Active and Passive Fatigue
in Simulated Driving
Dyani J. Saxby
Study Synopsis
► Development
of more automated vehicle
systems might remove some of the task
load that can lead to fatigue ?
► Active fatigue may stem from active physical
control of vehicle system, while passive
fatigue may stem from the driver taking on
a supervisory role (Desmond & Hancock,
2001).
Our Current Understanding of
Fatigue
► Fatigue
- fatigue itself is multifaceted and difficult
to define precisely (Desmond & Hancock, 2001) .
► Causes
of Fatigue
- It is often difficult to separate task-induced
fatigue from sleep loss and circadian rhythm
effects, which often interact in real-world driving
scenarios.
- The manifestation of symptoms is dependent
upon a number of factors, including individual
differences in personality and coping, as well as
the task itself (Hitchcock & Matthews, 2005) .
► wide
range of symptoms
- ‘mental’ symptoms (e.g. boredom, loss of
alertness )
- ‘physical’ symptoms (e.g. muscle pain,
eyestrain)
- changes in brain function
Measures of Fatigue
Types of Measures
 It is important to distinguish between mental
and physical symptoms and the effects on
performance that may result.
 It is critical to obtain both subjective state and
objective performance measures when
researching driver fatigue (Craig & Cooper,
1992).
Types of Measures
► Performance
Measures
 Used to assess driver fatigue on a driving
simulator : curvature error, heading error,
steering wheel rate, lane deviation, reaction
time, traffic violations, and divided attention
tasks
Performance Measures
► Limitations
 variety of individual differences : more experienced
driver’s increased capacity to cope with fatigue and
compensate accordingly
 performance indices may be affected by factors other
than fatigue : distraction and stress
► Subjective
measures of fatigue that are
independent of performance measures are
useful to gain a full understanding of driver
fatigue, and to establish that the fatigue
state is linked to loss of performance and
safety.
Traits and Subjective State
► Traits
are stable aspects of personality that
predispose individuals toward reacting to
various situations in a certain manner.
 research suggests that personality measures
that are specific to driving are better predictors
of performance because individuals may
actually have a personality that is unique to the
driving experience.
Traits and Subjective State
►
States are considered to be temporary
reactions to a situation (Matthews, et al.,
2000).
 State measures have a more direct impact on
performance. Unfortunately, state models are
less advanced.
Traits and Subjective State
► Fatigue
may overlap considerably with other
states such as stress.
► Different
task demands or situational factors
can impact subjective fatigue states in
unique ways.
Traits and Subjective State
► Matthews’
(2002) transactional model of
driver stress and fatigue suggests that
individual personality factors interact with
situational demands, which in turn, elicit
cognitive processes that can reduce or
exacerbate the effects that driver stress
vulnerability has on subjective states.
Fatigue Models for Performance
► Unidimensional
Fatigue Models
 De Vries, Michielsen, and Van Heck (2003).
►They
administered six questionnaires by mail to 351
individuals who worked at least 20 hours per week.
► All six questionnaires were found to have a onefactor solution as revealed by exploratory factor
analyses. Furthermore, analysis of the pooled
questions showed that a one-factor solution, which
accounted for 44 percent of the total variance, was
most appropriate.
Fatigue Models for Performance
► Multidimensional
Models - Many
researchers have rejected the notion that
fatigue is aunidimensional construct and
have instead found evidence supporting a
multidimensional approach.
Fatigue Models for Performance
► Multidimensional
Models
 Smetz, Garssen, Bonke, and Haes (1995) :
tested the psychometric properties of the
Multidimensional Fatigue Inventory (MFI) on a
broad sample of individuals
Fatigue Models for Performance
► Multidimensional
Models
 American Cancer Society Behavioral Research
Center :
►Confirmatory
factor analyses revealed five factors:
General Fatigue, Emotional Fatigue, Physical Fatigue,
Mental Fatigue, and Vigor. Lavidor
 Weller and Babkoff (2002) :
►found
evidence that fatigue is multidimensional in
nature in non-patient populations as well.
► the highest correlations between Mental and
Physical fatigue and between Severity and Tiredness.
Fatigue Models for Performance
► Unidimensional
Fatigue Models v.s.
Multidimensional Models
 To summarize, there is currently no “gold
standard” for the assessment of fatigue.
 It is possible that some of the disagreement
amongst researchers regarding both approaches
may be due to psychometric issues.
Fatigue Models for Performance
► Psychometric
Issue
 One reason for lack of consensus amongst
researchers is that the appropriateness of
unidimensional versus multidimensional
measures might be context dependent.
Fatigue Models for Performance
► Hitchcock
and Matthews (2005)
 have identified several psychometric issues,
which may contribute to the research
discrepancies concerning the dimensionality of
fatigue.
the range of constructs sampled on various fatigue
instruments is highly variable
►hierarchical models might be useful, but little
attention has been allocated to them in fatigue
assessment. For example : WAIS – III
►
Fatigue Models for Performance
► Dundee
Stress State Questionnaire
 assess transient states associated with stress,
arousal, and fatigue, and to reflect the
multidimensionality of these states
Automation and Fatigue
►A
Remedy for Driver Fatigue?
 Automobile manufacturers often promote
increased automation as a way of increasing
safety (Funke, Matthews, Warm, & Emo, 2007).
► Reducing
ideal goal
workload may not always be an
 When operators are in underload or overload
conditions they may not be able or willing to
allocate efforts toward the task; as a result,
performance may be compromised.
Automation and Fatigue
► Recent
Advancements in Automated
Vehicle Systems
 Intelligent Parking Assist systems
►help
drivers parallel park
► “Car, Park Thyself,” the reporter warned, “Hands-free
driving doesn't mean you can read a book or doze off.
• Recent Advancements in Automated
Vehicle Systems
 Adaptive Cruise Control (ACC)
►It
has the ability to maintain a set speed. In addition
to maintaining a set speed, it also has sensors that
are able to detect vehicles in front of the driver and
adjust speed accordingly
►ACC does reduce mental workload, but only in
limited circumstances (Young & Stanton, 2004).
Active versus Passive Fatigue
► Passive
fatigue may result from the driver
taking on a supervisory role.
► Active
fatigue may correspond to overload
conditions, whereas passive fatigue may
correspond to underload conditions.
Active versus Passive Fatigue
Automation of vehicle systems will expose
drivers to a new supervisory role, which
reduces active control, producing passive
fatigue, associated with monotony and
boredom.
► It is important to test whether active and
passive fatigue states are genuinely
different, perhaps having different
implications for driver behavior and safety.
►
► Drive
Duration
► Fatigue
Manipulations on the Driving
Simulator
► Specific
Hypotheses