Presentation by Tyron Louw at http://icttp2016.com www.its.leeds.ac.uk/people/t.louw

1. A Methodology for Inducing the Out of the Loop
Phenomenon in Highly Automated Driving
11 August 2016 ICTTP 2016, Brisbane, Australia1
Tyron Louw
@tyronlouw
researchgate.net/profile/Tyron_Louw
Institute For Transport Studies
University Of Leeds, UK

2. Acknowledgements
11 August 2016 ICTTP 2016, Brisbane, Australia2
• CO-AUTHORS
• Natasha Merat
• AND COLLEAGUES AT ITS LEEDS:
Oliver Carsten
Ruth Madigan
Gustav Markkula
Anthony Horrobin
Michael Daly

3. Introduction
“Vehicle Automation takes drivers “OUT OF THE LOOP””
”When an operator is removed from a control loop…operator
awareness of system states may be reduced” (Endsley & Kiris, 1995)
But what is this Out-of-the-Loop phenomenon and which loop are
they talking about?
11 August 2016 ICTTP 2016, Brisbane, Australia3

4. What is ‘out of the loop’?
Definition:
“the driver is not immediately aware of the vehicle and the road
traffic situation… because they are not actively monitoring,
making decisions or providing input to the driving task” (Kienle et
al., 2009)
Implication:
“Being out-of-loop leads to a diminished ability to detect system
errors and manually respond to them” (Endsley & Kiris, 1995)
11 August 2016 ICTTP 2016, Brisbane, Australia4

5. Concept of “out-of-the loop”
11 August 2016 ICTTP 2016, Brisbane, Australia5
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”

6. Staying IN or OUT of the loop?
Some say IN:
“out-of-the-loop performance could be improved, for example by
preventing the driver from drifting further out of the loop” (Strand
et al., 2014)
Some say OUT:
‘‘The driver cannot be relied upon to act as a monitor if moment-
to-moment vehicle control is taken away” (Jacoby and Schuster, 1997).
11 August 2016 ICTTP 2016, Brisbane, Australia6

7. Some interesting problems
The OOTL phenomenon raises some interesting questions:
1.How do we study it? “Difficult to catch a driver
being OOTL”
2.How do we measure it? No drivers = No measures?
11 August 2016 ICTTP 2016, Brisbane, Australia7

8. Research questions
1. How do we study it?
Can we use screen manipulations to induce the out of the
loop state?
2. How do we measure it?
Can we use eye gaze dispersion to measure the out of the
loop state? (Wang et al., 2014)
11 August 2016 ICTTP 2016, Brisbane, Australia8

9. University of Leeds Driving Simulator
11 August 2016 ICTTP 2016, Brisbane, Australia9
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

10. Participants
11 August 2016 ICTTP 2016, Brisbane, Australia10
Gender
Mean 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)

11. Out of the loop manipulations
11 August 2016 ICTTP 2016, Brisbane, Australia11
NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ

12. 11 August 2016 ICTTP 2016, Brisbane, Australia12
Manipulation Aims
No Fog: Control Condition
Light Fog: Simulate a process whereby limited visual
attention was directed towards the screen.
Heavy Fog: Simulate situations where the driver is looking
completely away from the road and is unaware
of the traffic conditions.
Heavy Fog + Quiz: Assess the effect of a visual task without a
physical distraction.

13. Drive Design
11 August 2016 ICTTP 2016, Brisbane, Australia13
No Fog Heavy FogLight Fog Heavy Fog + Task
Lead
vehicle
a Automation On
b Screen Manipulations On
c Drone Moves Into Lane
d Screen Manipulations Off/Uncertainty Alert
e Lead Vehicle Action
Non-critical Critical
1 2 3 4 5 6
≈150s
a b d ec
Ego
vehicle
No Fog + NBack
100 s 3 s 3 s

14. Automation vs Manual
11 August 2016 ICTTP 2016, Brisbane, Australia14
AUTOMATED DRIVING = 8.35° MANUAL DRIVING = 6.92°
SD of Horizontal
Gaze (p<.01)
Status Speedometer Status Speedometer

15. During the OOTL manipulations
11 August 2016 ICTTP 2016, Brisbane, Australia15
NO FOG LIGHT FOG
HEAVY FOG HEAVY FOG + QUIZ

16. 11 August 2016 ICTTP 2016, Brisbane, Australia16
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

17. After the OOTL Manipulations
In the three second period after the manipulations ended,
there were no differences between the OOTL
manipulation groups for Horizontal or Vertical Gaze
Dispersion
11 August 2016 ICTTP 2016, Brisbane, Australia17

18. Summary
Observable difference between automation and manual
OOTL manipulations affected drivers’ eye gaze dispersion
Differences resolved within 3 seconds of manipulations
stopping
11 August 2016 ICTTP 2016, Brisbane, Australia18

19. Next steps
11 August 2016 ICTTP 2016, Brisbane, Australia19
Where do drivers look first when manipulations end?
What happens when the durations OOTL increase?
Is the pattern different for those who crash?

20. Thanks!
t.l.louw@leeds.ac.uk
11 August 2016 ICTTP 2016, Brisbane, Australia20
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