Special Session 8

During recent years, the interest of scientific literature in the assessment of drivers’ behavior and their effective interaction with advanced onboard vehicle technology, for improved safety and better driving experience, has increased remarkably, as shown, for example, by the different sensing technologies and methods proposed to measure the stress on drivers. Also for the automotive industry, during the design phase, it is important to evaluate different car setups in order to match the driver’s preference and provide confidence to the driver. Moreover, with the introduction of ADAS (Advanced Driver-Assistance Systems), it is important to measure how the ADAS algorithms are perceived as reliable and safe by the users. As another aspect, the automatic measurement and detection of drowsiness on drivers is a very important branch of research, which involves, but is not limited to, measurements of bio-signals on drivers. In the last decade, driver attention, fatigue, drowsiness, arousal and stress have been quantified by means of eye tracking systems (pupil diameter, eye blink, and eyelid), electrocardiogram (ECG), electrodermal activity (EDA), but also by proper combinations of sensors capturing the way the driver interacts with the vehicle. Recently, most of papers combine these signals to train artificial intelligence (AI) or machine learning (ML) algorithms to quantify the level of stress on drivers. In this scenario, research contributions in the field of novel and accurate sensors development, sensor fusion approaches, and the application of new methods and ML techniques to stress/attention/drowsiness measurement on drivers, are 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,...);
• 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 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. in Electronic engineering from University of Parma, Parma, Italy in 2003. In 2007 he got the Ph. D. degree in Information Technologies from the same University. Since 2009 he joined the Electrical and Mechanical Engineering department at University of Udine, Udine, Italy. Currently he is associate professor of Electrical Measurements and Sensors at the Polytechnic Department of Engineering and Architecture of the University of Udine. His scientific interests are in the fields of wearable sensors, industrial sensors and lab-on-chip.

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) and wearable sensors for the extraction of measurement signals applied to human monitoring, fall detection, motion-related measurements, action recognition. She co-authored more than 230 papers in international peer reviewed journals and conference proceedings. Susanna 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.

Andrea Amidei, Graduate Student Member, IEEE, received his M.Sc. degree in Electronic Engineering from the University of Modena and Reggio Emilia, Italy, in 2020. He is currently working on his Ph.D. in Automotive Engineering for Intelligent Mobility under the supervision of Prof. P. Pavan at DIEF department University of Modena and Reggio Emilia. His research interests include the design of driver monitoring systems using biosignals to prevent dangerous behaviors during driving.