Copyright: Shell Brands International AG 2008

Projects & Technology

8/29/2013

Decisions to overtake are affected by implicit learning of the performance of the car

Stephen Skippon, Nick Reed & Ryan Robbins

6th International Conference on Driver Behaviour & Training, Helsinki, August 2013

Decisions to overtake

Decisions on whether or not to overtake depend on the

resolution of a conflict between two types of goals:

•Progress: e.g. “Get to destination as fast as

possible”

•Safety: e.g. “Avoid harm to myself”

The first motivates overtaking behaviour, but the

second motivates the driver not to overtake

Interactive activation and competition model of decisions to overtake

•Alternative behaviours are mentally

represented

•Their representations receive excitatory and

inhibitory signals from the goals, and inhibit

each other

•Net activation of each behaviour is a weighted

sum of these inputs

•Behaviour with the highest net activation

executes

GOAL M: Get to

destination as fast as

possible

GOAL N: Avoid harm

BEHAVIOUR X:

Overtake

BEHAVIOUR Y:

Follow

Excitatory signal Inhibitory signal

Excitatory and inhibitory signals from competing goals depend on the net activation levels of those goals

comparison signal

in behavioural

action feedback

loop

Net Activation

level of goal

Net excitation and

inhibition from

other goals

These inputs are

multiplied together

Excitation and

Inhibition

signals from

other goals

These inputs

are summed

together

Activation decay: activation is gradually

lost in the absence of new inputs

Inter-goal dynamics processes

(e.g. Kruglanski et al. (002)

Goal-pursuit

processes (e.g.

Carver &

Scheier, 1998)

Self regulatory control: the behavioural action feedback loop for a goal

R: Mental

representation of

intended state of

the world

Behaviour

P: Perception of

state of the world

Comparison

E = R - P

State of the

world External

events

Internal

working

models

Sensory inputs

Overtaking decisions require access to an internal

working model of the performance of the car

Magnitude of comparison signal E depends on how far

the goal is from fulfilment (i.e. R – P)

Internal working model of vehicle performance

Humans use internal working models to

predict the behaviour of entities in our

perceptual worlds:

• e.g. internal working models of other

people

To assess whether a particular road situation

represents an opportunity to overtake safely,

the driver must combine:

• information about the scene situation

(speeds of vehicles, distance available,

etc.)

• knowledge of the way his/her own

vehicle will respond to his/her control

actions

This knowledge is contained in an internal

working model of the response of the vehicle

to control actions

Internal working model dynamics

Drivers may use different vehicles, and

the performance of the same vehicle

may vary depending on load, engine

temperature, altitude, weather, and fuel

So to maintain its utility the internal

working model is likely to be able to

adapt in the light of new information

•Explicit learning: information

acquired directly via an external

source (e.g. observation that car is

heavily loaded)

AND/OR

•Implicit learning (Frensch &

Runger, 2003): Information

acquired indirectly, through

experience of the vehicle’s

response to control actions

Hypotheses:

Decisions to overtake will be made

more frequently and/or in more

challenging circumstances if the

driver has been exposed to:

•Explicit information that the

performance of the vehicle is

higher

•Implicit learning that that the

performance of the vehicle is

higher

TRL DigiCar Driving simulator

DigiCar simulator at TRL: Honda Civic

simulator vehicle; Renault vehicle

dynamics model; OKTAL SCANeR II

Vehicle dynamics updated at 100Hz;

visuals refreshed at 60Hz; data recorded at

20 Hz

Three projection screens, 210o forward field

of view; one rear screen, 60o rear field of

view

Visual scene resolution1280 × 1024 pixels

per screen

Motion system with three degrees of

freedom (pitch; roll and heave)

Participants walked up to and entered the

car as they would a real vehicle,

encouraging expectations of a close-to-

normal driving experience

Study design

2 x 2 x 2 within-participants design with 48 participants

Each participant completed 4 experimental drives in TRL

DigiCar simulator

3 independent variables, each with 2 levels:

Explicit prior knowledge – participants briefed that

simulated vehicle has:

•Average performance

•Higher performance

Implicit learning condition: simulator vehicle performance:

•Normal

•Higher (8% higher acceleration)

Overtaking difficulty: two experimental overtaking

opportunities per drive (of different levels of difficulty)

•String of 4 vehicles following each other closely

•String of 7 vehicles following each other closely

Independent variable: Frequency of positive decisions to

overtake (move into overtaking lane AND accelerate)

Practice

overtaking Experimental

overtaking

Familiarisation

driving

Experimental route: Rural single

carriageway with short village

sections, and sections of dual

carriageway for overtaking

Overtaking 4 vehicles

Results: Frequency of overtaking vs. explicit learning and implicit learning – overtaking 4 vehicles

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Briefed low, actual low

Briefed low, actual high

Briefed high, actual low

Briefed high, actual high

Freq

uenc

y of

pos

itive

dec

isio

ns to

ove

rtak

e

Available acceleration

Results: Frequency of overtaking vs. explicit learning and implicit learning – overtaking 7 vehicles

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Briefed low, actual low

Briefed low, actual high

Briefed high, actual low

Briefed high, actual high

Freq

uenc

y of

pos

itive

dec

isio

ns to

ove

rtak

e

Available acceleration

Results summary

Significant main effects of:

•explicit learning

• implicit learning

No significant interactions

Supports hypotheses that:

Decisions to overtake will be made more frequently if the driver has been

exposed to explicit information that the performance of the vehicle is

higher

AND

Decisions to overtake will be made more frequently if the driver has been

exposed to implicit learning that that the performance of the vehicle is

higher

Discussion: role of learning processes in overtaking decisions

Both explicit information and implicit learning can independently

change the internal working model of vehicle performance

Situational perception when faced with a decision whether of not

to overtake

Relative activation levels of competing goals

Relative strengths of excitatory and inhibitory signals to mental

representations of alternative behaviours

Decision: enacted behaviour

which affects

which affects

which affects

which affects

Conclusions

•Drivers’ decisions on whether to overtake in a given situation draw on an internal working

model of the performance of their vehicle

•The internal working model is not constant , but adaptable

•The internal working model can be influenced by information provided explicitly to the

driver. Real-world examples might include: knowledge that the engine has recently been

tuned/serviced; knowledge that the vehicle is lightly/heavily loaded; marketing

communications

•The internal working model is also influenced through implicit learning of the vehicle’s

actual response to control actions in recent driving

•These processes are independent

Acknowledgments

Thank you for your attention!

The authors would like to acknowledge the contribution of Lena

Weaver of TRL for running the driving simulator experiment, and

thank our participants for helping us carry out the study

References

Carver, C.S. & Scheier, M.F. (1998). On the Self-Regulation of Behavior.

Cambridge, England: Cambridge University Press.

Frensch, P. A. & Runger, D. (2003). Implicit learning. Current Directions in

Psychological Science 12 (1), 13-18.

Kruglanski, A.W., Shah, J.Y., Fishbach, A., Friedman, R., Chun, W.Y. & Sleeth-

Keppler, W. (2002). A theory of goal systems. In M.P. Zanna (Ed.) Advances in

Experimental Social Psychology, Vol. 34, pp. 331-378. San Diego, CA:

Academic Press.