Were they in the loop during automated driving? Links between visual attention and collisions

Weretheyintheloopduringautomateddriving?Linksbetweenvisualattentionandcollisions

11 August 2016 ICTTP 2016, Brisbane, Australia1

TyronLouw@tyronlouw

researchgate.net/profile/Tyron_Louw

InstituteForTransportStudiesUniversityOfLeeds,UK

Acknowledgements


• CO-AUTHORS• Natasha Merat

Oliver CarstenRuth Madigan

• AND COLLEAGUES AT ITS LEEDS:Gustav MarkkulaAnthony HorrobinMichael Daly

Introduction

Thefirstgenerationofpartially-automatedvehicles(SAELevel2)isalreadyonourroads(SAE,2014).

Driversarestillrequiredtosupervisethesystemandresumecontrol.

Butwhatwilltheyactuallydo?


PossibleOutcome1– PassiveFatigue

Prolongedmonitoringtakesdriversoutoftheloop (Merat etal.,2014) inducingpassivefatigue (Desmond&Hancock,2001).

Reducesdrivers’attentionalcapacity(Desmond&Matthews,1997)

andabilitytodetect,evaluateandrespondtocriticalevents.

Increasingthelikelihoodofcrashes(Endsley,1995;Hollnagel & Woods,2005;deWinteretal.,2014).


PossibleOutcome2– TaskDisengagement

Driversmaychoosetoengageinnon-drivingrelatedactivities(Carstenetal.,2012).

Distractsfromthesupposedprimarytaskofmonitoringthevehicle.


Consequenceofoutcomes

Passivefatigueandtaskdisengagement mayhamperdrivers’abilitytosafelyresumecontrolfromanautomateddrivingsystem(Neubauer etal.,2012;Merat etal.,2012)

Suddenchangestotheroadenvironmentcapturedrivers’attention,resultinginreducedvisualscanningofthesceneandincreasedfixationstowardschanges(Chapman& Underwood,1997;Velichkovsky etal.,2002) whichlinkdirectlytoanincreaseincrashes(Crundall, Shenton& Underwood,2004).


Experimentalsetup

Outoftheloop(OOTL)manipulationtechniques

Alteringdrivers’visibilityoftheroadsceneduringautomationwhiletheycompletedvisualandnon-visualtasks

Aim:varyingdrivers’levelofawarenessandengagementinthedrivingtask(Louw etal.,2015)


NO FOG + n-back

e)

ManipulationAims

NoFog: ControlConditionNoFog+n-back: Assesstheeffectofacognitivetask.LightFog: Simulateaprocesswherebylimitedvisual

attentionwasdirectedtowardsthescreen.HeavyFog: Simulatesituationswherethedriverislooking

completelyawayfromtheroadandisunawareofthetrafficconditions.

HeavyFog+Quiz:Assesstheeffectofavisualtaskwithoutaphysicaldistraction.


Researchquestions

(i) HowdotheOOTLmanipulationsaffectthelocationofdrivers’firstfixationaftertheuncertaintyevent

(ii) Howaredrivers’fixationsdistributedovertime

(iii) Whatistherelationshipbetweenfixationsduringtheuncertaintyalertsandcrashfrequency.


Participants


• 75 drivers (41 male)

• 21-69 years (M=36.16, SD=12.38)

• Normal or corrected-to-normal vision

• Average annual mileage of 8290.46 (SD=6723.08)

• Years holding driving licence (M=16.22, SD=12.92)

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

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

Not a takeover request, but a request to monitor and intervene if deemed necessary

AreasofInterestCentre:6° circularregionoftheroadcentreareaFixations:200msthresholdwasusedwithastandarddeviationofgazepositionbelow1o


Visual attention regions (Carsten et al., 2012)Carsten O, Lai FCH, Barnard Y, Jamson AH, Merat N. Control task substitution in semi-automated driving: Does it matter what aspects are automated? Hum Factors 2012;54,747–761.

Wheredodriversfirstlookafterthemanipulationsend?


0

0.1

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1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NoFog LightFog HeavyFog HeavyFog+Quiz

NoFog+n-back

Inde

xofDisp

ersio

n

Percen

toffixations

Centre Top Left Bottom Right IndexofDispersion

Eachbar:Percentoftotalnumberoffixations(Firstfixationof15driversfor6events=90fixations)ineachAreaofInterestforonegroup.

Indexofdispersion(id):Calculationofhowevenlyfixationsaredistributedamongtheareasofinterest

LookingattheRoadCenter inthefirst3s

6X3X5mixedANOVA

EventNumber(1-6)andTime(1-3)aswithin

ScreenManipulation(5Conditions)between


No effect of Screen Manipulation but interaction of Time*Screen Manipulation = 1st Second differences

0%10%20%30%40%50%60%70%80%90%

100%

1s 2s 3s

PercentR

oadCenter

****

Effect of Time

** p < .001

LookingattheRoadCenter inthefirst3s


0%10%20%30%40%50%60%70%80%90%

100%

Event1 Event2 Event3 Event4 Event5 Event6

PercentR

oadCenter

Event1 Event2

**

***


Effect of Event Number

* p < .01, ** p < .001

Fixationsduringthetransition


NoFog HeavyFogLightFog HeavyFog+Task

Leadvehicle

a AutomationOn

b ScreenManipulationsOn

c DroneMovesIntoLane

d ScreenManipulationsOff/UncertaintyAlert

e LeadVehicleAction


1 2 3 4 5 6

≈150s

a b d ec

Egovehicle

NoFog+NBack

100s 3s 3s Crash

ü

💥

No Crash

VS

6X2mixedANOVATimeWindow(1s-6saftermanipulationsended)aswithinfactorCrashOutcome(collision/nocollision)betweenfactor

CrashOutcomes


Critical Event 1 Critical Event 2 No Crash Crash No Crash Crash

No Fog (N=15) 10 5 15 0 Light Fog (N=15) 14 1 14 1

Heavy Fog (N=15) 8 7 12 3 Heavy Fog + Quiz (N=15) 13 2 15 0 No Fog + n-back (N=15) 11 4 15 0

Total 56 19 71 4

Focus on Critical Event 1

FixationsbasedonCrashOutcomeinCriticalEvent1


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

120%

1 s before 1 s 2 s 3 s 4 s 5 s 6 s

Perc

ent

Road

Cen

ter

No Collision in Critical Event 1 Collision in Critical Event 1 No Collision in Critical Event 2

**

*

SCREEN MANIPULATIONS END BRAKE LIGHT ONSET

**

* p<.05, ** p<.005

ü No Crash (N=54)

3 s after brake light

3 s after manipulations

💥 Crash (N=19)

Effect of Time Window

No effect of Crash Outcome

Interaction of Crash Outcome*Time Window

No Difference 1s before

Summary

Whatdriversdidduringautomationinfluencedwheretheyfirstlooked,butdifferencesdisappearedwithin2s

Drivers’visualattentionallocationstrategiesadapted totheuncertaintyalert


Summary

Non-collidersidentifiedhazardearlyandhadconsistentscanningpattern

Colliderslatetoidentifyhazardandexperiencedperceptualtunnelling


Conclusion(1)

Driversactivelyseekouthazardsandanticipatetheirresponse

Thisisdemandinginmanual,butmoresoinautomation.


Conclusion(2)

Withincreasingautomation,drivers’decisionswillshiftfrombeingaboutwhen toacttowhether toact

Implication

Driversneedtopossessaconfidentunderstandingoftheirroleandthecapabilitiesoftheirhighlyautomateddrivingsystems


Conclusion(3)

Thetypeofthehazardhasastronginfluenceonwhatisfixatedandhowquicklyitisprocessed

Implication

Highlyautomateddrivingsystemsshoulddiscriminatehazardsleadingtoautomationdisengagement,anddirecttodrivers’visualattentiontotheminsufficienttimetorespondappropriately


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Were they in the loop during automated driving? Links between visual attention and collisions

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