Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/1/hfm20798_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/2/hfm20798.pd

An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs

Critical Analysis on the NHTSA Acceptance Criteria for In-Vehicle Electronic Devices

We tested a commercial in-car navigation system prototype against the NHTSA criteria for acceptance testing of in-vehicle electronic devices, in order to see what types of in-car tasks fail the acceptance test and why. In addition, we studied the visual demands of the driving scenario recommended by NHTSA for task acceptance testing. In the light of the results, NHTSA guidelines and acceptance criteria need to be further developed. In particular visual demands of the driving scenario and for different simulators need to be standardized in order to enable fair testing and comparable test results. We suggest the visual occlusion method for finding a driving scenario that corresponds better with real-life driving in visual demands as well as for standardizing the visual demands of the scenario when applied to different driving simulators. Furthermore, the acceptance criteria need to be re-evaluated. Especially the TEORT limit's applicability to a variety of test tasks needs to be validated and exceptions for certain task types considered. The utility of the average glance duration criterion should be reconsidered.peerReviewe