Unpacking Situation Awareness: Improving Diagnosis and Assessment of Older Drivers’ Perception, Comprehension, and Projection Failures in a Specific Driving Scenario

Models of situation awareness (SA) and constructs originated in aviation domain, not originally intended for the driving task. Situation awareness is defined as “the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future” (Endsley, 1995). Endsley described three levels of SA: perceiving critical factors in the environment (level 1), determining critical factors’ significance in relation to user’s goals (level 2), and understanding how these critical factors will affect the user’s goals and projecting this system’s behavior in the near future (level 3).

The boundaries between these levels of SA in the driving task, where the environment is more populated with potential hazards (e.g., other drivers, pedestrians, etc.), are not as well-delineated as they are within the aviation realm. That is, the driving task’s more dynamic demands lead to more potential overlap between the perception and comprehension levels relative to aviation task, and most likely need to be more quickly followed by evasive/corrective action (e.g., a car pulling out in front of the driver unexpectedly vs. a pilot realizing a fuel leak by noting the fuel gauge for one engine is depleting faster than the other engines). Identification and correction of older drivers’ SA-related deficits stand to benefit from more sensitive measures focused on the levels of SA specific to the driving task, as opposed to the construct at large.

To explore and develop possible convergent measures in a driving simulator equipped with eye-tracking capability that reveal gaps in driving skill that can be related to perceptual or comprehension processes. These measures may then be used to evaluate technological systems or training regimens aimed at assisting older drivers in maintaining SA in driving situations that are problematic to this age group.

Developing measures that identify the specific level of SA (e.g., perception, recognition, or projection) that is deficient within the individual, specific failure points in a particular driving scenario, as well as evaluate technology that can augment these SA components at these failure points.

Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society. doi:10.1518/001872095779049543

Stanton, N. A., Salmon, P. M., & Walker, G. H. (2014). Let the Reader Decide: A Paradigm Shift for Situation Awareness in Sociotechnical Systems. Journal of Cognitive Engineering and Decision Making. doi:10.1177/1555343414552297

Endsley, M. R. (2015). Final Reflections: Situation Awareness Models and Measures. Journal of Cognitive Engineering and Decision Making, 9(1), 101–111. doi:10.1177/1555343415573911

Liu, Y.-C., & Cian, J.-Y. (2014). Effects of situation awareness under different road environments on young and elder drivers. Journal of Industrial and Production Engineering, 31(5), 253–260. doi:10.1080/21681015.2014.946103

Review recent literature for methods of differentiating and measuring the three levels of SA (perception, comprehension, and projection).

Pilot left turn and/or merge tasks in a driving simulator instrumented with eye-tracking capabilities to validate these methods’ ability to differentiate between the levels of SA.

Obtain IRB approval, recruit young (18-30), middle (40-60), and older participants (70+):

· Exclusion criteria: medical condition with limiting severity, medications that affect driving performance, failing Mini-Mental Status Examination score at pre-screen.

· Inclusion criteria: > 3 trips per week (defined as starting the car, driving to a different location, and turning the car off), valid license

Recruit participants from the local area, invite to the lab to participate in study, and obtain informed consent prior to having them complete [specific driving task] and collect multiple types of data (e.g., eye-tracking, objective driving measures, and concurrent verbal protocols), analyze collected data, and write up results.

Establish useful next steps for using these types of data to diagnose what level of SA needs intervention/ augmentation within a particular individual as well as evaluating the assistance provided by in-vehicle technologies that might provide such help.

Difficulty recruiting older adult samples due to increased potential for simulator sickness. Proprietary nature of manufacturers’ assessment methods of SA might be prohibitive to their collection.

We have no strong measures to evaluate the extent to which emergent ITS devices support specific levels of SA (particularly that of older adults) within the driving setting. This research informs the development of a metric to accurately measure SA at the perception, comprehension, and projection levels in the context of [whichever driving task we select] and the extent to which these burgeoning technologies support, rather than detract from, older adults’ SA while navigating [selected driving task].