algorithms to quantify driver stress levels.

In this scenario, research contributions focusing on the development of novel and accurate sensors, sensor fusion approaches, and the application of new methods and ML techniques for measuring driver stress, attention, and drowsiness are particularly welcome in this special session.

Topics of interest for this Special Session include, but are not limited to:

• design of sensors for drivers' stress measurement using bio-signals (ECG, EDA, EEG, PPG,...);
• contactless sensors and techniques to monitor the driver;
• sensors, algorithms and methods to assess the driving environment and effects on the driver’s performance;
• design of sensors and systems for drivers' drowsiness measurement;
• design of sensors and systems for drivers' attention measurement;
• on-vehicle and on-driver sensors fusion to reliably measure driver’s conditions;
• methods and approaches to combine sensors for understanding driver-vehicle interaction;
• sensors and systems to measure the impact of vehicle and driving conditions (acoustic comfort, vibrations and noise reduction) on the driver;
• implementation of AI and data fusion algorithms applied to drivers for stress detection;
• implementation of AI and data fusion algorithms applied to drivers for drowsiness detection.

Antonio Affanni received the M.S. degree in Electronic Engineering and the Ph.D. in Information Technologies from the University of Parma, Parma, Italy in 2003 and 2007, respectively. Since 2009 he joined the Polytechnic Department of Engineering and Architecture of the University of Udine, Udine, Italy. He is currently Associate Professor at the University of Udine, Italy. His research interests include the development and characterization of wearable sensors for bio-signals acquisition and the signal processing for stress detection in individuals.

Gianluca Ciattaglia received the bachelor’s and master’s degrees in electronic engineering from Università Politecnica delle Marche, Ancona, Italy, in 2014 and 2017, respectively, where he pursued the Ph.D. degree in information engineering in 2021. In 2018, he joined Ferrari S.p.A. Gestione Sportiva, Maranello, Italy, as an Electronic Support Engineer for Formula 1 test and support team. From January 2022 to August 2024, he has been a Research Fellow with Università Politecnica delle Marche where now he is Assistant Professor of electrical and electronics measurements. His work focuses on radar signal processing techniques and measurements with radar sensors. His scientific activity is related to develop measurement techniques and methods based on radar sensors.

Susanna Spinsante is currently an Associate Professor of Electrical and Electronic Measurements at the Information Engineering Department (DII) of the Polytechnic University of Marche, Italy. She received her PhD in Electronics and Telecommunications Engineering in 2005 from the same University. Since 2012 her research interests are focused on the use of ambient (RGB-D, radar) and wearable sensors for the extraction of measurement signals applied to human monitoring, fall detection, motion-related measurements, action recognition. She is a Senior Member of the IEEE since 2013, member of the IEEE Instrumentation and Measurement Society, GMEE, and CNIT. She servers as Associate Editor for the IEEE Transactions on Instrumentation and Measurements, and Measurement by Elsevier.