Human factors of monitoring driving automation: Eyes and Scenes

This PhD thesis document is a collection of several of my published (and submitted) peer review journal articles from underneath the Human Factors of Automated Driving (HF Auto, PITN-GA-2013-605817) seventh framework program (FP7) of the European Commission. The topics include: human factors, automotive road safety, autonomous/automated driving technology, human supervisory control, adaptive automation, driver state monitoring, and scene-tied (situated) eye-based assessments of attention. Outside of the publications are summary, introduction, and conclusion chapters as well as contribution appendices to tie all the related work together. Summary: Like fatigue and distraction driving aids before, the advent of additional driving automation/autonomy poses new challenges for protecting road users now against vigilance decrements. Within the larger Human Factors of Automated Driving (HFAuto) project, the goal of this thesis was ‘to develop a system that is able to monitor the driver’s vigilance’. The approach taken was to investigate vigilance from a cognitive systems engineering (ecological perspective). Instead of conceptually restricting vigilance to be some kind of internal cognitive state/property of a driver, this thesis treated vigilance as a state/property of a system (i.e., the relationship between a driver and driving scene/situation). This thesis contains seven research papers in the form of literature reviews and experiments with eye-tracking, driving video clips, driving simulation, and on-road semi-naturalistic observation. It can be concluded form this thesis, that to develop driver monitoring systems (DMS) of driving vigilance, eye measurements (especially of movement distances) and scene contents (especially road curvatures and collision hazards) are important and relatable factors. Furthermore, it is concluded that these factors are obtainable in viable ways for future research and development application efforts. Specifically, the studies suggest means for DMS to be targeted to protect and maintain a foundational level or inner-most loop of driving attention at a behavioral level (rather than interactive implicit cognitive layers and representational experiences that can be added on top). An applied observational, data-driven, and behavioral/situated approach is expected to better avoid higher order cognitive ambiguity/dilemmas, and so serves to make more end-user acceptable DMS more tractable