Experimental Simulation Set-Up for Validating Out-Of-The-Loop Mitigation when Monitoring High Levels of Automation in Air Traffic Control
An increasing degree of automation in air traffic will also change the role of the air traffic controller (ATCO). ATCOs will fulfill significantly more monitoring tasks compared to today. However, this rather passive role may lead to Out-Of-The-Loop (OOTL) effects comprising vigilance decrement and less situation awareness. The project MINIMA (Mitigating Negative Impacts of Monitoring high levels of Automation) has conceived a system to control and mitigate such OOTL phenomena. In order to demonstrate the MINIMA concept, an experimental simulation set-up has been designed. This set-up consists of two parts: 1) a Task Environment (TE) comprising a Terminal Maneuvering Area (TMA) simulator as well as 2) a Vigilance and Attention Controller (VAC) based on neurophysiological data recording such as electroencephalography (EEG) and eye-tracking devices. The current vigilance level and the attention focus of the controller are measured during the ATCO’s active work in front of the human machine interface (HMI). The derived vigilance level and attention trigger adaptive automation functionalities in the TE to avoid OOTL effects. This paper describes the full-scale experimental set-up and the component development work towards it. Hence, it encompasses a pre-test whose results influenced the development of the VAC as well as the functionalities of the final TE and the two VAC’s sub-components.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316361Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 ACARE, “Flightpath 2050-Europe’s Vision for Aviation,” Advisory Council for Aeronautics Research in Europe, 2011.
 SESAR JU, “European ATM Master Plan,” 2012.
 HALA!, “Deliverable, I. D. Position Paper,” hala-sesar.net, 2012.
 E. E. Jones, A. R. Carter-Sowell, J. R. Kelly, and K. D. Williams, “I'm out of the loop': Ostracism through information exclusion,” Group Processes & Intergroup Relations, 12(2), 2009, pp. 157–174.
 P. Arico, G. Borghini, G. Di Flumeri, N. Sciaraffa, A. Colosimo, and F. Babiloni, “Passive BCI in Operational Environments: Insights, Recent Advances and Future trends,” IEEE Transactions on Biomedical Engineering, 2017.
 M. I. Posner and S. J. Boies, “Components of attention,” Psychological review, 78(5), 391, 1971.
 D. Kahneman, “Attention and effort,” Vol. 1063, Englewood Cliffs, NJ, Prentice-Hall, 1973.
 R. Parasuraman, J. S. Warm, and J. E. See, “Brain systems of vigilance,” in The attentive brain, Cambridge, MA, US: The MIT Press, 1998, pp. 221–256.
 M. Steriade, P. Gloor, R. R. Llinas, F. L. Da Silva, and M.-M. Mesulam, “Basic mechanisms of cerebral rhythmic activities,” Electroencephalogr. Clin. Neurophysiol., vol. 76, no. 6, pp. 481–508, 1990.
 W. Sturm and K. Willmes, “On the Functional Neuroanatomy of Intrinsic and Phasic Alertness,” Neuroimage, vol. 14, no. 1, pp. S76–S84, Jul. 2001.
 M. Singh and A. Sharma, “Correlational study of attention task performance and EEG alpha power,” Int. J. Inf. Technol. Knowl. Manag., vol. 8, no. 2, pp. 188–196, 2015.
 F. M. Howells, D. J. Stein, and V. A. Russell, “Perceived mental effort correlates with changes in tonic arousal during attentional tasks,” in Behav. Brain Funct., vol. 6, p. 39, 2010.
 E. Molina, Á. Correa, D. Sanabria, and T. P. Jung, “Tonic EEG dynamics during psychomotor vigilance task,” 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013, pp. 1382–1385.
 R. T. Wilkinson and D. Houghton, “Field test of arousal: a portable reaction timer with data storage,” in Hum. Factors, vol. 24, no. 4, pp. 487–493, Aug. 1982.
 D. Gould et al., “Visual Analogue Scale (VAS),” Journal of Clinical Nursing, 2001, 10, pp. 697–706.
 T.-W. Lee, M. Girolami, and T. J. Sejnowski, “Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources,” Neural Comput., vol. 11, no. 2, pp. 417–441, Feb. 1999.
 A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” J. Neurosci. Methods, vol. 134, no. 1, pp. 9–21, Mar. 2004.
 F. J. Harris, “On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform,” Proc. IEEE, vol. 66, no. 1, pp. 51–83, 1978.
 W. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis,” Brain Res. Rev., vol. 29, no. 2–3, pp. 169–195, Apr. 1999.
 P. Aricò, G. Di Borghini, G. Di Flumeri, S. Bonelli, A. Golfetti, I. Graziani, S. Pozzi, J.-P. Imbert, G. Granger, R. Benhacene, D. Schaefer, F. Babiloni, “Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review,” IEEE reviews in biomedical engineering, 2017.
 P. Aricò, G. Borghini, G. Di Flumeri, A. Colosimo, S. Bonelli, A. Golfetti, and F. Babiloni, “Adaptive automation triggered by EEG-based mental workload index: a passive brain-computer interface application in realistic air traffic control environment,” Frontiers in human neuroscience, 10, 2016.