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AIAA Unmanned Aerial Vehicle, Systems, Technology, and Operations Conference Sense, Detect and Avoid Workshop - PowerPoint PPT Presentation

AIAA Unmanned Aerial Vehicle, Systems, Technology, and Operations Conference Sense, Detect and Avoid Workshop. Collision Avoidance. Session Outline. IntroductionJohn Price Collision Avoidance – US Civil AirspaceJohn Price Lessons Learned

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AIAA Unmanned Aerial Vehicle, Systems, Technology, and Operations Conference Sense, Detect and Avoid Workshop

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AIAAUnmanned Aerial Vehicle, Systems, Technology, and Operations ConferenceSense, Detect and Avoid Workshop

Collision Avoidance


Session Outline

IntroductionJohn Price

Collision Avoidance – US Civil AirspaceJohn Price

Lessons Learned

Challenges for Different Classes of Airspace

Collision Avoidance – Restricted AirspaceBruce Clough

Collision Avoidance – European PerspectiveDick Wagaman


FAA Data References

  • Advisory Circular AC 90-48C (Pilots’ Role in Collision Avoidance), FAA, U.S. Dept. of Transportation

  • FAA-P-8740-51 FAA Accident Prevention program

  • FAA Aviation Safety Data

  • Aviation Safety Network

  • NTSB Aviation Safety Data – NMACS Database


FAA MAC Statistics – 1978 to 1982

  • A Total of 152 Midair Collisions (MAC) Occurred in the United States From 1978 Through October 1982 Resulting in 377 Fatalities.

  • The Yearly Statistics Remained Fairly Constant Throughout This Approximately 5 Years.

  • During This Same Time Period There Were 2,241 Reported Near Midair Collisions (NMAC).

  • Statistics Indicate That the Majority of These Midair Collisions and Near Midair Collisions Occurred in Good Weather and During the Hours of Daylight.

  • FAA Has Since Introduced Several Programs With a Greater Emphasis on the Need for Recognition of the Human Factors Associated With Midair Conflicts.


FAA NMAC Statistics – 1987 to 1996

* Critical: Less than 100 feet aircraft separation

** Potential: Less than 500 feet aircraft separation

Measures Taken by FAA And Airline Industry Show Steady Improvements


Comparison of NMAC by Operator Type

  • GA Is the Biggest Culprit, but All Aircraft Types Had Been Involved Including A/C to A/C (Ranked #5)

5

1

3

2

6

4

7


Comparison of NMAC by Flight Plan

  • Neither Flight Plan Is Free From NMAC. However, IFR/VFR Has the Highest Incident Rates While IFR/IFR Has the Lowest Rates.


Some Data on Dutch Air Traffic Incidents

  • Similar Trends As USA:

    • GA Has the Highest Incident Rates

    • More Occurrence Under VMC (VFR) than IMC (IFR)

    • Most Frequently Occurred Under 3,000 Feet Surrounding Airports


Lessons Learned

  • Nearly All Midair Collisions Occur During Daylight Hours and in VFR Conditions. Majority of These Happen Within Five Miles of an Airport, in the Areas of Greatest Traffic Concentration

  • Statistics on 105 In-flight Collisions Show That:

    • 82% Were at Overtaking

    • 5% Were From a Head-on Angle

    • 77% Occurred at or Below 3,000 feet And 49% at or Below 500 feet

  • Increasing Traffic and Higher Closing Speeds Pose Increased Potential of Midair Collisions. It Takes a Minimum of 10 seconds, Says the FAA, For a Pilot to Spot Traffic, Identify It, Realize It's a Collision Threat, React, and Have His Aircraft Respond.

  • The Reason Most Often Noted in the Statistics Reads: "Failure of Pilot to See Other Aircraft." In Most Cases at Least One of the Pilots Involved Could Have Seen the Other in Time to Avoid Contact, If He Had Used His Eyes Properly.


Military Data References

  • Jet Safety.com - F-16 Crash Site

  • Jet Safety.com - USN, USMC, USA data


Military Aircraft Accident (F-16, FY2001)


Lessons Learned

  • Most Military Aircraft Midair Collisions Occur During Training. Close Proximity and Aggressive Maneuvers Create Special Challenges As Relative Geometry/velocity Can Be Such That One Would Fail to See or Maintain Safe Distance From the Others. Exercises That Involve Red and Blue Forces Can Further Create Chaos, Thus Increasing the Risk of MAC.


UAV Population Vs Airspace Class

28 % of UAVs Class A

64% of UAVs Class E

8% of UAVs Class G


UAVs in Class A Airspace

  • Situation:

    • Normally a Highly Structured Environment

    • Undergoing Change – GATM

    • Technology Challenge – SWAP and Cost

    • Domain of Larger more Expensive UAVs

  • Sensors/Equipment:

    • Mode S Extended

    • ADS-B

    • TCAS II ?

    • Dual GPS/INS

    • Redundant Airdata/Altimeters


UAVs in Class A Airspace

  • Issues:

    • “Sense and Avoid” vs “Broadcast your Position and let Other Avoid You”

      • May help UAVs, but does it add other restrictions

    • “Man in the Loop” vs “Autonomous Operations”

    • Finding Appropriate Space of the UAV for Equipment

    • Cost


UAVs in Class E Airspace

  • Situation:

    • Not Well Structured Environment – Largely Domain of General Aviation

    • Technology Challenge – Performance, SWAP, Cost

    • Generally Occupied by Smaller, Less Expensive UAVs

  • Sensors/Equipments:

    • EO/IR

    • Radar

    • Ladar


UAVs in Class E Airspace

  • Issues:

    • “…comparable see-and-avoid requirements for manned A/C”

      • What is the detection range and reliability of the human pilot in various weather and lighting conditions?

      • What is his field of regard?

      • What is his reaction time

      • Should we strive to exceed manned performance

    • “Man in the Loop” vs “Autonomous Operations”

    • Finding appropriate on the UAV for Sensors

    • Cost


Summary

  • Data and lessons learned indicate that our biggest challenge will be UAV integration with General Aviation aircraft

  • The old question of “man in the loop” vs “autonomous operations” plays across all classes of airspace

  • Technology has provided us solutions, but can we fit them in the UAV and still retain a reasonable payload volume? Can we afford them?


Backup Slides


OPEC Model Results for F-16

This quantifies “…same range…”


Line-of-sight

Human Observer

AFRL/SN OPEC Model

  • Models human detection

    • Backgrounds

    • Lighting

      • Sunshine

      • Earthshine

      • Skyshine

    • Aircraft models

      • 10,000 facets

      • Dynamics

    • Atmospheric transmission

  • Validated

    • 937 human trials

    • Model calibrated using trials


Predator Analysis Results


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