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Improving Aviation Safety with Information Visualization: Airflow Hazard Display for Pilots

Improving Aviation Safety with Information Visualization: Airflow Hazard Display for Pilots. Cecilia R. Aragon IEOR 170 UC Berkeley Spring 2006. Acknowledgments. This work was funded by the NASA Ames Full-Time Graduate Study Program (Ph.D. in Computer Science at UC Berkeley)

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Improving Aviation Safety with Information Visualization: Airflow Hazard Display for Pilots

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  1. Improving Aviation Safety with Information Visualization:Airflow Hazard Display for Pilots Cecilia R. Aragon IEOR 170 UC Berkeley Spring 2006

  2. Acknowledgments • This work was funded by the NASA Ames Full-Time Graduate Study Program (Ph.D. in Computer Science at UC Berkeley) • Thanks to my advisor at UC Berkeley, Professor Marti Hearst, and Navy flight test engineer Kurtis Long • Thanks to Advanced Rotorcraft Technology, Inc. for the use of their high-fidelity flight simulator IEOR 170

  3. Talk Outline • Introduction • Related Work • Preliminary Usability Study • Flight Simulation Usability Study • Conclusions and Further Work IEOR 170

  4. Introduction IEOR 170

  5. Motivation • Invisible airflow hazards cause aircraft accidents • Wind shear • Microbursts • Vortices (turbulence) • Downdrafts • Hot exhaust plumes • Crash of Delta Flight 191 at DFW 1985 (microburst) • NTSB database 1989-99 • 21,380 aircraft accidents • 2,098 turbulence/wind related IEOR 170

  6. IEOR 170

  7. IEOR 170

  8. The Problem • Invisible airflow hazards cause aircraft accidents • Air is invisible, so pilots can’t see hazards • If air flows past obstacles, flow will become more turbulent • Helicopters are especially vulnerable • Rotorcraft aerodynamics • Must operate in confined spaces • Operationally stressful conditions (EMS, military operations, shipboard operations) IEOR 170

  9. A Possible Solution • If pilots could see hazards, could take appropriate action • New lidar technology suggests a solution • Lidar (light detection and ranging) is essentially laser radar. A laser transmits light which is scattered by aerosols or air molecules and then collected by a sensor. Doppler lidar can detect the position and velocity of air particles. • My research focuses on the human interface -- how to visualize the sensor data for pilots -- too much information could overload pilot during critical moments IEOR 170

  10. Research Approach • User-centered (iterative) design process • Simulated interface for head-up display (HUD) based on lidar sensors that scan area ahead of helicopter and acquire airflow velocity data • Focused on helicopter-shipboard landings • Importance of realism: • Used actual flight test data from shipboard testing, high-fidelity helicopter simulator, experienced military and civilian helicopter pilots IEOR 170

  11. Rationale for using Shipboard Landings • Why focus on helicopter shipboard landings? • Problem is real: dangerous environment, want to improve safety • Ship superstructures always produce airwake • Large quantities of flight test data due to demanding environment IEOR 170

  12. Related Work IEOR 170

  13. Related Work • Flow visualization • Aviation displays • Navy “Dynamic Interface” flight tests IEOR 170

  14. Flow visualization • Detailed flow visualizations designed for scientists or engineers to analyze at length • Much work has been done in this area [Laramee et al 04] • Streamlines, contour lines (instantaneous flow) [Buning 89], [Strid et al 89], [Helman, Hesselink 91] • Spot noise [van Wijk 93], line integral convolution [Cabral, Leedom 93], flow volumes [Max, Becker, Crawfis 93], streaklines, timelines [Lane 96], moving textures [Max, Becker 95] (unsteady flow) • Automated detection of swirling flow [Haimes, Kenwright 95] • Terrain and turbulence visualization [LeClerc et al 02] • But usually no user tests [Laidlaw et al 01], andnot real-time IEOR 170

  15. Aviation displays • Synthetic and enhanced vision and augmented-reality displays [Hughes et al 02], [Parrish 03], [Spitzer et al 01], [Kramer 99], [Wickens 97] • Weather visualization, microburst detection [NASA AWIN, TPAWS], [Latorella 01], [Spirkovska 00], turbulence detection/prediction [Britt et al 02], [Kaplan 02] • Wake vortex visualization [Holforty 03] IEOR 170

  16. Navy Ship-Rotorcraft Compatibility Flight Testing (“Dynamic Interface”) • Very hazardous environment [Wilkinson et al 98] • Significant amounts of flight testing [Williams et al 99] • Recognized need for pilot testing • Goal: improve safety IEOR 170

  17. Current state of the art • Ship/helicopter flight tests, wind tunnel tests, CFD • Develop operational envelopes • Limit allowable landing conditions significantly • Envelopes are conservative for safety reasons • Pilots use intuition, but accidents still occur IEOR 170

  18. Preliminary Usability Study IEOR 170

  19. Preliminary usability study: goals • Assess efficacy of presenting airflow data in flight • Obtain expert feedback on presentation of sample hazard indicators to refine design choices IEOR 170

  20. Usability study: low-fidelity prototype • Rhino3D (3D CAD modeling program) • Easy access to ship models, ease of rapid prototyping • Chosen over 2D paper prototype, MS Flight Simulator, WildTangent, VRML-based tools, Java and Flash • Series of animations simulating helicopter’s final approach to landing • Different types of hazard indicators • Get pilot feedback and suggestions (interactive prototyping) IEOR 170

  21. Low-fi usability study screen shots IEOR 170

  22. Low-fi usability study screen shots IEOR 170

  23. Low-fi usability study participants • Navy helicopter test pilot, 2000 hours of flight time, 17 years experience • Navy helicopter flight test engineer, 2000+ hours of simulator time, 100 hours of flight time, 17 years experience • Civilian helicopter flight instructor, 1740 hours of flight time, 3 years experience IEOR 170

  24. Low-fi usability study results • All participants said they would use system • Feedback on hazard indicators: • Color: all preferred red/yellow only • Transparency: should be visible enough to get attention, but must be able to see visual cues behind it • Depth cueing: all preferred shadows below object, #1 said shadows alone sufficient. #2 wanted connecting line. No one wanted tick marks or numeric info. • Texture: #1, #2 didn’t want. #3 suggested striping • Shape: Rectilinear and cloud shapes favored. Keep it simple! Watch for conflicting HUD symbology. IEOR 170

  25. Low-fi usability study results (cont’d) • Motion is distracting! 1: absolutely no motion 2: didn’t like motion 3: slow rotation on surface of cloud OK, nothing fast IEOR 170

  26. Low-fi usability study conclusions • They want it! • Keep it simple • Color: red & yellow only (red = danger, yellow = caution) • Less complex shapes preferred • Use accepted symbology/metaphors • Watch for conflicting HUD symbology • Decision support system, not scientific visualization system • Show effects rather than causes • Don’t want distraction during high-workload task • Preference for static rather than dynamic indicators IEOR 170

  27. Flight Simulation Usability Study

  28. Flight Simulation Usability Study • Implement visual hazard display system in simulator based on results from low-fidelity prototype • Advanced Rotorcraft Technology, Inc. in Mountain View, CA, USA • High-fidelity helicopter flight simulator • Accurate aerodynamic models • Use existing ship and helicopter models, flight test data • Simulated hazardous conditions, create scenarios, validated by Navy pilots and flight engineers IEOR 170

  29. Flight Simulation Usability Study: Participants • 16 helicopter pilots • from all 5 branches of the military (Army, Navy, Air Force, Coast Guard, Marines) • civilian test pilots (NASA) • wide range of experience • 200 to 7,300 helicopter flight hours (median 2,250 hours) • 2 to 46 years of experience (median 13 years) • age 25 to 65 (median age 36) • No previous experience with airflow hazard visualization IEOR 170

  30. Simulation Experiment Design • 4 x 4 x 2 within-subjects design (each pilot flew the same approaches) • 4 shipboard approach scenarios • 4 landing difficulty levels (US Navy Pilot Rating Scale - PRS 1-4) • Each scenario was flown at all difficulty levels both with and without hazard indicators • Orders of flight were varied to control for learning effects IEOR 170

  31. Airflow Hazard Indicators in Simulator IEOR 170

  32. Landing difficulty Description Purpose Hazard indicator LD 1 No problems; minimal pilot effort required Control None LD 2 Moderate pilot effort required; most pilots able to land safely Test negative effects of hazard indicator Yellow/None LD 3 Maximum pilot effort required; repeated safe landings may not be possible Test benefit of hazard indicator Yellow/None LD 4 Controllability in question; safe landings not probable Test benefit of hazard indicator combined with pilot SOP Red/None Simulation Experiment Design IEOR 170

  33. IEOR 170

  34. Dependent Variables • Objective data: sampled at 10 Hz from simulator • aircraft velocity and position in x, y, z • lateral and longitudinal cyclic position and velocity • collective and pedal positions and velocities • landing gear forces and velocities • (A “crash” was defined as an impact with the ship deck with a vertical velocity of more than 12 fps) • Subjective data: 21-probe Likert-scale questionnaire administered to pilots after flight IEOR 170

  35. Hypotheses 1. Crash rate will be reduced by the presence of hazard indicator (LD 3). 2. Crashes will be eliminated by red hazard indicator if a standard operating procedure (SOP) is given to the pilots (LD 4). 3. Hazard indicator will not cause distraction or degradation in performance in situations where adequate performance is expected without indicator (LD 2). 4. Pilots will say they would use airflow hazard visualization system IEOR 170

  36. Hypothesis 1 confirmed • Presence of the hazard indicator reduces the frequency of crashes during simulated shipboard helicopter landings (t-test for paired samples, t=2.39, df=63, p=0.00985). 19% --> 6.3% IEOR 170

  37. Hypothesis 2 confirmed • Presence of the red hazard indicator combined with appropriate instructions to the pilot prevents crashes (t=4.39, df=63, p < 0.000022). 23%-->0% IEOR 170

  38. Hypothesis 3 • No negative effect of hazard indicator. 8%-->8% IEOR 170

  39. Hypothesis 3 (cont’d) • Pilots believe hazard indicators were not distracting (Probe 6 results). IEOR 170

  40. Hypothesis 4 confirmed • Pilots would use the system (Probe 21 results). IEOR 170

  41. Pilot workload:Power spectrum analysis of control inputs IEOR 170

  42. Go-Arounds (Aborted Landings) • Does the presence of the hazard indicator increase the go-around rate? • No significant differences found. IEOR 170

  43. Analysis by Pilot Experience Level • Does pilot experience level have any effect on the benefits produced by the hazard indicators? • To find out, divide pilots into three groups: IEOR 170

  44. Analysis by Pilot Experience Level (cont’d) • Same general trends -- but small sample size • No significant difference between the groups IEOR 170

  45. Analysis of Subjective Data • 94% found hazard indicators helpful IEOR 170

  46. Analysis of Subjective Data (cont’d) • Is motion (animation) helpful or distracting? IEOR 170

  47. Conclusions and Further Work IEOR 170

  48. Conclusions • Flight-deck visualization of airflow hazards yields a significant improvement in pilot ability to land safely under turbulent conditions in simulator • Type of visualization to improve operational safety much simpler than that required for analysis • Success of user-centered design procedure IEOR 170

  49. Further Work • Additional data analysis • Further studies • Steps toward system deployment • Extensions to other areas IEOR 170

  50. Additional data analysis • Power spectrum analysis of control input data • Flight path deviations and landing dispersion • Quantitative measures of landing quality IEOR 170

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