Investigation of Pilot Reaction Time on Automatic Ground Collision Avoidance System

Yıl 2024, Cilt: 40 Sayı: 3, 661 - 669, 30.12.2024

Birce Boga , Zeynep Seda Mor , Nida Kuşku

Öz

In this study it is aimed to compare manual pilot recovery behavior and automatic recovery in case of Controlled Flight into Terrain (CFIT) condition. Several test cases are conducted for random initial conditions in a fixed-based simulator environment and obtained test results are compared. Average pilot reaction time is determined from simulation results. Collected data is used to improve GCAS algorithm design for augmented pilot trust and comfort.

Anahtar Kelimeler

Ground collision, Pilot interaction, Reaction time, Manual GCAS, Automatic GCAS, Flight test

Proje Numarası

-

Otomatik Yer Çarpmasından Kaçınma Sistemi için Pilot Tepki Süresinin İncelenmesi

Öz

Bu çalışmada Araziye Kontrollü Uçuş (CFIT) durumunda manuel pilot kurtarma davranışı ile otomatik kurtarma davranışının karşılaştırılması amaçlanmaktadır. Sabit tabanlı bir simülatör ortamında rastgele başlangıç koşulları için çeşitli test senaryoları yürütülür ve elde edilen test sonuçları karşılaştırılır. Ortalama pilot reaksiyon süresi simülasyon sonuçlarından belirlenir. Toplanan veriler, pilotun güvenini ve konforunu artırmak amacıyla GCAS algoritma tasarımını geliştirmek için kullanılır.

Anahtar Kelimeler

Pilot etkileşimi, Reaksiyon süresi, Manuel GCAS, Otomatik GCAS, Uçuş testi

Proje Numarası

-

Kaynakça

• [1] Lyons, Joseph & Ho, Nhut & Abel, Anna & Hoffmann, Lauren & Sadler, Garrett & Fergueson, William & Grigsby, Michelle & Wilkins, Mark. (2017). Comparing Trust in Auto-GCAS between Experienced and Novice Air Force Pilots. Ergonomics in Design: The Quarterly of Human Factors Applications. 25. 106480461771661. 10.1177/1064804617716612.
• [2] D. E. Swiharta, A. F. (2011). Design, Integration and Flight Test of an Autonomous Ground. Gyroscopy and Navigation, 84–91.
• [3] R. Huffman Jr., M. S. (1998). Application of Ground Collision Avoidance System Nuisance Criteria. ICAS and AIAA, 76-85.
• [4] Dr. William B. Albery, C. M. (2003). Differences in Pilot Automation Philosophies in the US and Russian Air Forces Ground Collision Avoidance Systems. OH: Defence Technical Information Center.
• [5] Hoffmann, Lauren. (2019). Assisting the Improvement of a Military Safety System: An Application of Rapid Assessment Procedures to the Automatic Ground Collision Avoidance System. Human organization. 78. 241- 252. 10.17730/0018-7259.78.3.241.
• [6] Kirkendoll, Zack & Hook, Loyd. (2021). Automatic Ground Collision Avoidance System Trajectory Prediction and Control for General Aviation. 1-10. 10.1109/DASC52595.2021.9594506.
• [7] Lyons, Joseph & Ho, Nhut & Fergueson, William & Sadler, Garrett & Cals, Samantha & Richardson, Casey & Wilkins, Mark. (2016). Trust of an Automatic Ground Collision Avoidance Technology: A Fighter Pilot Perspective. Military Psychology. 28. 10.1037/mil0000124.
• [8] Griffiths, S., Saunders, J., Curtis, A., McLain, T. W., & Beard, R. W. (2005). Obstacle and terrain avoidance for miniature aerial vehicles. Advances in Unmanned Aerial Vehicles, 213–244.
• [9] Curry, I., & Phipps, D. (1997). TERPROM: The terrain referenced navigation, flight path optimisation and mission planning system. The Aeronautical Journal, 101(1008), 189–195.
• [10] Sun, J., Tang, J., & Lao, S. (2017). Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm. IEEE Access, 5, 18382–18390.
• [11] Zhou, B., & Zhang, H. (2020). An integrated geometric obstacle avoidance and genetic algorithm TSP solution for UAV path planning. Drones, 4(3), 42.
• [12] Kuchar, J. K., & Drumm, A. C. (2005). The traffic alert and collision avoidance system. Lincoln Laboratory Journal, 16(2), 277–296.
• [13] Banerjee, A., & Gorospe, G. (2020). Risk assessment of obstacle collision for UAVs under off-nominal conditions. In Proceedings of the Annual Conference of the Prognostics and Health Management Society.
• [14] Chen, L., & Tomlin, C. J. (2018). Probabilistic collision avoidance for UAVs using Gaussian mixture models. IEEE Transactions on Robotics, 34(3), 828–841.
• [15] Zhang, Y., & Zhang, H. (2021). Safety risk modelling and assessment of civil unmanned aircraft system operations: A literature review. Drones, 5(1), 1.
• [16] Schouwenaars, T., De Moor, B., Feron, E., & How, J. P. (2001). Mixed integer programming for multi-vehicle path planning. In Proceedings of the IEEE European Control Conference (pp. 2603–2608).
• [17] Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 17(7), 760–772.