Abstract
Introduction. Eye tracking technology can be used to characterise a pilot's visual behaviour as well as to further analyse the workload and status of the pilot, which is crucial for tracking and predicting pilot performance and enhancing flight safety. Research questions. This research aims to investigate and identify the visual-related factors that could affect the pilot's landing operation performance (depending on whether the landing was successful or not). Method. There are 23 participants who performed the task of landing in the Future system simulator (FSS) while wearing eye trackers. Their eye tracking parameters including proportion of fixation count on primary flight display (PFC on PFD), proportion of fixation count on out the window (PFC on OTW), percentage change in pupil diameter (PCPD) and blink count were trained for classification using XGBoost according to whether they landed successfully or not. Results & Discussion. The results demonstrated that eye-movement features can be used to classify and predict a pilot's landing performance with an accuracy of 77.02%. PCPD and PFC on PFD are more crucial for performance classification out of the four features. Conclusion. It is practical to classify and predict pilot performance using eye-tracking technologies. The high importance of PCPD and PFC on PFD indicates that there is a correlation between pilots’ workload and attention distribution and performance, which has important implications for future predictive and analytical research on performance. The prediction of performance using eye tracking suggests that pilot status monitoring has a useful application in flight deck design.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, T., Lim, C.P., Nguyen, N.D., Gordon-Brown, L., Nahavandi, S.: A review of situation awareness assessment approaches in aviation environments. IEEE Syst. J. 13(3), 3590–3603 (2019). https://doi.org/10.1109/JSYST.2019.2918283
Novak, A., Mrazova, M.: Research of physiological factors affecting pilot performance in flight simulation training device. Commun.-Sci. Lett. Univ. Zilina 17(3), 103–107 (2015). https://doi.org/10.26552/com.C.2015.3.103-107
Lee, K.: Effects of flight factors on pilot performance, workload, and stress at final approach to landing phase of flight. University of Central Florida (2010). Accessed 20 Feb 2023. https://stars.library.ucf.edu/etd/1628
Carroll, M., Dahlstrom, N.: Human computer interaction on the modern flight deck. Int. J. Hum.-Comput. Interact. 37(7), 585–587 (2021). https://doi.org/10.1080/10447318.2021.1890495
Li, W.C., Zhang, J., Minh, T.L., Cao, J., Wang, L.: Visual scan patterns reflect to human-computer interactions on processing different types of messages in the flight deck. Int. J. Ind. Ergon. 72, 54–60 (2019). https://doi.org/10.1016/j.ergon.2019.04.003
Stanton, N.A., Plant, K.L., Roberts, A.P., Allison, C.K.: Use of highways in the sky and a virtual pad for landing head up display symbology to enable improved helicopter pilots situation awareness and workload in degraded visual conditions. Ergonomics 62(2), 255–267 (2017). https://doi.org/10.1080/00140139.2017.1414301
Hebbar, P.A., Pashilkar, A.A., Biswas, P.: Using eye tracking system for aircraft design – a flight simulator study. Aviation 26(1), 11–21 (2022). https://doi.org/10.3846/AVIATION.2022.16398
Ryffel, C.P., Muehlethaler, C.M., Huber, S.M., Elfering, A.: Eye tracking as a debriefing tool in upset prevention and recovery training (UPRT) for general aviation pilots. Ergonomics 62(2), 319–329 (2019). https://doi.org/10.1080/00140139.2018.1501093
Chen, S., Epps, J.: Using task-induced pupil diameter and blink rate to infer cognitive load. Hum. Comput. Interact. 29, 390–413 (2014). https://doi.org/10.1080/07370024.2014.892428
Yang, L., Yu, R., Lin, X., Xie, Y., Ma, L.: Visual search tasks: measurement of dynamic visual lobe and relationship with display movement velocity. Ergonomics 61(2), 273–283 (2017). https://doi.org/10.1080/00140139.2017.1353138
Yahoodik, S., Tahami, H., Unverricht, J., Yamani, Y., Handley, H., Thompson, D.: Blink rate as a measure of driver workload during simulated driving. In: Proceedings of the 2020 HFES 64th International Annual Meeting, vol. 64, no. 1, pp. 1825–1828 (2021). https://doi.org/10.1177/1071181320641439
Li, W.C., Braithwaite, G., Wang, T., Yung, M., Kearney, P.: The benefits of integrated eye tracking with airborne image recorders in the flight deck: a rejected landing case study. Int. J. Ind. Ergon. 78, 102982 (2020). https://doi.org/10.1016/J.ERGON.2020.102982
Neboshynsky, C.M.: Expertise on cognitive workloads and performance during navigation and target detection (2012). Accessed 19 Feb 2023. https://apps.dtic.mil/sti/citations/ADA561981
Biswas, P., Jeevithashree, D.V.: Eye gaze controlled MFD for military aviation. In: International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 79–89 (2018).https://doi.org/10.1145/3172944.3172973
Causse, M., Lancelot, F., Maillant, J., Behrend, J., Cousy, M., Schneider, N.: Encoding decisions and expertise in the operator’s eyes: using eye-tracking as input for system adaptation. Int. J. Hum. Comput. Stud. 125, 55–65 (2019). https://doi.org/10.1016/J.IJHCS.2018.12.010
Babu, M.D., JeevithaShree, D.V., Prabhakar, G., Saluja, K.P.S., Pashilkar, A., Biswas, P.: Estimating pilots’ cognitive load from ocular parameters through simulation and in-flight studies. J. Eye Mov. Res. 12(3), 1–16 (2019). https://doi.org/10.16910/JEMR.12.3.3
Callaway, F., Rangel, A., Griffiths, T.L.: Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. 17(3), e1008863 (2021). https://doi.org/10.1371/JOURNAL.PCBI.1008863
Li, W.C., Horn, A., Sun, Z., Zhang, J., Braithwaite, G.: Augmented visualization cues on primary flight display facilitating pilot’s monitoring performance. Int. J. Hum. Comput. Stud. 135, 102377 (2020). https://doi.org/10.1016/j.ijhcs.2019.102377
Chen, S., Epps, J.: Automatic classification of eye activity for cognitive load measurement with emotion interference. Comput. Methods Programs Biomed. 110(2), 111–124 (2013). https://doi.org/10.1016/J.CMPB.2012.10.021
Chen, S., Epps, J., Ruiz, N., Chen, F.: Eye activity as a measure of human mental effort in HCI. In: International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 315–318 (2011).https://doi.org/10.1145/1943403.1943454
Krejtz, K., Duchowski, A.T., Niedzielska, A., Biele, C., Krejtz, I.: Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PLoS ONE 13(9), e0203629 (2018). https://doi.org/10.1371/JOURNAL.PONE.0203629
Kruger, J.L., Hefer, E., Matthew, G.: Measuring the impact of subtitles on cognitive load: eye tracking and dynamic audiovisual texts. In: ACM International Conference Proceeding Series, pp. 62–66 (2013).https://doi.org/10.1145/2509315.2509331
Coyne, J.T., Foroughi, C., Sibley, C.: Pupil diameter and performance in a supervisory control task: a measure of effort or individual differences? In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 865–869 (2017). https://doi.org/10.1177/1541931213601689
Alhanbali, S., Munro, K.J., Dawes, P., Carolan, P.J., Millman, R.E.: Dimensions of self-reported listening effort and fatigue on a digits-in-noise task, and association with baseline pupil size and performance accuracy. Int. J. Audiol. 60(10), 762–772 (2020). https://doi.org/10.1080/14992027.2020.1853262
van den Brink, R.L., Murphy, P.R., Nieuwenhuis, S.: Pupil diameter tracks lapses of attention. PLoS ONE 11(10), e0165274 (2016). https://doi.org/10.1371/JOURNAL.PONE.0165274
Appel, T., Scharinger, C., Gerjets, P., Kasneci, E.: Cross-subject workload classification using pupil-related measures. In: Eye Tracking Research and Applications Symposium (ETRA), vol. 18 (2018). https://doi.org/10.1145/3204493.3204531
iF Design. Future Systems Simulator (FSS) (2021). https://ifdesign.com/en/winner-ranking/project/future-systems-simulator-fss/314432. Accessed 21 Nov 2022
Korek, W.T., Li, W.C., Lu, L., Lone, M.: Investigating pilots’ operational behaviours while interacting with different types of inceptors. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13307 LNAI, pp. 314–325 (2022). https://doi.org/10.1007/978-3-031-06086-1_24/COVER
Li, W.C., Wang, Y., Korek, W.T.: To be or not to be? assessment on using touchscreen as inceptor in flight operation. Transport. Res. Procedia 66(C), 117–124 (2022). https://doi.org/10.1016/J.TRPRO.2022.12.013
Li, W.-C., Moore, P., Zhang, J., Lin, J., Kearney, P.: The impact of out-the-window size on air traffic controllers’ visual behaviours and response time on digital tower operations. Int. J. Hum. Comput. Stud. 166, 102880 (2022). https://doi.org/10.1016/J.IJHCS.2022.102880
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785
Ziv, G.: Gaze behavior and visual attention: a review of eye tracking studies in aviation. Int. J. Aviat. Psychol. 26(3–4), 75–104 (2017). https://doi.org/10.1080/10508414.2017.1313096
Pfleging, B., Fekety, D.K., Schmidt, A., Kun, A.L.: A model relating pupil diameter to mental workload and lighting conditions. In: Conference on Human Factors in Computing Systems - Proceedings, pp. 5776–5788 (2016). https://doi.org/10.1145/2858036.2858117
Acknowledgements
This research is co-financed by the European Union through the European Social Fund (grant number POWR.03.02.00-00-I029). The authors would like to thank the FSS Team in Cranfield, especially Mudassir Lone, for his generous support during the project’s development, and Rolls-Royce, particularly Peter Beecroft, for approving the research to be carried out in the Future Systems Simulator.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Yang, L., Korek, W.T., Zhao, Y., Li, WC. (2023). The Evaluations of the Impact of the Pilot’s Visual Behaviours on the Landing Performance by Using Eye Tracking Technology. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2023. Lecture Notes in Computer Science(), vol 14017. Springer, Cham. https://doi.org/10.1007/978-3-031-35392-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-35392-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35391-8
Online ISBN: 978-3-031-35392-5
eBook Packages: Computer ScienceComputer Science (R0)