a methodology for inducing the out of the loop phenomenon in highly automated driving
TRANSCRIPT
AMethodologyforInducingtheOutoftheLoopPhenomenoninHighlyAutomatedDriving
5 August 2016 ICTTP 2016, Brisbane, Australia1
TyronLouw@tyronlouw
researchgate.net/profile/Tyron_Louw
InstituteForTransportStudiesUniversityOfLeeds,UK
Acknowledgements
5 August 2016 ICTTP 2016, Brisbane, Australia2
• CO-AUTHORS• Natasha Merat
• AND COLLEAGUES AT ITS LEEDS:Oliver CarstenRuth MadiganGustav MarkkulaAnthony HorrobinMichael Daly
Introduction
“VehicleAutomationtakesdrivers“OUTOFTHELOOP””
”Whenanoperatorisremovedfromacontrolloop…operatorawarenessofsystemstatesmaybereduced”(Endsley&Kiris,1995)
ButwhatisthisOut-of-the-Loopphenomenon andwhichlooparetheytalkingabout?
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Whatis‘outoftheloop’?
Definition:“thedriverisnot immediatelyaware ofthevehicle andtheroadtrafficsituation…becausetheyarenot activelymonitoring,
makingdecisions orprovidinginputtothedrivingtask”(Kienle etal.,2009)
Implication:“Beingout-of-loopleadstoadiminishedabilitytodetectsystem
errorsandmanuallyrespondtothem”(Endsley &Kiris,1995)
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Conceptof“out-of-theloop”
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Schematic representation of the out-of-the-loop phenomenon (Louw et al., 2015)
“…because they are not actively monitoring, making decisions or
providing input to the driving task”
StayingINorOUToftheloop?
SomesayIN:
“out-of-the-loopperformancecouldbeimproved,forexamplebypreventingthedriverfromdriftingfurtheroutoftheloop”(Strandetal.,2014)
SomesayOUT:‘‘Thedrivercannotbereliedupontoactasamonitorifmoment-to-momentvehiclecontrolistakenaway”(JacobyandSchuster,1997).
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Someinterestingproblems
TheOOTLphenomenonraisessomeinterestingquestions:
1.How do we study it? “Difficult to catch a driver being OOTL”
2.How do we measure it? No drivers = No measures?
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Researchquestions
1. Howdowestudyit?Canweusescreenmanipulationstoinducetheoutofthe
loopstate?
2. Howdowemeasureit?Canweuseeyegazedispersiontomeasuretheoutofthe
loopstate?(Wangetal.,2014)
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UniversityofLeedsDrivingSimulator
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Jaguar S-type cab in a 4m spherical projection dome
300° field-of-view projection system
v4.5 Seeing Machines faceLAB eye-tracker recorded eye movements at 60Hz
Participants
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GenderMean age (SD)
Mean Approximate Annual Mileage (SD)
Years holding a full UK driving license (SD)Male Female
No Fog (N=15) 8 6 36.43 (9.88) 8592 (12457.83) 15.43 (10.10)
Light fog (N=15) 10 5 38.47 (13.51) 9966 (6767.75) 20 (13.76)
Heavy Fog (N=15) 10 5 39.2 (15.38) 7800 (4139.53) 19.73 (16.79)
Heavy Fog + Task (N=15) 4 11 29.47 (9.96) 5333 (3653.11) 9.67 (7.13)
Outoftheloopmanipulations
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NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ
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ManipulationAims
NoFog: ControlConditionLightFog: Simulateaprocesswherebylimitedvisual
attentionwasdirectedtowardsthescreen.HeavyFog: Simulatesituationswherethedriverislooking
completelyawayfromtheroadandisunawareofthetrafficconditions.
HeavyFog+Quiz:Assesstheeffectofavisualtaskwithoutaphysicaldistraction.
DriveDesign
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NoFog HeavyFogLightFog HeavyFog+Task
Leadvehicle
a AutomationOn
b ScreenManipulationsOn
c DroneMovesIntoLane
d ScreenManipulationsOff/UncertaintyAlert
e LeadVehicleAction
Non-critical Critical
1 2 3 4 5 6
≈150s
a b d ec
Egovehicle
NoFog+NBack
100s 3s 3s
AutomationvsManual
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AUTOMATED DRIVING = 8.35° MANUAL DRIVING = 6.92°
SD of Horizontal Gaze (p<.01)
Status Speedometer Status Speedometer
DuringtheOOTLmanipulations
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NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ
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Status Speedometer
NO FOG LIGHT FOG
HEAVY FOG + QUIZHEAVY FOG
Status Speedometer
Status Speedometer Status Speedometer
NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ
NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ
AftertheOOTLManipulations
Inthethreesecondperiodafterthemanipulationsended,therewerenodifferencesbetweentheOOTLmanipulationgroupsforHorizontalorVerticalGazeDispersion
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Summary
Observabledifferencebetweenautomationandmanual
OOTLmanipulationsaffecteddrivers’eyegazedispersion
Differencesresolvedwithin3secondsofmanipulationsstopping
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Nextsteps
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Wheredodriverslookfirstwhenmanipulationsend?
WhathappenswhenthedurationsOOTLincrease?
Isthepatterndifferentforthosewhocrash?
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For more information look out for:
Are you in the loop? Using Gaze Dispersion to Understand Driver Visual Attention During Resumption of Control from Automation
Tyron Louw & Natasha Merat
Institute for Transport Studies, University of Leeds, UK