This talk highlights some of the gains that can be had from reaching across the cross-disciplinary gap between engineering on the one hand, and psychology and neuroscience on the other. To this end, the talk briefly outlines a driver modelling framework that has been previously proposed by the presenter, and then describes how this framework has engendered new research hypotheses and results of applied engineering relevance. The modelling framework draws on knowledge from contemporary neuroscience and psychology, and construes driver control behaviour rather differently from many existing models, especially if comparing to models that have been put to use in applied engineering contexts. The follow-on research and results that have built on predictions from the framework include an improved understanding of driver response in crashes and near-crashes, improved estimates of safety benefits of collision avoidance systems, new perspectives on the effects of driver impairment and distraction, and objective methods for fidelity assessment of driving simulators. Cross-fertilisation can also happen in the other direction, with psychological accounts of the driver being informed by concepts from vehicle engineering. www.its.leeds.ac.uk/people/g.markkula www.its.leeds.ac.uk/about/events/seminar-series/

1. Institute for Transport Studies
Faculty of Environment
What do psychology and neuroscience have to
do with vehicle engineering? – The driver model
case for cross-disciplinarity
Dr. Gustav Markkula
Human Factors & Safety Group
ITS Research Seminar
2016-11-17

2. Vehicle engineering?

3. The driver

4. Driver models
”The virtual crash test
dummy with a brain”

5. Conflicting descriptions?
Routine driving Near-crash driving
Closed-loop Open-loop
Short delays Long, random delays
Well-adjusted control Under- and overreactions
(Van Auken et
al., 2011)
(MacAdam et
al., 2003)

6. Stealing ideas
Motor primitives
(Flash and
Henis, 1991)
(Cook and Maunsell, 2002)Evidence accumulation
Perceptual heuristics
(Land and Horwood, 1995;
Wann and Wilkie, 2004;
Salvucci and Gray, 2004)

7. Routine driving Near-crash driving
Closed-loop Open-loop
Short delays Long, random delays
Well-adjusted control Under- and overreactions
Conflicting descriptions?
(Markkula,2014)

8. Steering during skidding
𝛿 𝑡 + 𝑇R = 𝑘f 𝜃f 𝑡 ≈ −𝑘 𝑓 𝜓(𝑡)
(Markkula, 2013; Markkula,
Benderius, Wahde, & Wolff, 2014)

9. Understanding near-crash
brake response
(Markkula, 2014)
0 0.5
crash
5
-3 -2 -1 0
-1
-0.5
0
0.5
SHRP 2
near-crash
R2
= 0.18
0 5
0
0.5
1
-6 -4 -2
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.60
0 5
0
0.5
1
-1 -0.5
-1
-0.5
0
0.5
ANNEXT
crash
R2
= 1.00
0 5
0
0.5
1
-3 -2 -1 0
-1
-0.5
0
0.5
SHRP 2
near-crash
R2
= 0.75
0 5
0
0.5
1
-2 -1
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.98
0 5
0
0.5
1
-4 -2 0
-1
-0.5
0
0.5
SHRP 2
near-crash
R2
= 0.87
0 5
0
0.5
1
-4 -2
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.97
0 5
0
0.5
1
0
P 2
ash
5
-6 -4 -2
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.60
0 5
0
0.5
1
-1 -0.5
-1
-0.5
0
0.5
ANNEXT
crash
R2
= 1.00
0 5
0
0.5
1
-3 -2 -1 0
-1
-0.5
0
0.5
SHRP 2
near-crash
R2
= 0.75
0 5
0
0.5
1
-2 -1
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.98
0 5
0
0.5
1
-4 -2 0
-1
-0.5
0
0.5
SHRP 2
near-crash
R2
= 0.87
0 5
0
0.5
1
-4 -2
-1
-0.5
0
0.5
SHRP 2
crash
R2
= 0.97
0 5
0
0.5
1
-4 -2
-1
-0.5
0
0.5 Truck crash
R2
= 0.92
0 5
0
0.5
1
Predictions for (near-/)crashes:
• Piecewise near-linear deceleration
• Above threshold, brake response
time decreases with urgency
• Deceleration ramp-up slow, but
increasing with urgency
(Markkula, Engström, Lodin,
Bärgman & Victor, 2016)
Time (s)
Acceleration(g)
Time (s)

10. Explaining variability in near-
crash response time
(poster by Markkula, Lodin, Wells,
Theander & Sandin, 2016)

11. (a number of slides with unpublished
work removed for web sharing)

12. Related application: Transitions
from automated to manual driving
(Merat, Jamson, Lai, Daly & Carsten, 2014)
(Zeeb, Buchner & Schrauf, 2015)

13. (a number of slides with unpublished
work removed for web sharing)

14. Summary / reflections
• Reaching from an applied question to ideas and
concepts from more basic sciences
• Key here: Constrained applied problem
provided focus
• Resulting cross-disciplinary connection proved
a rich source of new ideas and applications
• Mechanisms grounded in more basic sciences
 applied models more likely to generalise

15. Pros and cons of cross-disciplinarity
• Potential for low-hanging fruit
• Personal development, fun
• More difficult to communicate
findings?

16. Institute for Transport Studies
Faculty of Environment
Thanks!