The key findings of the eyeglance data analyses are summarized as follows:
1. Several categories of eyeglance measures proved reliable in split-half analyses of the data:
• Number of glances: o To road locations
o To situation awareness locations o To task-related areas
o To Total/All, and To Not Road (combines everything other than Road)
• Durations of glances:
o Mean (for most locations: Road, Situation Awareness, Task (was borderline), and Not Road
o Median (for some locations: Road, Situation Awareness)
o Standard deviation (for some locations: Road, Task, Total/All, Not Road) o Max (for only certain location types: Road, Task, Total/All)
• Accumulations of durations for certain location types: o Total Glance Time to Road Location
o Total Glance Time to Situation Awareness Location o Total Glance Time to Task-Related Areas
• Percent (or proportions) of task time spent looking at a location type: o To Road Locations
o To Situation Awareness Areas (borderline) o To Task-Related Areas
• Rates of glances per second
o Overall (Total/All), Road, Task, Not Road
2. These same measures tended to reveal interesting findings. First, and very important among these findings, was the fact that not all information is in the simple classification of glances as on-road or off-road. Glances to road, task, and mirror locations all carried important information. Among these, there were measures that discriminated between types of tasks. There were distinct patterns of glancing revealed across types of locations (road, mirrors, task).
3. Among the most interesting findings from formal statistical analysis was a significant Task by Location Type interaction across many of the eyeglance measures. Notable was the fact that the pattern of glances to the roadway discriminated task types particularly well, and a measure that integrated multiple measures together—proportion of task time spent looking at the road (Pct Dur Rd)—was particularly useful for characterizing patterns of glancing associated with tasks, along with a similar measure applied to each other glance location (task and mirrors).
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In summary, there were multiple effects of in-vehicle tasks on eyeglance behavior. Eyeglance metrics showed distinct patterns for different types of task engagement (just driving versus concurrently performing an auditory-vocal task or concurrently performing a visual-manual task). The Just Drive task was distinguished by patterns in which drivers looked at the road about 83 percent of the time and scanned their mirrors about 14.3 percent of the time. Glances on the road were about 8 seconds duration, on average. Auditory-vocal tasks showed a somewhat similar pattern, though drivers gazed at the forward roadway somewhat more (88%), using longer gazes (9 to 16 seconds, on average), and scanned their mirrors somewhat less (11%). The miss rate for event detection was slightly elevated over just driving for auditory-vocal tasks for CHMSL and LVD events, showing an increase of ~4 percent for CHMSL and LVD events, and somewhat more for peripheral FVTS events, showing an increase of ~23 percent, although event detection was less affected by auditory vocal tasks than by visual-manual tasks. Visual-manual tasks showed a different pattern, in which drivers looked at the forward roadway much less (viewing the road only 34 to 61 percent of the time during a task, and using glance durations on the road that were less than 2 seconds, on average. This reduction in glances to the road was made in order to view task-related areas required for performing the in-vehicle activity (viewing the task 29 to 60 percent of the time during its length). For visual-manual tasks, glances tended to cycle frequently back-and-forth between the task and the roadway locations, and glance rate measures proved to carry interesting information. Visual-manual tasks led to a more pronounced reduction in mirror- scanning (to 7%) and were associated with higher rates of missed events, although this was sometimes due to a methodological constraint for LVDs. Increases in miss rates over Just Drive were approximately 14 percent for CHMSLs, 20 percent for LVDs, and 42 percent for FVTS events, on average.
4. Interrelationships with driving performance measures revealed:
• Correlations with SDLP
• Correlations with Speed Difference
• Correlations with Event Detection (due to influence of Just Drive and selected tasks)
5. A striking new finding emerged from relating eyeglance data to event-detection data. Qualitative exploration of the time series data suggested that event detection affected eyeglance behavior. In brief, formal analyses of task summary statistics (a detailed analysis of the time series will be a target of future work) indicated that when an event occurred and was responded to, eyeglance behavior changed such that:
• For CHMSL events,
o Durations of glances decreased slightly for all locations except to situation awareness locations
o Rate of glancing increased slightly to road and situation awareness areas (mirrors)
• For LVD events,
o Durations of glances to the road lengthened
o Rate of glancing decreased to task-related and situation awareness areas
• For FVTS events,
Chapter 3 Test Track Results
o Rate of glancing to road and situation awareness areas increased
Changes to glance durations interacted with Task Type and were more pronounced for Just Drive and auditory-vocal tasks than for visual-manual tasks, which usually showed a different pattern. Events, when detected, appeared to act as attentional interrupts for auditory-vocal tasks and the Just Drive tasks, in eliciting more active scanning of the forward roadway and mirrors. This was a strategy that would be expected to improve subsequent event detection. Event detection also affected glance behavior during visual– manual tasks, but somewhat differently. Rate of scanning between all locations (road, task, and mirrors) increased, but higher glance rates were associated with higher rates of missed events (except for LVD events).
The finding that event detection may affect glance behavior has implications for analysis and design of future studies. Methods used to study event detection may influence the behavior of interest and suggest that when evaluating the visual demand of tasks in an advanced information system or in-vehicle device, it is important that multiple test trials be conducted—some with and some without event detection. The trials used to evaluate the visual demand of a task should not include events to-be-detected in order to obtain clean measurements of glance behavior, free from the influence of co-occurring events. The findings on event detection, substantive and methodological, highlight a rich area for future exploration.
6. Recommendations on eyeglance metrics for use in future work. The usefulness of the traditional eyeglance metrics for visual-manual tasks was confirmed through analyses of repeatability, predictive validity, and discriminability. These included: number of glances to task-related areas and total glance time to task. Retention of glance duration of task-related glances was recommended as well. Additional metrics, which emerged from new findings from this research, were recommended for use in future research on visual-manual tasks. No eyeglance metrics were recommended for application to the assessment of auditory-vocal tasks, although for research purposes, metrics emerging from this work as promising were identified (i.e., Proportion of task duration spent looking at the road, and mean duration of glances to the road, and proportion of task duration spent looking at mirrors/situation awareness areas).
7. Eyeglance behavior appears to be a key diagnostic for workload, and its associated metrics offer promise as key discriminators in identifying tasks that interfere with visual performance on the road.