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Quantifying situation awareness for small unmanned aircraft

Towards routine Beyond Visual Line of Sight operations

Published online by Cambridge University Press:  19 March 2018

O. McAree ,
J.M. Aitken  and
S.M. Veres
O. McAree*
Affiliation:
Faculty of Science, Liverpool John Moores University, UK
J.M. Aitken
Affiliation:
Department of Automatic Control and Systems Engineering, The University of Sheffield, UK
S.M. Veres
Affiliation:
Department of Automatic Control and Systems Engineering, The University of Sheffield, SysBrain Ltd, Southampton, UK
*
O.D.McAree@ljmu.ac.uk
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Abstract

A novel statistical model is presented to quantify situation awareness in the operation of small civilian Unmanned Aircraft Systems (UAS). Today, the vast majority of small Unmanned Aircraft Systems (UAS) operation takes place under Visual Line of Sight (VLOS) of a human operator, who is wholly responsible for the safety of the flight. As operation begins to move to Beyond Visual Line of Sight (BVLOS), it is likely that this responsibility will become shared between operator and the increasingly autonomous UAS itself. Before we seek to quantify the safety of such a system, it is beneficial to analyse the safety of existing Visual Line of Sight (VLOS) operations to provide a target level of safety. Prior to considering any on-board decision making, it is essential to ensure that the artificial situation awareness system of a UAS in Beyond Visual Line of Sight (BVLOS) is at least as good as awareness of a human operator. The paper provides a probabilistic theory and model for the high-level abstractions of situation awareness to guide future assessment of BVLOS operations.

Keywords

Unmanned Aerial SystemsAutonomySituation AwarenessSafety Classification Description: Aerospace SafetyAirworthinessControl ApplicationsHuman FactorsUnmanned Aerial Vehicles
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1251 , May 2018 , pp. 733 - 746
Copyright
Copyright © Royal Aeronautical Society 2018 

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References

REFERENCES

1. Adams, J.A. Unmanned vehicle situation awareness: A path forward, Human Systems Integration Symposium, Annapolis, Citesteer, 2007, pp. 31–89.Google Scholar
2. Aitken, J.M., Alexander, R. and Kelly, T. A case for dynamic risk assessment in NEC systems of systems, 5th International Conference on System of Systems Engineering (SoSE), Loughborough, IEEE, 2010, pp. 1–6.Google Scholar
3. Cameron, D., Aitken, J.M., Collins, E.C., Boorman, L., Chua, A., Fernando, S., McAree, O., Martinez-Hernandez, U. and Law, J. Framing factors: The importance of context and the individual in understanding trust in human-robot interaction, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Designing and Evaluating Social Robots for Public Settings, Hamburg, White Rose Research Online, 2015Google Scholar
4. Dzindolet, M.T., Peterson, S.A., Pomranky, R.A., Pierce, L.G. and Beck, H.P. The role of trust in automation reliance, Int J Human-Computer Studies, 2003, 58 (6), pp 697–718Google Scholar
5. Dzindolet, M.T., Pierce, L.G., Beck, H.P., Dawe, L.A. and Anderson, B.W. Predicting misuse and disuse of combat identification systems, Military Psychology, 2001, 13, (3), p 147.Google Scholar
6. Endsley, M.R. Design and evaluation for situation awareness enhancement, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 32. No. 2, Los Angeles, CA: SAGE Publications, 1988, pp. 97–101.Google Scholar
7. Endsley, M.R. Situation awareness global assessment technique (SAGAT), Proceedings of the IEEE National Aerospace and Electronics Conference, 1988, pp. 789–795.Google Scholar
8. Endsley, M.R. Measurement of situation awareness in dynamic systems, Human Factors: J Human Factors and Ergonomics Soc, 1995, 37, (1), pp. 65–84Google Scholar
9. Endsley, M.R. Direct measurement of situation awareness: Validity and use of SAGAT, Situation awareness analysis and measurement, 2000, 10Google Scholar
10. Klein, G. Sources of Power: How People Make Decisions, 1999, MIT Press, Cambridge, Massachusetts, US, pp. 94–96.Google Scholar
11. Lee, J.D. and Moray, N. Trust, self-confidence, and operators’ adaptation to automation, Int J Human-Computer Studies, 1994, 40, (1), 153–184.Google Scholar
12. Liu, C., Coombes, M., Li, B. and Chen, W.H. Enhanced situation awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2016.Google Scholar
13. McAree, O., Aitken, J.M. and Veres, S.M. Towards artificial situation awareness by autonomous vehicles, IFAC-PapersOnLine, 2017, 50, (1), 7038–7043.Google Scholar
14. McAree, O. and Chen, W.H. Artificial situation awareness for increased autonomy of unmanned aerial systems in the terminal area, J Intelligent Robotic Systems, 2013, 70, (1–4), 545–555.CrossRefGoogle Scholar
15. Stanton, N.A., Salmon, P.M., Jenkins, D.P., Walker, G.H., Rafferty, L.A. and Revell, K. Decisions, decisions and even more decisions: The impact of digitisation in the land warfare domain, Proceedings of NDM9, the 9th International Conference on Naturalistic Decision Making, 2009, British Computer Society, London, BCS Learning & Development Ltd., pp 1–9.Google Scholar
16. Veres, S.M., McAree, O. and Aitken, J.M. Towards formal verification of small and micro UAS, European Control Conference (ECC), Aalborg, IEEE, 2016, pp. 433–440.Google Scholar