Developing a Computational Model of the Pilot’s Best Possible Expectation of Aircraft State Given...

Developing a Computational Model of the Pilot’s Best Possible Expectation of Aircraft

State Given Vestibular and Visual Cues

Can Onur

Master’s Thesis Defence

Committee Members

Dr. Amy Pritchett (Chair)

Dr. Eric Johnson

Dr. Santosh Mathan

This work is funded by the NASA Aviation Safety Program

Overview

Introduction and Motivation

Objectives

1 – Developing the Model

2 – Verify & Validate the Model

Conclusion

Spatial Disorientation in Aviation

3

Spatial Disorientation (SD): occurs when a pilot fails to properly sense the aircraft’s motion, position or attitude

Spatial Disorientation (SD) Loss of Control (LOC)

SKYbraryFlight Safety Foundation, 1992

“The chance of a pilot experiencing SD during their career is in the order of 90 to 100 per cent.”

Australian Transport Safety Bureau, 2007

leads

Loss of Control Accidents

4

( )

Boeing, 2013Bateman, et al., 2011

Fatal Accidents – Worldwide Commercial Jet Fleet – 2003 Through 2012

Aircraft State Perception and Susceptibility to SD

Human vestibular system evolved in a 1-g environment (walking, running, sitting)

5

Contributor #1: The Vestibular System

Limitations:+ Threshold Values (No sensation in case of a sub-t maneuver)+ Sensor Dynamics (Signals exponentially decay during a sustained stimulus)

Vestibular System

Semi-Circular Canals Otolith

Vestibular Illusions in Aviation

Limitations are causing illusions (especially when visual cues are lacking)

6

Somatogyral Illusions (mostly due to SCC)(e.g., dead-man’s spiral, leans)

Somatogravic Illusions (mostly due to otolith)(e.g., false sensation of pitch)

7


Flight desk displays are the most reliable source of information (If scanned)

Contributor #2: The Visual System

Contributor #3: Pilot Knowledge of the Aircraft Dynamics

• Pilot expertise through training and experience

• Ability to generate internal expectations of the aircraft state based on sensory cues

8


Flight desk displays are the most reliable source of information (If scanned)

• Pilot Expertise through training and experience

• Ability to generate internal expectations of the aircraft state based on sensory cues

Contributor #2: The Visual System

Contributor #3: The Knowledge of the Aircraft Dynamics

Problem 1: How does a pilot incorporate these sensory inputs and the expertise into their expectation of spatial orientation?

Countermeasures to SD

+ Training Simulators “Believe your flight instruments”

trainings

+ Alerting Systems Auditory Tactile Visual

+ Flight Deck Display Designs NextGen Flight Deck Displays Software/Hardware Enhancements

9

Countermeasures to SD

+ Training Simulators “Believe your flight instruments”

trainings

+ Alerting Systems Auditory Tactile Visual

+ Flight Deck Display Designs NextGen Flight Deck Displays Software/Hardware Enhancements

10

Problem 2: How to identify the pilot’s information requirements?

Problem 3: How to help analyze potential flight deck technology interventions?

Overview


Objectives



Conclusion

Objectives

1. Develop a computational model (Model-Based Observer) to predict the pilot’s best possible expectation of the aircraft state given vestibular and visual cues.

2. Parameterize and verify & validate the model using: Preliminary scenarios (brief turns, banking maneuvers etc.) Empirical data from the literature

12

Overview


Objectives



Conclusion

Best Possible

⌃

⌃ ⌃

The Model-Based Observer

14

1 Aircraft Dynamics

2Measurements of Aircraft State

3Pilot’s “Internal Simulation” of the Aircraft

4

Discrepancy between estimated and actual measurements

Discrete Visual Scanning Model

15

…

timeline

Discrete Measurements

+ Measurement error (v) for visual scan Sensor noise Error due to display design (thick needle -> elevated error) Pilot perception error

+ MBO stable for a range of error values

Vestibular Model

16

Continuous measurements of the aircraft states and state derivatives

Merfeld 1990, Grant & Best 1986

+ Measurement error (v) for vestibular model+ Error values given in the previous work

The SCC Dynamics(based on Merfeld’s model)

The Otolith Dynamics (based on Grant & Best’s model)

* *==ySCC yOTO

MBO Structure: Hybrid Kalman Filter

+ Continuous-time non-linear system dynamics (aircraft dynamics) P -> the error covariance matrix (a measure of the estimated accuracy of the state estimate)

17

(Discrete RE)

(Continuous RE)

(Discrete Kalman Gain )

(Continuous Kalman Gain)

Kd & Kc are the optimal gains (Kalman Gains) to generate the best possible estimate

Overview


Objectives



Conclusion

Verification of the Model Components

i. The Hybrid Kalman Filterii. The Semi-Circular Canal Modeliii. The Otolith Model

19

The MBO components

Implemented in aircraft simulation to emulate turbulence

iv. The Dryden Model

i) Kalman Filter Verification

20

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

valu

e (r

ad/s

)

Kalman filter estimate

SCC afferences

actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

erro

r va

lue

+2sigma

-2sigmaEstimate error

Gaussian Error is used in the Kalman Filter

Estimation error is expected to be Gaussian

Diagonal Entries = Predicted estimation error variances of each state

i) Kalman Filter Verification

21

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

valu

e (r

ad/s

)


SCC afferences

actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

erro

r va

lue

+2sigma


Gaussian Error is used in the Kalman Filter

Estimation error is expected to be Gaussian

-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.030

50

100

150

200

250

300

350

400

450q (roll rate) - estimation error distribution

Mean = -0.00046Std. Dev. = 0.00788Median = -0.00015

Diagonal Entries = Predicted estimation error variances of each state

i) Kalman Filter Verification – Measurements Impact

22

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)q

- va

lue

(rad

/s)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

erro

r va

lue

+2sigma


0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

valu

e (r

ad/s

)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

q -

erro

r va

lue

+2sigma

-2sigma

Estimate error

Above-Threshold(no visual)

Sub-Threshold(no visual – no SCC)

ii) SCC Model Verification

23

0 5 10 15 20 25 30 35 40 45-0.05

0

0.05

0.1

0.15

0.2

0.25

Time (s)

Angula

r V

elo

city (

rad/s

)

Stimulus (step)

Canal Afferent Response

10 15 20 25 30 35 40 45 50-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Time [s]

Ang.

Velo

city (

p)

[rad/s

]

Stimulus (Actual Aircraft State)

Canal Afferent Response

A previously developed model’s responses (Borah et. al, 1988)

ii) SCC Model Verification (sub-threshold behavior)

+ The SCC does not provide accurate information in case of a sub-threshold maneuver Does it provide no measurement? (is it completely inactive) Does it provide measurements of zero?

24

ii) SCC Model Verification (sub-threshold behavior)

25

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (

rad/s

)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

err

or

valu

e

+2sigma


0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (

rad/s

)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

err

or

valu

e

+2sigma

-2sigma

Estimate error

(1) The SCC provides measurements of zero when maneuver is sub-threshold

(2) The SCC provides no measurement when maneuver is sub-threshold

Above-T Sub-ThresholdSub-Threshold

iii) The Otolith Model Verification

26

0 5 10 15 20 25 30 35 40-5

0

5

10

time (s)

thet

a -

valu

e (d

eg)


actual

0 5 10 15 20 25 30 35 40-100

-50

0

50

100

time (s)

thet

a -

erro

r va

lue

+2sigma

-2sigma

Estimate error

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2x 10

-4

time (s)

sfx

otolith afferent firing rate

0 5 10 15 20 25 30 35 402

3

4

5

6

7

8

9

time (s)lin

ear

acce

lera

tion

Forward Acceleration Experiment

sf_x = –θ.g – ů

iii) The Otolith Model Verification

27

0 5 10 15 20 25 30 35 40-6

-4

-2

0

2

4

6x 10

-4

time (s)sf x

otolith afferent firing rate

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

2

3

4

time (s)

linea

r ac

cele

ratio

n

0 5 10 15 20 25 30 35 40-5

0

5

10

15

time (s)

thet

a -

valu

e (d

eg)


actual

0 5 10 15 20 25 30 35 40-100

-50

0

50

100

time (s)

thet

a -

erro

r va

lue

+2sigma

-2sigma

Estimate error

Pitch-up Experiment

sf_x = –θ.g – ů

(While we didn’t command a deceleration the pitch up did cause it)

iv) The Dryden Model Verification

28

0 1 2 3 4 5 6 7 8 9 10

-20

0

20

WMC Dryden Implementation

V

x

Vy

Vz

0 1 2 3 4 5 6 7 8 9 10

-20

0

20

Matlab Dryden Built-in

Vx

Vy

Vz

0 20 40 60 80 100 1200

200

400

600

800WMC Dryden Implementation

Frequency (bins)

Magnitude

V - linear component gusts magnitude spectrum

0 20 40 60 80 100 1200

200

400

600

800Matlab Dryden Built-in

Frequency (bins)

Magnitude

0 1 2 3 4 5 6 7 8 9 10

-0.1

-0.05

0

0.05

0.1


wx

wy

wz

0 1 2 3 4 5 6 7 8 9 10

-0.1

-0.05

0

0.05

0.1


wx

wy

wz

0 20 40 60 80 100 1200

0.5

1


Frequency (bins)

Magnitude

W - angular component gusts magnitude spectrum

0 20 40 60 80 100 1200

0.5

1


Frequency (bins)

Magnitude

Linear gust verification

Angular gust verification

Validation of the Integrated MBO

Ability of the model to predict known problems with pilot SD.

Specifically, to reproduce the illusions that occur due to vestibular limitations when visual cues are lacking.

Impact of visual scanning. Do the visual corrections help overcome the illusion?

29

Somatogyral Illusion

30

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (r

ad/s

)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

erro

r va

lue

+2sigma


Sub-Threshold BankingAbove-Threshold Banking

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (r

ad/s

)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

erro

r va

lue

+2sigma

-2sigma

Estimate error

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (r

ad/s

)


actual

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

erro

r va

lue

+2sigma


Visual Corrections on Somatogyral Illusion

31

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (r

ad/s

)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

erro

r va

lue

+2sigma


0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

valu

e (r

ad/s

)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-0.1

-0.05

0

0.05

0.1

time (s)

p -

erro

r va

lue

+2sigma


Sub-Threshold BankingAbove-Threshold Banking

Somatogravic Illusion

32

0 5 10 15 20 25 30 35 40-10

-5

0

5

time (s)

thet

a -

valu

e (d

eg)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-100

-50

0

50

100

time (s)

thet

a -

erro

r va

lue

+2sigma


0 5 10 15 20 25 30 35 40-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

time (s)

linea

r ac

cele

ratio

n

0 5 10 15 20 25 30 35 401

2

3

4

5

6

7

8

9

10

time (s)

linea

r ac

cele

ratio

n

x-component (ft/sec2)

0 5 10 15 20 25 30 35 40-5

0

5

10

time (s)

thet

a -

valu

e (d

eg)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-100

-50

0

50

100

time (s)

thet

a -

erro

r va

lue

+2sigma

-2sigma

Estimate error

Forward Acceleration

Deceleration

Visual Corrections on Somatogravic Illusion

33

0 5 10 15 20 25 30 35 40-10

-5

0

5

10

time (s)

thet

a -

valu

e (d

eg)

Pilot Expectation

Aircraft State

0 5 10 15 20 25 30 35 40-20

-10

0

10

20

time (s)

thet

a -

erro

r va

lue

+2sigma

-2sigma

Estimate error

0 5 10 15 20 25 30 35 401

2

3

4

5

6

7

8

9

10

time (s)lin

ear

acce

lera

tion

x-component (ft/sec2)

Forward Acceleration

Overview


Objectives



Conclusion

Examples & Potential Design Interventions

Concerns Model Representation Potential Design Intervention

Pilot distraction No scan for some or all of the instruments

Alerting for rare (and rarely-sampled) flight

conditions.

Inaccurate pilot perception of state from instruments

Inaccurate or noisy measurement

More accurate/higher resolution presentation of

key aircraft states.

35

Summary & Contributions

The MBO enables several analyses:

Investigate the mechanism of spatial disorientation

Predict the best possible pilot’s expectations of the aircraft state with a given visual scan pattern

Identify the pilot’s information requirements (regarding the appropriate energy-state and attitude awareness)

Analyze potential flight deck technology interventions and/or provide design insights for the NextGen flight deck display designs.

36

Thank You!

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Developing a Computational Model of the Pilot’s Best Possible Expectation of Aircraft State Given...

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