Using Eye Tracker Data in Air Traffic Control Puck Imants
Tjerk de Greef
TNO
Delft University of Technology
Kampweg 5 3769 DE Soesterberg The Netherlands
[email protected]
Mekelweg 4 2628 CD Delft The Netherlands
[email protected]
ABSTRACT
Motivation/Research approach – An exploratory study was conducted to investigate whether eye movement metrics discriminate between different air traffic control tasks. Findings/Design – The results show the three tasks elicit different eye movement, as Yarbus (1967) also showed in static pictures, and that a number of eye tracking metrics demonstrate the differences. Research limitations/Implications – The effect was demonstrated using only one participant. The results can be used to further study various eye movement metrics. Originality/Value – The research demonstrates that different calculus distinguishes between tasks allowing targeting specific support given the type of task. Take away message – A combination of eye tracker metrics discriminates between tasks helping to provide flexible task support. Keywords
Eye tracking data, scan-path, fixations, adaptive automation, air traffic control INTRODUCTION
An air traffic controller (ATCo) is responsible for safe and efficient operation of air traffic. The goal for the ATCo is finding ways of maintaining and expediting an orderly flow of air traffic. Today, ATCos are well trained and operate mostly within safety margins and procedures. However, the workload of the controller is a function of the flow of air traffic, at times leading to cognitive overload and underload. These cognitive conditions increase the risk for intentional tunnelling, cognitive lockup, and the out-of-the-loop syndrome (Endsley & Kiris, 1995). Unfortunately, such situations do occur and potentially cause fatal accidents (Bundesstelle für Flugunfalluntersuchung, 2004). The demands on the ATCo are increasing given the complexity of the air traffic control (ATC) tasks and the expected increase in flight movements (EUROCONTROL, 2010). Task dependant automation and adaptive interfaces potentially support the ATCo given the increased flight movements. Given the visual demand of the tasks related to ATC, this exploratory
study focuses on using an eye tracker to infer which task the operator is engaging in. Yarbus (1967) showed that the path the eye follows differs when you examine the same static picture given a different task. In this exploratory study we examined if the same effect could be found for three ATC tasks and if this effect can be captured numerically using various eye movement measures. TASKS IN AIR TRAFFIC CONTROL
The air traffic control task can roughly be divided in three tasks, namely monitoring, planning, and controlling. Monitoring the traffic flow involves checking if all flights are separated and that traffic continues in a safe and orderly manner. Therefore the ATCo has to indentify flights and interpreted the overall traffic situation. Planning involves ensuring that the traffic will continue in a safe and orderly manner in the future. Where monitoring is more about assessing the current situation planning associates with anticipating future safety issues. When controlling the air traffic, the ATCo issues clearances and instructions to ensure the separation of the air traffic. THEORETICAL BACKGROUND EYE METRICS
People make various eye movements. Saccades, for example, are rapid movements moving the eyes around the scene. In the fixation that follows visual information is processed requiring a stable gaze (Land, 2009). A scanpath consists of a series of fixations and saccades. A lengthier scanpath indicates less efficient scanning behaviour (Goldberg, 1999). A large average fixation duration implies that more time is spent interpreting or relating the information to internalized representations (Jacob, 2003, Goldberg, 1999). A convex hull area determines the area of the scanpath; a large area indicates a large search area. The combination of the scanpath length with the convex hull area determines whether the search covers a large or localized area (Goldberg, 1999). CLAIMS
We expect that the ATC tasks yield different scanpaths and these differences can be captured with metrics. Table 1 summarizes the expected scanpath metrics outcomes per ATC task.
Table 1: the ATC tasks and the expected eye movement measurements Scanpath Length Average duration Convex Hull Area
Monitor Longer Shorter Larger
Plan Shorter Longer Smaller
Control Shorter Longer Smaller
EXPERIMENT
An explorative study was setup to support the claims described in table 1. Two different air traffic control scenarios were prepared with ATC-Lab (Fothergill, 2009). We recruited an ATCo student and she was familiarized with ATC-Lab prior to the trials. The participant performed the three ATC-subtasks and each scenario was run two times for one minute yielding in 12 runs in total. The eye movements were measured with the Eyelink II. RESULTS
Figure 1 shows, for scenario 1 (2nd run), a screenshot of ATC lab in addition to the scanpaths per task. The pictures show, first of all, high density of fixations clusters that correspond to the aircrafts. The pictures also highlight differences. The controlling task shows that the fixations in the clusters are wider spread compared to the fixations in the monitoring and planning fixation clusters. In addition, the scanpath is wider spread in the monitoring task, with fixations on both the aircrafts and the surrounding area whereas the scanning area is more compact in the planning task.
involves a longer duration of fixations that are concentrated in a smaller area compared to monitoring. A fixation duration and scanpath length that lies between planning and monitoring characterizes the controlling task. Table 2: The eye movement measures averaged over the two runs and the two scenarios. Scanpath Length(pixels *105) Average duration (mSec) Convex Hull (pixels *105)
Monitor 1,14 266 7,73
Plan 0,83 308 4,10
Control 0,97 284 4,41
CONCLUSIONS
The results of this exploratory study indicate that there is a visible and numerical difference in scanning pattern for the three ATC tasks and that it is possible to differentiate between these three sub-tasks making use of scanpath metrics. This knowledge is valuable for optimizing task dependent support. FUTURE RESEARCH
The results encourage continuation of this study but with more participants. Also, the incorporation of spatial arrangement metrics such as the transition matrices, sample entropy, and the nearest neighbour index might benefit task differentiation. REFERENCES
Bundesstelle für Flugunfalluntersuchung, (2004). Investigation Report AX001-1-2/02. Bundesstelle für Flugunfalluntersuchung, Braunschweig. Endsley, M.R., & Kiris, E.O., (1995). The out-of-theloop performance problem and level of control in automation. Human Factors, 37(2), 381-394. EUROCONTROL, (2010). Long-Term Forecast: IFR Flight Movements 2010-2030. EUROCONTROL Headquarters, Brussels. Fothergill S., Loft S. and Neal A., (2009) ATClabAdvanced: An air traffic control simulator with realism and control Behavior Research Methods, 41 (1), 118-127.
Figure 1: The traffic in scenario 1 (2nd run) in ATClab (upper left) and scan patterns (blue) and the circumscribing convex hull area in red for the three ATC tasks: upper right: monitoring, bottom left: planning and bottom right: controlling
Table 2 summarizes the 12 runs and splits the averages per task and scanpath metric. Taking a closer look, we see that the average duration of the fixations are higher when planning, indicating more cognitive processing, when compared to monitoring and controlling. The scanpath is longer when monitoring, indicating more search, compared to planning and controlling. The convex hull area taken together with the convex hull area indicates the coverage of more area by the scanning in the monitoring task. To conclude, the planning task
Goldberg, J.H., Kotval, X.P. (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal of Industrial Ergonomics. 24, 631-645 Jacob, R.J.K., and Karn, K.S., (2003). Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises, In J. Hyönä, R. Radach, & H. Deubel (Eds.), The mind’s eye: Cognitive and applied aspects of eye movement research (pp. 573-605). Amsterdam: Elsevier. Land M.F. and Tatler B.W., Looking and Acting, Oxford University Press, Oxford. Yarbus, A.L., (1967) Eye movement and Vision. Plenum Press, New York.
