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Visual Detection of Small Unmanned Aircraft System: Modeling the Limits of Human Pilots

Journal of Intelligent & Robotic Systems volume 99, pages 933–947 (2020)Cite this article

Abstract

Every month, the Federal Aviation Administration (FAA) receives over 100 reports of small Unmanned Aircraft Systems (sUAS) operating in airspace where they do not belong and the industry has not deployed any specific, ubiquitous solution to preclude this potential collision hazard for pilots of manned aircraft [1]. The purpose of this research is to determine the key physical attributes and construct a new mathematical model to determine the probability of visual detection and avoidance of sUAS. Using the Monte Carlo simulation method, this study provided a means for addressing the effects of uncertainty in the uncontrollable inputs. As a result, it produced a set of probability curves for various operating scenarios and depicted the likelihood of visually detecting a small, unmanned aircraft in time to avoid colliding with it. This study suggested the probability of detecting a sUAS in time to avoid a collision, in all cases modeled during the study, is far less than 50%. The probability was well under 10% for sUAS aircraft similar to the products used by many recreational and hobby operators. This study indicated the see-and-avoid is not a reliable technique for collision prevention by manned-aircraft pilots with small, unmanned aircraft and call for regulators and the industry’s deployment of alternative methods.

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References

  1. Federal Aviation Administration (FAA): sUAS sightings report. Retrieved from https://www.faa.gov/uas/resources/public_records/uas_sightings_report/ (2019)

  2. Federal Aviation Administration (FAA): FAA aerospace forecast: Fiscal years 2019–2039. Retrieved from https://www.faa.gov/data_research/aviation/aerospace_forecasts/media/FAA_Aerospace_Forecasts_FY_2019-2039.pdf (2019)

  3. Federal Aviation Administration (FAA). Operation and certification of small unmanned aircraft systems. Federal Register, 81(124), June 28. Retrieved from https://www.federalregister.gov/documents/2016/06/28/2016-15079/operation-and-certification-of-small-unmanned-aircraft-systems (2016)

  4. Federal Aviation Administration (FAA). Getting started. Retrieved from: https://www.faa.gov/uas/getting_started/ (2016)

  5. Federal Aviation Administration (FAA): FAA releases updated drone sighting reports. Retrieved from https://www.faa.gov/news/updates/ ?newsId=87565 (2017)

  6. Federal Aviation Administration (FAA): FAA releases updated sUAS sighting reports. Retrieved from https://www.faa.gov/news/updates/?newsId=85229&omniRss=news_updatesAoc&cid=101_N_U (2016)

  7. Loffi, J., Wallace, R. J., Ison, C. S.: Analysis of the Federal Aviation Administration’s sUAS regulations for hobbyist and recreational users. International Journal of Aviation, Aeronautics, and Aerospace, 3(1), Article 3. (2016)

  8. Operation and Certification of Small Unmanned Aircraft Systems: Final Rule. 81 Fed. Reg. § 124. Retrieved from https://www.gpo.gov/fdsys/pkg/FR- 2016-06-28/pdf/2016-15079.pdf (2016)

  9. Esler, D.: What a business aviation flight department needs to know about sUAS. Aviation Week & Space Technology, September 25. Retrieved from http://aviationweek.com/bca/what-business-aviation-flight-department-needs-know-about-uas (2015)

  10. Mcfadyen, A., Mejias, L.: A survey of autonomous vision-based see and avoid for unmanned aircraft systems. Progress in Aerospace Systems. 80, 1–17 (2015)

    Google Scholar 

  11. Yu, X., Zhang, Y.: Sense and avoid technologies with applications to unmanned aircraft systems: review and prospects. Prog. Aerosp. Sci. 74(2015), 152–166 (2015)

    Article  Google Scholar 

  12. Consiglio, M. C., Chamberlain, J. P., Muñoz, C. A., Hoffler, K. D.: Concept of integration for sUAS operations in the NAS (NASA Technical Report: NF1676L-13199). Report presented at ICAS 2012 – 28th Congress of the International Council of the Aeronautical Sciences, Brisbane, Australia. (2012)

  13. Lai, J., Mejias, L., Ford, J.J.: Airborne vision-based collision-detection system. Journal of Field Robotics. 28(2), 137–157 (2011)

    Article  Google Scholar 

  14. Gettinger, D., & Michel, A. H.: Drone sightings and close encounters: An analysis. Annondale-on-Hudson, NY:Center for the Study of the Drone at Bard College. Retrieved from http://dronecenter.bard.edu/files/2015/12/12-11-Drone-Sightings-and-Close-Encounters.pdf (2015)

  15. Loffi, J. M., Wallace, R. J., Jacob, J. D., Dunlap, J. C.: Seeing the threat: Pilot visual detection of small unmanned aircraft systems in visual meteorological conditions. International Journal of Aviation, Aeronautics, and Aerospace, 3(3), Article 13. (2016)

  16. Wallace, R.J., Loffi, J.M., Vance, S.M., Jacob, J., Dunlap, J.C., Mitchell, T.A.: Pilot visual detection of small unmanned aircraft systems (sUAS) equipped with strobe lighting. Journal of Aviation Technology and Engineering. 7(2), 57–66 (2018)

    Article  Google Scholar 

  17. Howett, G.L.: Size of Letters Required for Visibility as a Function of Viewing Distance and Observer Visual Acuity (National Bureau of Standards Technical Note 1180). Government Printing Office, Washington, DC (1983)

    Google Scholar 

  18. Gibb, R., Gray, R., Scharff, L.: Aviation Visual Perception: Research, Misperception, and Mishaps. Ashgate Publishing, Burlington, VT (2010)

    Google Scholar 

  19. Hirsch, J., Curcio, C.A.: The spatial resolution capacity of human foveal retina. Vision Research Journal. 29(9), 1095–1101 (1989)

    Article  Google Scholar 

  20. Westheimer, G.: Visual acuity and hyperacuity. Handbook of Optics. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.438.6210 &rep=rep1&type=pdf (2010)

  21. Wulfeck, J.W., Weisz, A., Raben, M.W.: Vision in Military Aviation (United States air Force Report 58–399). Wright Air Development Center, Air Research and Development Command (1958)

    Google Scholar 

  22. Greening, C.P.: Mathematical modeling of air-to-ground target acquisition. Hum. Factors. 18(2), 111–148 (1976)

    Article  Google Scholar 

  23. Akerman III, A., Kinzly, R.E.: Predicting aircraft visibility. Hum. Factors. 21(3), 277–291 (1979)

    Article  Google Scholar 

  24. Chisum, G. T.: Prediction of airborne target detection. (Naval Air Development Center Report NADC-77102-40). (1977)

  25. Poe, A. C.: A model for visual detection of aircraft by ground observers. U.S. Army Missile Command technical report RD-75-30. Washington, DC. (1974)

  26. Andrews, J. W.: Air-to-air visual acquisition performance with TCAS II. (MIT Lincoln Laboratory Report No. ATC-130). (1984)

  27. Andrews, J. W.: Air-to-air visual acquisition handbook. (MIT Lincoln Laboratory Report No. ATC-151). (1991)

  28. Graham, W.: See and avoid/cockpit visibility. (FAA technical center report. DOT/FAA/CT-TN89/18). Atlantic City. NJ. (1989)

  29. Australian Transportation Safety Bureau (ATSB): Midair collisions: Limitations of the see-and-avoid concept. (Australian Transportation Safety Bureau report, ISBN 0 642 16089 9) (1991)

  30. Regan, D.: Human Perception of Objects. Sinauer Associates, Sunderland (2000)

    Google Scholar 

  31. McKee, S.P., Nakayama, K.: The detection of motion in the peripheral visual field. Vis. Res. 24(1), 25–32 (1984)

    Article  Google Scholar 

  32. Andrews, J. W.: Unalerted air-to-air visual acquisition (MIT Lincoln Laboratory Report No. ATC-152). (1991)

  33. Papadopoulos, C.E., Yeung, H.: Uncertainty estimation and Monte Carlo simulation method. Flow Meas. Instrum. 12(2001), 291–298 (2001)

    Article  Google Scholar 

  34. Veneri, G., Pretegiani, E., Federighi, P., Rosini, P., Federico, A., Rufa, A.: Evaluating human visual search performance by Monte Carlo methods and heuristic model. Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 1–4. (2010)

  35. Cohn, T.E.: Absolute threshold: analysis in terms of uncertainty. J. Opt. Soc. Am. 71(6), 783–785 (1981)

    Article  Google Scholar 

  36. Morris, C.: Midair collisions: limitations of the see-and-avoid concept in civil aviation. Aviat. Space Environ. Med. 76(4), 357–365 (2005)

    Google Scholar 

  37. Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., Martin, K.: An Introduction to Management Science: Quantitative Approaches to Decision Making. Mason, OH, South-Western Cengage Learning (2016)

    Google Scholar 

  38. Jones, D.B., Freitag, M., Collyer, S.C.: D. C. Middledauff (Ed.). Air-to- Ground Target Acquisition Source Book: a Review of the Literature. Orlando, FL, Martin Marietta Corp (1974)

    Book  Google Scholar 

  39. Volpe National Transportation Systems Center: Presentation on the Number of Mandatory Occurrence Reports Filed for sUAS Sightings per Month. Unpublished analysis of Federal Aviation Administration Mandatory Occurrence Reports data (2017)

  40. Federal Aviation Administration (FAA): Pilot’s role in collision avoidance (AC No. 90-48D). (2016)

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Authors and Affiliations

  1. Embry-Riddle Aeronautical University, 600 S. Clyde Morris Blvd. Daytona Beach, Florida, USA

    Gregory Stephen Woo, Dothang Truong & Woojin Choi

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  1. Gregory Stephen Woo
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  2. Dothang Truong
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  3. Woojin Choi
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Correspondence to Dothang Truong.

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Woo, G.S., Truong, D. & Choi, W. Visual Detection of Small Unmanned Aircraft System: Modeling the Limits of Human Pilots. J Intell Robot Syst 99, 933–947 (2020). https://doi.org/10.1007/s10846-020-01152-w

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  • DOI: https://doi.org/10.1007/s10846-020-01152-w

Keywords

  • Small unmanned aircraft system
  • Unmanned aerial vehicle
  • Visual detection
  • See-and-avoid
  • Monte Carlo simulation