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Weretheyintheloopduringautomateddriving?Linksbetweenvisualattentionandcollisions
11 August 2016 ICTTP 2016, Brisbane, Australia1
TyronLouw@tyronlouw
researchgate.net/profile/Tyron_Louw
InstituteForTransportStudiesUniversityOfLeeds,UK
Acknowledgements
11 August 2016 ICTTP 2016, Brisbane, Australia2
• 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?
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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).
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PossibleOutcome2– TaskDisengagement
Driversmaychoosetoengageinnon-drivingrelatedactivities(Carstenetal.,2012).
Distractsfromthesupposedprimarytaskofmonitoringthevehicle.
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Consequenceofoutcomes
Passivefatigueandtaskdisengagement mayhamperdrivers’abilitytosafelyresumecontrolfromanautomateddrivingsystem(Neubauer etal.,2012;Merat etal.,2012)
Suddenchangestotheroadenvironmentcapturedrivers’attention,resultinginreducedvisualscanningofthesceneandincreasedfixationstowardschanges(Chapman& Underwood,1997;Velichkovsky etal.,2002) whichlinkdirectlytoanincreaseincrashes(Crundall, Shenton& Underwood,2004).
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Experimentalsetup
Outoftheloop(OOTL)manipulationtechniques
Alteringdrivers’visibilityoftheroadsceneduringautomationwhiletheycompletedvisualandnon-visualtasks
Aim:varyingdrivers’levelofawarenessandengagementinthedrivingtask(Louw etal.,2015)
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NO FOG + n-back
e)
ManipulationAims
NoFog: ControlConditionNoFog+n-back: Assesstheeffectofacognitivetask.LightFog: Simulateaprocesswherebylimitedvisual
attentionwasdirectedtowardsthescreen.HeavyFog: Simulatesituationswherethedriverislooking
completelyawayfromtheroadandisunawareofthetrafficconditions.
HeavyFog+Quiz:Assesstheeffectofavisualtaskwithoutaphysicaldistraction.
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Researchquestions
(i) HowdotheOOTLmanipulationsaffectthelocationofdrivers’firstfixationaftertheuncertaintyevent
(ii) Howaredrivers’fixationsdistributedovertime
(iii) Whatistherelationshipbetweenfixationsduringtheuncertaintyalertsandcrashfrequency.
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Participants
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• 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
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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
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
Not a takeover request, but a request to monitor and intervene if deemed necessary
AreasofInterestCentre:6° circularregionoftheroadcentreareaFixations:200msthresholdwasusedwithastandarddeviationofgazepositionbelow1o
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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?
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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
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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
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0%10%20%30%40%50%60%70%80%90%
100%
Event1 Event2 Event3 Event4 Event5 Event6
PercentR
oadCenter
Event1 Event2
**
***
Non-critical Critical
Effect of Event Number
* p < .01, ** p < .001
Fixationsduringthetransition
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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 Crash
ü
💥
No Crash
VS
6X2mixedANOVATimeWindow(1s-6saftermanipulationsended)aswithinfactorCrashOutcome(collision/nocollision)betweenfactor
CrashOutcomes
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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
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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
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Summary
Non-collidersidentifiedhazardearlyandhadconsistentscanningpattern
Colliderslatetoidentifyhazardandexperiencedperceptualtunnelling
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Conclusion(1)
Driversactivelyseekouthazardsandanticipatetheirresponse
Thisisdemandinginmanual,butmoresoinautomation.
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Conclusion(2)
Withincreasingautomation,drivers’decisionswillshiftfrombeingaboutwhen toacttowhether toact
Implication
Driversneedtopossessaconfidentunderstandingoftheirroleandthecapabilitiesoftheirhighlyautomateddrivingsystems
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Conclusion(3)
Thetypeofthehazardhasastronginfluenceonwhatisfixatedandhowquicklyitisprocessed
Implication
Highlyautomateddrivingsystemsshoulddiscriminatehazardsleadingtoautomationdisengagement,anddirecttodrivers’visualattentiontotheminsufficienttimetorespondappropriately
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