Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment

  • Haibo Wang School of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics
  • Haiqing Si
  • Yao Li
  • Ting Pan
  • Yitong Zong
  • Naiqi Jiang
Keywords: flight safety, pilot’s intention, dynamic characteristics, one-way analysis of variance

Abstract

Intention is the main embodiment of human cerebral conscious activities, which has an important influence on guiding the realization of human behaviour. It is a vital prerequisite for analysing the dynamic characteristics of pilots with different intentions. Considering the intention law of the generation, transfer and reduction, this paper analyses dynamic characteristics of pilots with different intentions, starting from the factors of effect on the intention. Taking airfield traffic pattern as an example for simulating flight experiments, the pilot’s multi-source dynamic data of human – aircraft – environment system under different intentions and their psycho-physiological-physical characteristics were recorded. Based on Matlab, one-way analysis of variance was used to extract variables with significant changes, and the variables under different intentions were compared and analysed. The results show that the conventional pilots are more conducive to control the aircraft to keep a stable flight attitude. This study is of great significance for perfecting the warning system of flight safety and improving the pilot’s micro-behaviour assessment system.

References

Degani A, Heymann M, Meyer G, Shafto M. Some Formal Aspects of Human-Automation Interaction. Moffett Field, CA: NASA Ames Research Center TM 2000-209600; 2000.

Crow J, Javaux D, Rushby J. Models and Mechanized Methods that Integrate Human Factors into Automation Design. Proceedings of the International Conference on Human-Computer Interaction in Aeronautics: HCI-Aero, 27-29 Sep 2000, Toulouse, France. Tolosa Press; 2000. p. 163-168.

Final Report of the Accident Investigation Flash Airlines Flight 604, Boeing 737-300, SU-ZCF, January 3, 2004, Red Sea near Sharm El-Sheikh, Egypt. Egyptian Ministry of Civil Aviation, Final Investigation Report, Cairo, Egypt, 2004.

Report on Accident to Indian Airlines Airbus A-320 Aircraft VT-EPN at Bangalore on 14th February 1990. Indian Court of Inquiry, Indian Government, Final Investigation Report, New Delhi, India, 1992.

Wilson GF. An Analysis of Mental Workload in Pilots during Flight Using Multiple Psychophysiological Measures. International Journal of Aviation Psychology. 2002;12(1): 3-18.

Schlimm KA. A Model for Situational Awareness in Aircraft Upset Prevention and Recovery. 15th AIAA Aviation Technology, Integration, and Operations Conference, 22-26 June 2015, Dallas, TX; 2015.

Zhang L, Zhang W, Zhu Z, Ding L. Probing into the evaluation of flight training in special environment based on EMGs. Procedia Manufacturing. 2015;3(18): 4493-4500.

Jia B, Wei CF, Mao JF, Law R, Fu S, Wu Q. Identification of flight state under different simulator modes using improved diffusion maps. Optik. 2016;127(9): 3905-3911.

Marinescu A, Sharples S, Rithie C, Sanchez LS, McDowell M, Morvan H. Exploring the Relationship between Mental Workload, Variation in Performance and Physiological Parameters. 3th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems HMS. 2016;49(19): 591-596.

Olivari M, Venrooij J, Nieuwenhuizen FM, Pollini L, Heinrich H. Identifying Time-Varying Pilot Responses: a Regularized Recursive Least-Squares Algorithm. 2016 AIAA Modeling and Simulation Technologies Conference, 13-17 June 2016. Washington, D.C; 2016.

Tavcar A, Kuznar D, Gams M. Hybrid Multi-Agent Strategy Discovering Algorithm for human behaviour. Expert Systems with Applications. 2017;71: 370-382.

Jaquess KJ, Gentili RJ, Lo LC, Hatfield B. Empirical evidence for the relationship between cognitive workload and attentional reserve. International Journal of Psychophysiology. 2017;121: 46-55.

Lampton AK, Klyde DH, Schulze PC. Evaluation of a Missed Approach/Go-Around Spatial Disorientation Demonstration Scenario for Commercial Pilot Training. 2017 AIAA Modeling and Simulation Technologies Conference, 9-13 January 2017, Grapevine, Texas; 2017.

Russi-Vigoya MN, Patterson P. Analysis of pilot eye behaviour during glass cockpit simulations. Procedia Manufacturing. 2015;3: 5028-5035.

Wang XY, Liu YQ, Wang JQ, Zhang JL. Study on Influencing Factors Selection of Driver’s Propensity. Transportation Research Part D. 2019;66(1): 35-48.

Sun R, Peng T. Design of Flight Cognitive Ability Measurement Scheme and Its Model. China Safety Science Journal. 2010;20(11): 47-51.

Hadder EM, Takahashi TT. Minimum Control Speed Estimation for Conceptual Design. 17th AIAA Aviation Technology, Integration, and Operations Conference, 5-9 June 2017, Denver, Colorado; 2017.

Published
2020-02-13
How to Cite
1.
Wang H, Si H, Li Y, Pan T, Zong Y, Jiang N. Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment. Promet [Internet]. 2020Feb.13 [cited 2024Apr.20];32(1):153-66. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3237
Section
Articles