A Methodology for Inducing the Out of the Loop Phenomenon in Highly Automated Driving

AMethodologyforInducingtheOutoftheLoopPhenomenoninHighlyAutomatedDriving

5 August 2016 ICTTP 2016, Brisbane, Australia1

TyronLouw@tyronlouw

researchgate.net/profile/Tyron_Louw

InstituteForTransportStudiesUniversityOfLeeds,UK

Acknowledgements


• 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?


Whatis‘outoftheloop’?

Definition:“thedriverisnot immediatelyaware ofthevehicle andtheroadtrafficsituation…becausetheyarenot activelymonitoring,

makingdecisions orprovidinginputtothedrivingtask”(Kienle etal.,2009)

Implication:“Beingout-of-loopleadstoadiminishedabilitytodetectsystem

errorsandmanuallyrespondtothem”(Endsley &Kiris,1995)


Conceptof“out-of-theloop”


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).


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?


Researchquestions

1. Howdowestudyit?Canweusescreenmanipulationstoinducetheoutofthe

loopstate?

2. Howdowemeasureit?Canweuseeyegazedispersiontomeasuretheoutofthe

loopstate?(Wangetal.,2014)


UniversityofLeedsDrivingSimulator


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


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


NO FOG LIGHT FOG

HEAVY FOG HEAVY FOG + QUIZ


ManipulationAims

NoFog: ControlConditionLightFog: Simulateaprocesswherebylimitedvisual

attentionwasdirectedtowardsthescreen.HeavyFog: Simulatesituationswherethedriverislooking

completelyawayfromtheroadandisunawareofthetrafficconditions.

HeavyFog+Quiz:Assesstheeffectofavisualtaskwithoutaphysicaldistraction.

DriveDesign


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


AUTOMATED DRIVING = 8.35° MANUAL DRIVING = 6.92°

SD of Horizontal Gaze (p<.01)

Status Speedometer Status Speedometer

DuringtheOOTLmanipulations


NO FOG LIGHT FOG



Status Speedometer

NO FOG LIGHT FOG

HEAVY FOG + QUIZHEAVY FOG

Status Speedometer

Status Speedometer Status Speedometer

NO FOG LIGHT FOG


NO FOG LIGHT FOG


AftertheOOTLManipulations

Inthethreesecondperiodafterthemanipulationsended,therewerenodifferencesbetweentheOOTLmanipulationgroupsforHorizontalorVerticalGazeDispersion


Summary

Observabledifferencebetweenautomationandmanual

OOTLmanipulationsaffecteddrivers’eyegazedispersion

Differencesresolvedwithin3secondsofmanipulationsstopping


Nextsteps


Wheredodriverslookfirstwhenmanipulationsend?

WhathappenswhenthedurationsOOTLincrease?

Isthepatterndifferentforthosewhocrash?

[email protected]


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

A Methodology for Inducing the Out of the Loop Phenomenon in Highly Automated Driving

Science