Aspects of
Adaptive
Automation
Support of Air
Traffic Controllers
David B. Kaber, Ph.D.
Department of Industrial
Engineering
NC State University
NASA Langley Core Competency Directors' Visit, September 4, 2003
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Introduction
Adaptive automation (AA) - Dynamic allocation of
machine system control to human operator or
computer over time with purpose of optimizing
performance (Kaber & Riley, 1999).
Example system:

Ground Collision Avoidance System
in fighter aircraft (e.g., F-16)



•

System monitors and predicts altitude.
Provides warnings to pilot.
Takes control of flight path.
Returns control to human pilot.
Historically implemented using binary approach
(Parasuraman et al., 1993; Hilburn et al., 1993):
Full Automation
Automation
Manual Control
Binary Approach
Full
Full
Full
Automation
Automation
Automation
Manual
Manual
Manual
Manual
Control
Control
Control
Control
Time
Why Study AA?
•
Potential solution to
problems with high-level,
static automation:


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High monitoring workload
(Wiener, 1988).
Operator complacency and
vigilance decrements
(Parasuraman et al., 1993).
Loss of situation awareness
(SA; Kaber et al., 2001).
Skill decay (Parasuraman,
2000; Shiff, 1983).
Benefits of AA found in
prior research:


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Lower operator perceived
workload in air traffic control
(ATC) simulation compared
to static automation (Hilburn
et al., 1997).
Questions remain as to
how to effectively
implement AA:

What functions should be
automated?
Who invokes automation?
What is basis for function
allocations (operator
workload, SA, etc.)?
(Wickens & Hollands, 2000)
Improvements in monitoring

performance in multiple task
scenario (using MAT Battery)

compared to static
automation (Parasuraman et
al., 1993; Hilburn et al.,
1993).
NASA Langley Core Competency Directors' Visit, September 4, 2003
Grants and Projects Completed
•
FY01:

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
FY02:

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“Human Response to Adaptive Automation of Information
Acquisition Functions and Later Stages of Information
Processing”
NAG-1-0139; $39,971 (PM: L.J. Prinzel)

“Authority in Adaptive Automation Applied to Various
Stages of Human-Machine System Information
Processing”
NAG-1-02056; $50,067 (PM: L.J. Prinzel)
NASA GSRP Grant:



“Comparison of Physiological and Secondary Task
Measures for Triggering Adaptive Automation”
NGT-1-01004; $66,000 (TPOC: L.J. Prinzel)
Period of performance: 5/15/01-5/14/04.
NASA Langley Core Competency Directors' Visit, September 4, 2003
•
AA of ATC Information
Processing Functions
Need - Compare
• Approach:
effectiveness of AA applied to  Developed PC-based simulation
of TRACON and secondary
various air traffic controller
monitoring task.
information processing
functions, including:
1.
2.
3.
4.
Information acquisition gathering of aircraft flight
parameter data. (Auto was like
radar tracking system.)
Information analysis summarizing data, including
conflict identification. (Auto was
like futuristic EDD or TPA.)
Decision making - sorting
aircraft in terms of priority for
clearance based on potential
conflicts. (Auto was like CAA.)
Action implementation automated clearances after
communication link established
(auto was like data link).
Keyhole
Automation Aid

ATC functions were adaptively
automated based on monitoring
task performance (objective
measure of workload).
Findings:

May have been due to
complexity of automation aid
and visual attention
demands of display.
Performance During Manual Minutes
60%
50%
Manual
Action
Decision
10%
Analysis
20%
Acquisition
30%
Manual
40%
Action

70%
Decision

Workload measure very
sensitive to auto state
changes when AA applied to
action implementation.
Analysis

Performance “best” when AA
applied to act of issuing
clearances (action
implementation).
Manual control periods as
part of AA of action
implementation better than
all other conditions and
completely manual control.
AA of information
acquisition significantly
reduced controller workload.
AA of information analysis
function produced highest
workload.
Acquisition


Average Percent Cleared
•
Workload-Matched AA of
Controller Functions
0%
Trial 1
Trial 2
Level of Automation
Information Acquisition Information Analysis
Action Implementation Manual
Decision Making
FY01 Outcomes
•
Conference proceeding paper:

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Clamann, M. P., Wright, M. C. & Kaber, D. B. (2002).
Comparison of performance effects of adaptive
automation applied to various stages of human-machine
system information processing. In Proceedings of the
46th Annual Meeting of the Human Factors and
Ergonomics Society (pp. 342-346). Santa Monica, CA:
Human Factors and Ergonomics Society.
NASA Technical Publication:

Kaber, D. B., Prinzel, L. J., Wright, M. C. & Clamann, M.
P. (2002). Workload-matched adaptive automation
support of air traffic controller information processing
stages
(Tech.
Pub.:
NASA/TP-2002-211932).
Washington, DC: NASA.
NASA Langley Core Competency Directors' Visit, September 4, 2003
FY01 Outcomes
•
Book chapter:

•
Kaber, D. B. & Wright, M. C. (in press). Adaptive
automation of stages of information processing and the
interplay with operator functional states. To appear in. G.
R. J. Hockey, A. W. K. Gaillard & O. Burov (Eds.), NATO
Advanced Research Workshop - Operator Functional
State: The Assessment and Prediction of Human
Performance
Degradation
in
Complex
Tasks.
Amsterdam: IOS Press, NATO Science Series.
Journal article:

Kaber, D. B., Wright, M. C. & Clamann, M. P. (in
revision). Adaptive automation of information processing
functions and operator stress. Submitted to Human
Factors (5/31/02).
NASA Langley Core Competency Directors' Visit, September 4, 2003
Authority in AA of
ATC Functions
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Need - Assess human
performance and workload
effects of various forms of
authority in AA of ATC
information processing
functions.
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•
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Automation
Feedback Display
Computer mandates computer has complete
control of dynamic function
allocations in ATC task.
Human input is irrelevant.
Computer suggestion computer initiates function
allocations but needs human
approval to invoke.
Approach:

Implemented same forms of
ATC function automation as
studied in FY01 (info
acquisition, info analysis,
decision making, action
implementation).
Developed new version of
ATC simulation including
feedback display on locus of
control and automation.

All computer mandates and
suggestions for automation
based on operator secondary
task performance (workload).
Findings:

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Automation of data gathering
function (information
acquisition) yielded best
performance.
Automation of sorting of
aircraft for clearance
(decision making) produced
worst performance.
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Performance hindered by
complexity of automation.
High visual display demand.
Computer suggestions of
automation better than
mandates, in general.
However, when performing
task manually, mandates of
auto better than suggestions.
No differences in workload
among types of automation of
forms of authority.
Average Percent Aircraft Cleared
•
100%
90%
80%
70%
60%
Auto AA
50%
Manual AA
40%
30%
20%
10%
0%
Information
Acquisition
Information
Analysis
Decision Making
Action
Implementation
Autom ation Type
100%
Average Percent Aircraft Cleared
Invocation
Authority in AA
90%
80%
70%
60%
Auto AA
50%
Manual AA
40%
30%
20%
10%
0%
Mandate
Suggest
Authority
FY02 Outcomes
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Masters thesis:

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Technical Report:

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Clamann, M. P. (2002). “The Effects of Intermediate
Levels of Invocation Authority on Adaptive Automation of
Various Stages of Information Processing.”
Kaber, D. B. & Clamann, M. P. (March 2003). Authority in
adaptive automation applied to various stages of humanmachine system information processing. (Final Rep.:
NASA Langley Research Center Grant #NAG-1-02056).
Hampton, VA: NASA Langley Research Center.
Conference proceeding paper:

Clamann, M. P. & Kaber, D. B. (in press). Authority in
adaptive automation applied to various stages of humanmachine system information processing. To appear in
Proceedings of the 47th Annual Meeting of the Human
Factors and Ergonomics Society. Santa Monica, CA:
Human Factors and Ergonomics Society.
NASA Langley Core Competency Directors' Visit, September 4, 2003
Current Research: SA and AA
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FY03:
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
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“A Situation Awareness-Based Approach to Adaptive
Automation”
NAG-1-03022; $100,000 (PM: L.J. Prinzel)
Subcontract to SA Technologies, Inc. for consultation on
development of real-time measure of SA ($9,984).
Needs:



Define measure of SA in ATC sensitive to dynamic
function allocations as part of AA.
Empirically assess utility of controller SA measure for
classifying forms of AA of info processing functions.
Describe impact of AA of ATC information processing
functions on controller SA.
NASA Langley Core Competency Directors' Visit, September 4, 2003
SA and AA
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Approach:
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Developed enhanced version
of TRACON simulation based
on input from PM.
Studied task analyses on en
route control and TRACON
(e.g., Endsley & Rodgers,
1994; Endsley & Jones,
1995).
Developed query-based
measure of controller SA (like
Situation Awareness Global
Assessment Technique
(Endsley, 1995)).
Currently conducting
experiment:

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Eight trained subjects
working over 3 week period.
Experience all forms of AA of
ATC task.
SA quizzes posed during
experimental trials:
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Simulation frozen at random
points in time.
Subjects respond to
questions on state of aircraft
and environment.
Responses compared with
actual state of task to
evaluate to accuracy of SA.
Hypotheses:


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Lower levels of automation,
including information
acquisition and action
implementation expected to
support controller SA.
Manual control periods as
part of AA expected to
increase controller SA.
Information analysis
automation may degrade
system state comprehension.
Decision making auto may
degrade operator projection
of future system states.
Enhanced ATC Simulation
Data box
(flight
parameters).
Trajectory
Projection
Aid.
Radar scan
line.
Control box
for aircraft
clearances.
Command
history and
feedback.
Automation
aid & status
display.
Aircraft being
cleared.
NASA Langley Core Competency Directors' Visit, September 4, 2003
FY03 Outcomes
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Masters thesis:

•
McClernon, C. K. (in preparation). “Situation Awareness
Effects of Adaptive Automation of Various Air Traffic
Control Information Processing Functions”
Journal Article:

Kaber, D. B. & Endsley, M. R. (in press). The effects of
level of automation and adaptive automation on human
performance, situation awareness and workload in a
dynamic control task. Submitted to Theoretical Issues in
Ergonomics Science (12/15/01).
NASA Langley Core Competency Directors' Visit, September 4, 2003
Future Research
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Develop real-time probe measure of controller SA
for use in research on AA.
Conduct experiment involving real-time assessment
of controller SA as basis for triggering dynamic
function allocations (manual and automated control
allocations).
Compare results of workload-matched AA and SAmatched AA support on controller information
processing.
Assess performance, workload and SA effects of
AA of multiple ATC information processing
functions, simultaneously.
NASA Langley Core Competency Directors' Visit, September 4, 2003
Contact and Web Site Information
•
David B. Kaber, Ph.D.
Associate Professor
Department of Industrial
Engineering
North Carolina State University
2401 Stinson Dr.
328 Riddick Labs
Box 7906
Raleigh, NC 27695-7906
•
Faculty Web Page:
http://www.ie.ncsu.edu/kaber/
•
FY01 Results Page:
http://people.engr.ncsu.edu/dbk
aber/AA/
Tel.: (919) 515-2362
FAX: (919) 515-5281
e-mail: dbkaber@eos.ncsu.edu
NASA Langley Core Competency Directors' Visit, September 4, 2003