Human Factors Measurement for Future Air Traffic Control Systems

Langan‐Fox, Janice; Sankey, Michael J.; Canty, James M.

doi:10.1177/0018720809355278

Cited by 58 publications

(72 citation statements)

References 145 publications

(284 reference statements)

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“…However, pupil diameter and eyelid opening could indicate the current level of mental and physical workload. Once fatigue was induced by the accumulated workload, pupil diameter and eyelid opening would change and become smaller as deeper fatigue [14,15]. Also, heart rate, respiration rate and electrodermal activity also reduced as the psychophysical fatigue slowly appeared [16].…”

Section: Discussionmentioning

confidence: 99%

Pilot's visual attention allocation modeling under fatigue

Wu¹,

Wanyan²,

Zhuang

2015

THC

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Abstract. BACKGROUND: Human factors involved with visual attention mechanism and fatigue are critical causes of modern aviation accidents. OBJECTIVE: To investigate the connection between attention and flight fatigue, a mathematical model of pilot's visual attention allocation was established based on information processing channels. Multi-task condition and current psychophysical state were taken into account as well. METHODS: Sixteen participants were recruited to perform a long-term dual-task in a Boeing 737-800 flight simulator. The primary task was an envelope flight task and the secondary was an unusual attitude (UA) recovery task. Reaction time of the secondary task was recorded as a behavior performance index, while heart rate and respiration rate were measured as physiological indices as well as fixation distribution as attention allocation index. RESULTS:The experiment results showed a significant affect of experiment time that indicated the occurrence and influence of fatigue. Eye movement tracking also revealed good agreement with the predictable model and hence verified its effectiveness. Moreover, applicability of the model was validated under flight fatigue and multiple tasks condition. CONCLUSION: The current study provided a quantitative connection between pilot's visual attention allocation and flight fatigue, which was verified in the ergonomics experiment.

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Section: Discussionmentioning

confidence: 99%

Pilot's visual attention allocation modeling under fatigue

Wu¹,

Wanyan²,

Zhuang

2015

THC

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“…This problem of requiring a highly trained specialist to supervise a highly automated system with relatively few required interactions that results in boredom is not unique to UAV operations, as similar problems have been reported in air traffic control settings [6], the supervision of process control plants [7], train engineers [8] anesthesiologists [9,10] and even commercial aviation pilots [11,12]. This problem was recently brought to the attention of the general public when two Northwest pilots overflew Minneapolis by 90 minutes because the plane was on autopilot and, as reported by the FAA, the pilots became distracted by their laptops [13], presumably because the enroute portion of the flight required so little interaction that the pilots sought stimulation from another source.…”

Section: Introductionmentioning

confidence: 90%

Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control

Cummings¹,

Mastracchio²,

Thornburg³

et al. 2013

Interacting With Computers

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Operators currently controlling Unmanned Aerial Vehicles report significant boredom, and such systems will likely become more automated in the future. Similar problems are found in process control, commercial aviation, and medical settings. To examine the effect of boredom in such settings, a long duration low task load experiment was conducted. Three low task load levels requiring operator input every 10, 20, or 30 minutes were tested in a four-hour study using a multiple unmanned vehicle simulation environment that leverages decentralized algorithms for sometimes imperfect vehicle scheduling. Reaction times to system-generated events generally decreased across the four hours, as did participants' ability to maintain directed attention. Overall, participants spent almost half of the time in a distracted state. The top performer spent the majority of time in directed and divided attention states.Unexpectedly, the second-best participant, only 1% worse than the top performer, was distracted almost one third of the experiment, but exhibited a periodic switching strategy, allowing him to pay just enough attention to assist the automation when needed. Indeed, four of the five top performers were distracted more than one-third of the time. These findings suggest that distraction due to boring, low task load environments can be effectively managed through efficient attention switching. Future work is needed to determine optimal frequency and duration of attention state switches given various exogenous attributes, as well as individual variability. These findings have implications for the design of and personnel selection for supervisory control systems where operators monitor highly automated systems for long durations with only occasional or rare input.

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“…Examples from the literature include the Human Computer Trust Rating Scale [25]. Yagoda and Gillan [44] propose a scale that taps four closely-related adjectives for describing different aspects of HRI: reliability, dependability, accessibility, and timeliness or predictability.…”

Section: Trust and Distrust In Hrimentioning

confidence: 99%