Post on 24-Dec-2015
NASA
Self-Separation from the Air and Ground Self-Separation from the Air and Ground PerspectivePerspective
Margaret-Anne Mackintosh, Melisa Dunbar, Sandra Lozito, Patricia Cashion, Alison McGann, Victoria Dulchinos
NASA Ames Research Center mmackintosh@mail.arc.nasa.gov
Rob Ruigrok, Jacco Hoekstra, Ronald Van Gent National Aerospace Laboratory, NLR
ruigrok@nlr.nl
NASA
IntroductionIntroduction
• NLR: Free Flight with Airborne Separation Assurance
– Air perspective
• NASA Ames: Air-Ground Integration Study
– Air and Ground perspective
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study IntroductionIntroduction
• NLR: Free Flight with Airborne Separation Assurance– Free Flight Concept Development:
• Traffic & Experiment Manager off-line simulations• Find a suitable base-line concept
– Free Flight Safety Analysis:• Traffic Organization and Perturbation AnalyZer (TOPAZ)• Predict critical non-nominal situations
– Free Flight Human-in-the-Loop Simulation Experiment• NLR’s Research Flight Simulator• Human Factors Issues• Validation of concept with Human-in-the-Loop
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study MethodsMethods
• Probe the limits– No Air Traffic Control– Air crew responsible for traffic separation
• All aircraft in scenario fully equipped– Automatic Dependent Surveillance - Broadcast (ADS-B)– Conflict Detection– Conflict Resolution– Cockpit Display of Traffic Information (CDTI)
• Cruise flight only– Direct routing– Optimal cruise altitude
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study ScenariosScenarios
• 8 crews, 18 runs per crew, 20 minutes per run
• current airline pilots• 2 days including half
a day of training
• Traffic Densities: Single, Double, Triple• Level of Automation: Manual, Execute Combined, Execute Separate • Non-Nominal: Other aircraft failures/events, Own aircraft failures/events, Delay time increased
• Traffic Densities: Single, Double, Triple• Level of Automation: Manual, Execute Combined, Execute Separate • Non-Nominal: Other aircraft failures/events, Own aircraft failures/events, Delay time increased
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study ConceptConcept
• Modified Voltage Potential
• Characteristics:– Fail safe– Co-operative– More options– Clear to pilot– Communication
not required
I n t r u d e r ’ sp r o t e c t e d
z o n e
H e a d i n gc h a n g e
S p e e dc h a n g e
A v o i d a n c ev e c t o r
M i n i m u md i s t a n c e
O w n s h i p
I n t r u d e r
Similar in vertical planeSimilar in vertical plane
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study Flight Crew InterfaceFlight Crew Interface
• Navigation Display– Traffic Symbology– Conflict Detection– Resolution Advisories– Vertical Navigation Display– Extra EFIS Control Panel
functionality• Modifications to Autopilot
– Execute Combined– Execute Separate
• Aural alerts
NASA
NLR Human-In-The-Loop StudyNLR Human-In-The-Loop StudySubjective Results: Subjective Results: AcceptabilityAcceptability
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• Distribution of responses as a function of the three densities, across all sessions, across all subject pilots
• Acceptability: 91.5% (single), 83.0% (double), 78.7% (triple)
• Distribution of responses as a function of the three densities, across all sessions, across all subject pilots
• Acceptability: 91.5% (single), 83.0% (double), 78.7% (triple)
NASA
NLR Human-In-The-Loop StudyNLR Human-In-The-Loop StudySubjective Results: Subjective Results: SafetySafety
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ATCmuchsafer
ATCsafer
Same FFsafer
FFmuchsafer
Su
bje
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e ra
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gs
in p
erce
nta
ges
single
double
triple
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ATCmuchsafer
ATCsafer
Same FFsafer
FFmuchsafer
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single
double
triple
•Distribution of responses as a function of the three densities, across all sessions, across all subject pilots
• Safety: 88.3% (single), 75.5% (double), 71.3% (triple)
•Distribution of responses as a function of the three densities, across all sessions, across all subject pilots
• Safety: 88.3% (single), 75.5% (double), 71.3% (triple)
NASA
NLR Human-In-The-Loop StudyNLR Human-In-The-Loop StudySubjective Results: Subjective Results: Workload
Subject workload
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single double triple
density
nominal
non-nominal
• Rating Scale of Mental Effort (RSME)
• Rating less than 40 (“costing some effort”) over all densities
• Results similar to cruise phase results in current ATC scenarios
• Rating Scale of Mental Effort (RSME)
• Rating less than 40 (“costing some effort”) over all densities
• Results similar to cruise phase results in current ATC scenarios
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study Objective Results: Objective Results: EPOGEPOG
– Primary Flight Display: 8.1 %– Lateral Navigation Display: 48.9 %– Vertical Navigation Display: 7.6 %
•Eye-Point-Of-Gaze measurements
•Pilot Flying and Pilot-Not-Flying
•Percentages of the total fixation duration, averaged over the Pilot Flying and Pilot-Non-Flying, across all sessions:
•Eye-Point-Of-Gaze measurements
•Pilot Flying and Pilot-Not-Flying
•Percentages of the total fixation duration, averaged over the Pilot Flying and Pilot-Non-Flying, across all sessions:
NASA
NLR Human-In-The-Loop Study NLR Human-In-The-Loop Study Objective Results: Objective Results: ManeuversManeuvers
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heading speed altitude
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manual
execute combined
executeseparately
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heading speed altitude
Pe
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execute combined
executeseparately
•Distribution of maneuvers as a function of the three different modes, across all sessions, across all subject pilots
• Maneuvers: Heading: 71.0 % Speed: 40.3 % Altitude: 48.7 %
•Distribution of maneuvers as a function of the three different modes, across all sessions, across all subject pilots
• Maneuvers: Heading: 71.0 % Speed: 40.3 % Altitude: 48.7 %
NASA
NASA Air-Ground Integration Study NASA Air-Ground Integration Study MethodsMethods
• Boeing 747-400 simulator and Airspace Operations Lab• Flight deck and controller perspectives• 8 DIA enroute scenarios (20 minutes in duration)• 10 flight crews/10 controllers• New display features on flight deck• Airborne alert logic (no ground conflict probe)• Controller tools similar to those at DIA• Controller “monitoring” more than “controlling”• Run in March/April 1998
NASA
Background/Research GoalBackground/Research Goal• Background
– RTCA Free Flight document recommends aircraft self-separation in particular situations (e.g., enroute environment)
– Requires new conceptual airspace that includes human performance parameters
– Aircraft self-separation will require a shift in roles and responsibilities between the users on the ground and in the air
• Research Goal– To conduct early simulations examining flight deck human
performance parameters
NASA
NASA Air-Ground Integration Study NASA Air-Ground Integration Study ScenariosScenarios
• Traffic on flight deck (ADS-B range 120 nms)• Traffic on controller’s radar display (DIA Sector 9)• Representation of high v. low density/clutter
– High = 16-17 aircraft, low = 6-8 aircraft• “Blocker” aircraft preventing most common resolution• Conflict event types: high and low density
– Obtuse angle– Acute angle– Right angle– Almost intruder
NASA
NASA Air-Ground Integration StudyNASA Air-Ground Integration StudyDisplaysDisplays
• Flight deck display– No early alert indication (prior to alert zone
transgression)– Alert zone transgression display features– Temporal predictors and call signs selectable
• Controller Display– Similar features as those currently in DIA (e.g., vector
lines, J rings)– Some features from CTAS, but no enhanced functions
NASA
NASA Air-Ground Integration Study NASA Air-Ground Integration Study Flight Crew ResultsFlight Crew Results
• Density and detection time– Flight crews took longer to detect conflicts in high
density compared to low density scenarios• Conflict Angles and detection time
– No differences in detection times between the conflict angles
• Ratings of conflict detection and time pressure– Significant increase in reported workload and time
pressure as a function of traffic density• No differences for almost intruder for detection times
NASA
NASA Air-Ground Integration StudyNASA Air-Ground Integration StudyPilot Detection TimesPilot Detection Times
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Density
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High
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NASA
NASA Air-Ground Integration StudyNASA Air-Ground Integration StudyController ResultsController Results
• Effects of traffic density and conflict angle on detection times
– Interaction between density and angle• Longer detection time in obtuse angle high density v. obtuse
angle low density• Shorter detection time in acute angle high density v. right angle
and obtuse angle high density
• Ratings of workload and task complexity– Significant increase in ratings of workload and
complexity as a function of density– No differences for almost intruder detection times
NASA
NASA Air-Ground Integration StudyNASA Air-Ground Integration StudyController Detection TimesController Detection Times
131.4
224.1
123.1
155
103
81.2
Low High0
50
100
150
200
250
Se
con
ds
Density
Obtuse
Right
Acute
131.4
224.1
123.1
155
103
81.2
Low High0
50
100
150
200
250
Se
con
ds
Density
Obtuse
Right
Acute
NASA
General SummaryGeneral Summary
• Consistent Findings across Studies– Impact for increasing density
• density may be exacerbated by other factors• existence of abnormal situations (e.g. weather) may limit self-
separation
– Losses of minimum separation• flight crews try to minimize separation between aircraft while
maintaining legal separation• controllers wanted larger separation than the flight crews
maintained (NASA study)
NASA
General SummaryGeneral Summary
• Unique Findings– Pilots fixate on CDTI 60% of the time and PFD 10% of
the time (NLR study)• Pilots reported spending too much time on the CDTI (NASA
study)
– Performance parameter usage• Heading was most common parameter used (NLR study)
– similar to previous NASA studies• Altitude was most common parameter used (NASA study)
– inclusion of the “blocker” aircraft in most common lateral escape path
NASA
General SummaryGeneral Summary
• Unique Findings (NASA)– Conflict angles affect controllers and flight crews
• controller conflict detect times• flight crew timing and type of maneuver
– Density and conflict angle may interact– Frequent air-to-air communication
NASA
Future Research IssuesFuture Research Issues
• Addition of abnormal situations for workload realism (e.g., weather, winds, SUA, passenger problems)
• Assessment of data link for communications to help frequency congestion
• Simulation including representation of additional carriers and dispatch
• Information requirements assessment for shared situation awareness