Call for Papers: Special Section of Human Factors Journal On Assessment and Effectiveness of Driver Monitoring Systems
Call for Papers: Special Section of
Human Factors: the Journal of the Human Factors and Ergonomics Society
On
Assessment and Effectiveness of Driver Monitoring
Systems
Francesco N. Biondi1, Birsen Donmez2, Ignacio Alvarez3, William J. Horrey4, Balakumar Balasingam1
1University of Windsor, ON, Canada
2University of Toronto, ON, Canada
3Intel Corporation, WA, USA
4AAA Foundation for Traffic Safety, Washington D.C., USA
SUBMISSION DEADLINE EXTENDED TO FEBRUARY 10, 2023
Driver Monitoring Systems (DMS) aim to assess in real-time the state of the driver, including their physiological and attentional state. They are designed to detect the onset of detrimental conditions like distraction and drowsiness, and alert the driver if their attention or vigilance levels fall below set safety thresholds. The introduction of effective and accurate DMS is expected to drastically reduce the number of collisions caused by human error, with at least 140,000 fewer serious injuries expected in the European Union alone by 2038. Despite their potential, however, little is known about the effectiveness of DMS. Data on the accuracy of existing systems is sparse. Furthermore, while current DMS leverage vehicle dynamics and drivers’ behavioral patterns to determine levels of distraction and drowsiness, novel approaches that leverage the recording of physiological or neural signals are promised to increase the accuracy and effectiveness of driver state detection. The issues of drivers’ acceptance of and trust in these systems are also key, with recent investigations questioning their perceived accuracy across diverse traffic scenarios.
The proposed special section invites theoretical, methodological, and empirical efforts that explore the assessment and effectiveness of current and novel strategies for driver state detection. Topics of
interest may include, but are not limed to:
- evaluations of methodologies and systems for the monitoring of conditions like distraction and fatigue in passenger and commercial vehicles;
- novel and future tools for driver state monitoring;
- multi-modal models for driver state detection;
- safety benefit analysis of current and future DMS;
- development and validation of machine learning and computational models;
- investigation on drivers’ perception of DMS.
Deadline for submissions:
February 10, 2023
For inquiries, email francesco.biondi@uwindsor.ca