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This article aims to contextualize the conceptual definition of mental workload, conducting a review of its relation with driving task demands. In addition, it presents an overview of measurement tools used for evaluating driver mental workload.
Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
Available online at
1877-0428 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of PANAM 2014.
doi: 10.1016/j.sbspro.2014.12.212
XVIII Congreso Panamericano de Ingeniería de Tránsito, Transporte y Logística (PANAM 2014)
Mental Workload, Task Demand and Driving Performance:
What Relation?
Fátima Pereira da Silvaa
aPolytechnic Institute of Coimbra, Rua Dom João III, Solum, 3030 -329 Coimbra, Portugal
This article aims to contextualize the conceptual definition of mental workload, conducting a review of its relation with driving
task demands. In addition, it presents an overview of measurement tools used for evaluating driver mental workload.
© 2014 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of PANAM 2014.
Keywords: Mental workload, driving task, task demand, performance.
1. Workload and Mental Workload
Concerns related to the relationship between driving task demands, performance and human capacity appeared
more than thirty years ago (Pereira da Silva, 2003). Focus was placed mainly in aviation industry, where the need to
evaluate performance of pilots and air traffic controllers was increasingly important. Concomitantly, the concept of
workload and mental workload was emerging as an important aspect to be considered not only in the evaluation of
professional drivers, but also in the domain of scientific research.
Theoretical development of the concept was designed after an OTAN conference, with a following publication
of the book Mental Workload (Moray, 1979). After the publication of conference conclusions, several theoretical
studies emerged, approaching implications of mental workload evaluation in several organizational contexts
(Parasuraman & Hancock, 2001). The need for a proper understanding of the interaction between human-machine
systems, the advantages and limitations underlying this interaction and also the economic interest resulting from
* Corresponding author. Tel.: +351-965-006-455.
E-mail address:
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of PANAM 2014.
Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
greater profitability of this process were associated within theoretical research and practical implications of the
mental workload concept (Pauzié & Manzano, 2007; Piechulla et al., 2003; Wu & Liu, 2009). In many
organizational contexts, and air and land transport industry, mental workload began to be associated with studies on
safety and effectiveness of operator performance. In this context, matters related to the limit and amount of mental
effort, and tasks allowing to maintain an adequate level of performance began to guide research, with an emphasis
on the relationship between task demand and operator capabilities (and features) (De Waard, 1996; Verwey, 2000).
In a simplistic way, workload concept can be defined and perceived as a demand placed to the man (De Waard,
1996). However, according to De Waard (1996), this is a very simplistic view, in that it puts more emphasis on
external demands. Accordingly, workload is not reflected only on a particular task, but also, and inherently, reports
to a specific person (Rouse et al., 1993). Hoedemaeker (2002) argues that workload is defined by the amount of
resources required by a set of concurrent tasks, as well as by the use of resources needed to perform them. In an
attempt to categorize workload, the author distinguishes three types: (a) visual workload (e.g., for many types of
sources have the driver to look?); (b) motor workload (e.g., what should driver do with their hands and feets?); (c)
mental workload (e.g., how many types of information has the driver to process?).
Specifically, mental workload involves various processes, where neurophysiologic, perceptual and cognitive
processes are included (Baldwin & Coyne, 2003), and can be defined as the proportion of information processing
capability used to perform a task (Brookhuis & De Waard, 1993, 2000; De Waard, 1996; Kahneman 1973). As
stated by Brookhuis et al. (2009), mental workload reflects not only task specificities, but also performer features. In
other words, individual capabilities and characteristics (e.g., age, driving experience), motivation to perform the
task, strategies applied on task performance, as well as physical and emotional state affect the workload experienced
(e.g., Verwey, 2000). In turn, evaluation of mental workload translates the ratio of mental capacity applied in task
performance (O’Donnell & Eggemeier, 1986; De Waard & Brookhuis, 1997), attending to information processing
cost, also mentioned as mental effort (Mulder, 1986). Mental effort is similar to what is commonly indicated as
‘make the best’ or ‘try hard’ to achieve a particular level or goal (Broohkuis & De Waard, 2010). Mulder (1986)
distinguishes two types of mental effort, specifically the mental effort applied on information processing in a
controlled mode (i.e., computational effort) and the mental effort needed to apply when operators energy state is
affected (i.e., compensatory effort). While controlled mode is used to maintain an adequate level of task
performance, for example, considering variations in task complexity or introduction of secondary tasks;
compensatory mode is applied when performance decreases until a certain level, for instance, of fatigue. This
compensatory effort is only possible until a certain point, because when there are extreme levels of effort, task
difficulty and mental workload increase. As an alternative approach to the application of high levels of effort, which
are defined by the interaction between operator and task, operator may decide to apply adaptive strategies (e.g.,
Merat et al., 2005).
Considering suitability degree, mental workload can vary between low (i.e., underload) and very high levels (i.e.,
overload) (Brookhuis & De Waard, 2010; De Waard & Brookhuis, 1997). These two extremes of the optimal level
(i.e., level at which operator feels comfortable, can manage task demands and maintains a good performance) are
classified as inappropriate and can lead to imperfect or inaccurate perceptions, as well as to low levels of attention
and capacity, and to insufficient time for a proper information processing (Brookhuis & De Waard, 2010; De Waard
& Brookhuis, 1997; Lenné et al., 1997; Leung & Starmer, 2005; Ng Boyle et al., 2008; Nilsson et al., 1997;
Rakauskas et al., 2008; Thiffault & Bergeron, 2003; Verwey & Zaidel, 1999). High levels of mental workload occur
when task demands exceed performer capacity (Loft et al., 2007).
2. Mental Workload, demand and performance in driving task
According to Kantowitz & Simsek (2001), much of the research is consistent to assume that accident risks are
strongly associated with driver mental workload, attending to the impact that it has on driving task performance and
road safety. As stated by Kantowitz (2000), the most common types of collisions involve, as a rule, lack or loss of
Driving a vehicle in the twenty-first century is, increasingly, a complex task that involves extreme fluctuations in
mental workload (Baldwin & Coyne, 2003; Verwey, 2000). Considering the increasing difficulty of driving task and
the multitude of information that drivers have to process and manage inside and outside the vehicle, there are
312 Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
multiple potentially triggering factors of driver mental workload (Baldwin & Coyne, 2003; Silva 2003). The
increase of traffic intensity and the introduction of new information technologies inside the vehicle pose new
demands and increased levels of complexity to driving task (Engström et al., 2005; Jahn et al., 2005; Makishita &
Matsunaga, 2008; Pauzié & Manzano, 2007; Piechulla et al., 2003). Stress, fatigue and motivation for risk-taking
behaviours are some examples of human factors that limit an effective performance of drivers, which is associated to
the excess of information inside and outside the vehicle and the consequent increase of distribution level in visual
and auditory resources (Makishita & Matsunaga, 2008). In all these situations, driver mental workload is affected.
The permanent adaptive control approach is an important basis for understanding the relationship between
mental workload, driving performance and task demand (as perceived by driver) (e.g., De Waard, 2002). In this
context, there are moments on driving performance where simultaneous increase in number of tasks can increase
their demand, resulting in an increase of mental effort invested, and also in a decrease of performance efficiency.
There are other situations in which a decrease of driver ability implies an increase in mental workload. Decrease of
driver ability can be triggered by various factors such as: monotonous traffic environment; driver experience type; or
presence of circumstantial contexts (e.g., fatigue or sickness). In other situations, driving performance is stable,
although there is an increase of mental effort invested. Thus, mental effort invested by driver may reflect how to
maintain its performance in an appropriate or acceptable level.
In the words of Parasuraman & Hancock (2001), “human performance is thus the result of task demand effects
and mediators factors related to its adaptive strategies” (p. 307). According to the authors, it is easily expected that
there is an association or linear relation between task demand and mental workload, that is, when there is a high task
demand, there is also a high level of mental workload. However, as stated above, it is not always so, because drivers
develop a set of strategies that enable them to manage mental workload and regulate their performance. Thus, it is
not possible to establish an association or a linear relation between task demand and mental workload, as this
association is always dependent on strategies and internal factors of each operator. Also according to Parasuraman
& Hancock (2001), there is a large number of situations where it is evident some insensitivity, and this means that
even when there is an increase or decrease on task demand, it is possible to verify no changes in driver workload.
There is another process, also described by Wickens & Yeh (1988) and Hancock (1996), defined by Parasuraman &
Hancock (2001) as dissociation, which represents the moment when task demand increases and driver workload
decreases. Considering the perspective of an integrated system in which task demand, adaptive strategies, workload
and performance are closely related or interdependent, a change in each of these factors consequently affects all
3. Evaluation of Driver Mental Workload
A large part of research related with workload evaluation was and still is connected to the field of aviation
industry (e.g., Hancock & Verwey, 1997; Loft et al., 2007). In those researches, pilots have to perform a complex
task in extreme conditions, being the selection of pilots so strict that usually only young and without health
limitations people are chosen (De Waard, 1996). The same does not happen when we are focused on evaluation of
automobile driving task, where selection criteria concerning future drivers is much less restricted and, as a result,
driving population is much more diverse in terms of skills, abilities and characteristics. We can even accept that the
majority of drivers may not be prepared to drive modern cars and motorbikes at high-speed because of their
Since each person uses different strategies, these can be applied more or less effectively and with more or less
effort to achieve the same performance level (De Waard, 1996). If workload is defined taking into account driver
and driving task, we can not only evaluate task demand, because individual differences and operator state also have
an impact on mental workload (De Waard, 1996, 2002). Thus, assessment of mental workload is related to task
difficulty such as it is experienced by the person, since many reactions are possible due to demands of the same task
(Pauzié & Pachiaudi, 1996). According to Brookhuis & De Waard (2010), it can be pointed out three main reasons
for the currently and in the near future importance of assessing mental workload, mainly due to changes in driving
conditions over the last decades. Firstly, nature of driving task has undergone changes that have been extended from
physical effort to cognitive effort, from calm interaction with other road users to traffic situations with high levels of
Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
complexity. Secondly, accidents have been numerous, with causes that are mainly attributed to victims themselves.
Finally, human errors in road traffic related to mental workload (in the form of inaccurate perceptions, insufficient
attention and inefficient information processing) are among the main causes of most road accidents (e.g., Smiley &
Brookhius, 1987). The range of factors mentioned previously defines not only the specificity of workload analysis
under road traffic, as well as their relevance.
3.1. Critical moment for evaluation
Considering the inherent relationship between mental workload, task demand and performance, Meister
proposed, in 1976, an evaluation model that incorporates those valences. This model proposes the existence of three
regions: region A, region B and region C. In region A, there is a weak task demand, a low workload and a high level
of performance. If in this region happens an increase on task demand, performance efficiency is not compromised.
In region B, operator’s performance level decreases due to the increase of task demand, and may result on an
increase in workload.
In region C, there is a drastic decrease in performance as a result of an increase of task demand level and high
levels of workload. Here, performance can be maintained at minimum levels of efficiency even when there is an
increase of demand level. According to the author, region B represents the moment for determining the sensitivity of
measures for evaluation of performance and mental workload. Specifically, measures of performance evaluation
tend to remain stable in regions A and C, as they do not undergo changes that can be caused by task demand level.
Moreover, evaluation measures of mental workload may show an overworkload in region C, while region B can also
show some sensitivity. Region A is not presented as a sensitive region to assess workload experienced.
De Waard (1996) sought to complement the model of Meister (1976) and proposes the introduction of a region D
(called desactivation) that is located before region A and reflects the effects and implications of monotonous tasks
performance. As they possess a low demand level, monotonous tasks may precipitate an increase of task difficulty
by reducing capacity, which happens in monotony and boredom. These states require investment of a greater ability
to perform the same task, with a consequent increase in mental workload.
Although the described model can help us to understand the extremes of the process where region D features a
low performance and a high workload, and in the opposite region (C) remains the same situation, there is not a clear
specification of what is a high level of mental workload. This question brings us to questions of where is the
boundary between a high versus moderate versus low workload, and what is the critical moment termed as red line
(De Waard, 1996; Kantowitz, 2000). The notion of red line is associated with resources limits available by operator,
reflecting the time when performance decreases sharply (De Waard 1996; Kantowitz 2000; Reid & Colle, 1988).
Given that, in recent years, the answer to the question “(...) how must workload is too much” (De Waard, 1996,
p. 98) has received particular attention in the context of road traffic research, the concept of red line for mental
workload has become useful, allowing to establish consequences of high levels of driver mental workload. For
example, Reid & Colle (1988) report that the area par excellence where performance decreases (i.e., placement of
red line) is located in the transition from region A to region B.
De Waard (1996), discriminating the exact moment that the decreased performance happens, proposes a partition
of region A into several sub-regions. Thus, in the middle part of region A (called A2), operator can still easily
handle with task demand and performance remains at a stable level even when there is an increase in task demand
(i.e. there is not an increasing of effort); in sub-region A3, operator fails to maintain performance level without
increasing effort, but evaluation measures do not show a decline of performance. In this sub-region can happen a
temporary compensation for the use of occasional and not critical effort, which only reflects some operator
adaptability and flexibility. However, if workload peaks in this sub-region start happening too often or if effort lasts
to ensure performance, it emerges stress states, some disability, or scarcity situation, which need to be addressed. At
this time, there may arise a critical moment, where operator can lose control of the situation (i.e., red line). If effort
increases and difficulty between demand and performance occur, it seems plausible to assume that the critical
moment for mental workload happens in the transition from sub-region A2 to A3. In turn, the transition from region
D to region A1 is associated with monotony experienced by operator when he undertakes a major effort for not
diminishing performance level. Thus, it is through a greater effort that operator does not change his performance.
In sum, we can classify various regions as follows: region D (desactivation), operator state is affected, showing a
314 Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
high workload and a low performance; region A2, performance level is considered as optimal and operator can
easily handle with task demands; regions A1 and A3, performance does not change, but operator must use effort to
maintain performance; in region B, there is a performance decline; and in region C, performance remains at a
minimum level and operator has an experience of overworkload.
3.2. Measurement tools
In a literature review on methodologies for evaluating workload, Eggemeier & O’Donnell (1986) distinguish two
selection criteria for measurement tools, including sensitivity (if technique or tool can discriminate between
different levels of workload) and diagnosticity (if technique or tool can distinguish different types of workload). De
Waard (1996) also adds as criterion the primary task interference, that is, the extent to which the introduction of a
new task can damage performance of an essential or priority task.
There are three main types of measures for assessing driver mental workload, specifically: (1) subjective self-
report measures; (2) performance measures; and (3) physiological measures (Baldwin and Coyne, 2003; Brookhuis
& De Waard, 2002; Cantin et al., 2009; O’Donnell & Eggemeier, 1986).
3.2.1. Subjective self-report measures
Subjective self-report measures consist in the application submitted to the driver, attending to perceived level of
task demand, and being widely used in research (Silva, 2003). As reported by De Waard (1996), “(...) no one else is
more prepared to provide an accurate judgment on workload experienced than oneself” (p. 31). De Waard (2002)
points out some advantages in its use: (a) in addition to the reduced application cost, they have a high validity; (b)
they do not interfere with primary task performance when they are applied immediately after completing the task.
Regarding their disadvantages, Eggemeier & O’Donnell (1986) reported that sometimes it becomes difficult to
discriminate adequately between physical workload and mental workload, and the person may be unable to
distinguish between what are external task demands and mental effort experienced, and to quantify effective and
properly the mental effort invested (De Waard, 2002). Brookhuis & De Waard (2000) also highlight the
disadvantage of disability experienced by some people for detecting internal changes.
Considering the typology of subjective self-report measures, these can be unidimensional or multidimensional,
being respectively related to the evaluation of one or more dimensions inherent to mental workload. Regarding
unidimensional measures, we can highlight MCH-Modified Cooper Harper Scale (Cooper & Harper, 1969) that is
made up of ten items, and RSME Rating Scale Mental Effort (Zijlstra, 1993; Zijlstra & Van Dorm, 1985) that
evaluates only the proportion of mental effort invested in a task, which is marked by a cross in a continuous vertical
line. Minimum and maximum of the scale are from 0-150 mm and all ranges of 10 mm are indicated. Along vertical
line there are some landmarks identified with a verbal descriptor of effort (Hoedemaeker, 2002), ranging from “no
effort” to “extreme effort”. It should be noted that RSME is increasingly used to assess mental workload on traffic
sector. Additionally, Vetman & Verwey (1996), by using nine measures for assessing mental workload in very short
periods of time, found that RSME is one of the most sensitive measures of mental workload. According
Hoedemaeker (2002), although presenting some sensitivity to changes in task difficulty, MCH is not as sensitive as
NASA-TLX (multidimensional scale), or as RSME.
Regarding multidimensional measures, we can point out NASA-Task Load Index (NASA-TLX) (Hart and
Stavelan, 1988) and Subjective Workload SWAT-Assessment Technique (Reid & Nygren, 1988). NASA-TLX is
one of the most widely used multidimensional measures and evaluates six factors: mental demands, physical
demands, temporal demands, performance, and effort and frustration levels. Its usefulness is mainly related to the
workload evaluation after long periods of time, not showing, however, sensitive in detection of mental workload
peaks or small increases (Hoedemaeker, 2002). As a limitation of NASA-TLX, Brookhuis & De Waard (2000) point
out the lack of clarity in the attribution of mental and physical effort. SWAT considers the evaluation of three
dimensions: time stress, mental effort, and psychological stress. According to Hoedemaeker (2002), although its
evidence of validity and sensitivity, SWAT is not so sensitive and valid as NASA-TLX.
It is important to state that unidimensional scales have been more appropriate when assessing mental workload
aims to determine a single overall measure (Silva, 2003). Additionally, according to De Waard (1996), if
unidimensional scales are used separately in each dimension of the task, they also can provide multidimensional
Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
3.2.2. Performance measures
Performance measures are based on techniques of direct registration of driver ability to perform the driving task
at a level considered acceptable and safe, and properly maintain the vehicle on the road without colliding with other
participants of the road. Overall, we can consider two main groups of measures: (1) direct measurements, that is,
they are only focused on performance of the main or primary driving task; (2) indirect measurements, which
associate a secondary task to the primary driving task.
Performance measures focused on the main or primary driving task include the basic levels of driving task (i.e.,
levels of control and maneuver) (Michon, 1985; Theeuwes, 2002), where control of lateral and longitudinal position
and running, and speed are considered (De Waard, 1996, 2002; Törnros & Östlund, 2002).
Attending to control of lateral position, this consistes into maintaining the vehicle on the road, within its limits,
and it is a psychomotor task involving eye-hand coordination. This performance can be studied through variations
occurred in lateral position and also through time that occurs before crossing a road. It can be assessed using
perceptive measures, answered by driver or observers, as well as through objective measures. The latter are more
feasible in national and rural roads, insofar as urban roads have several intersections and narrow streets that make it
difficult to obtain an accurate measure of lateral position (Törnros & Östlund, 2002). According to De Waard
(2002), lateral position deviation becomes one of the most important indicators of deficiencies in driving, and it can
be translated by the possibility of the driver leave road centreline and get involved in an accident. The number of
lateral position deviations increases if driver energy state is not appropriate after, for example, alcohol consumption,
use of drugs or states of fatigue (De Waard, 2002). Also mobile phone use may precipitate a decrease in control of
lateral position (De Waard, 1996). Lateral position desviations are presented as a measure of performance very
sensitive (Brookhuis et al., 1985; Hicks & Wierwille, 1979; O’Hanlon, 1984; O’Hanlon et al., 1982).
Considering longitudinal control, this also allows keeping the vehicle safely, and its evaluation is usually
performed taking into account distance or time of distance. Execution of this kind of manoeuvre requires perception
and attention, and reaction time is one indicator considered for assessing driver performance.
Speed control aims to maintain security, prevents vehicle collisions with other vehicles that are in the same
direction, keeps a steady flow of traffic and allows always the driver to keeps control even when faced with normal
traffic events. This control can be studied by speed variation and speed adopted when compared to what is defined
as speed limit, as also as by the time required to correct the speed before colliding with another vehicle (Törnros &
Östlund, 2002). The speed increase under influence of distracters elements has been used as an indicator to
determine driver inability to control speed. Since increased accidents correlates with increased speed, speed factor
may be an indicator of decreased performance (Carvalhais, 2002). Speed is always a performance measure that is
dependent on driver, although this may be constrained by high density of traffic and other road users. Always
depending on these factors, speed increase or reduction can also translate an adaptive behaviour or a compensatory
reaction due to distraction or increased mental workload. Thus, the study of reaction time has been considered a
good indicator (Brookhuis et al., 1994).
It is also important to note measures related to time-to-line crossing, where performance lies at manoeuvre level
(Michon, 1985). Time-to-line crossing is generally defined by the time required for overcoming any boundary line
of traffic queue with any wheel, if speed of shuttlecock rotation angle remains constant. With mental workload
increase, it is expected a decrease in the efficiency of such behaviour.
Finally, in the context of evaluation measures focused on the primary driving task, we also have those that are
answered by experienced observers (Törnros & Östlund, 2002). These measures have the advantage of being used
by observers who have greater sensitivity to numerous risk conditions experienced by drivers. However, these
observers can become more flexible in their judgments, which can be pointed as a disadvantage.
The evaluation method that resorts to observers, accompanying drivers, is a method of assessing driving
performance initially developed by Risser (1985) to study inexperienced drivers. In this group, Observation Protocol
of LUND is an example. This measure is a shortened version of “Wiener Fahrprobe”, and it is focused on assessing
tactical level of driving (Michon, 1985) and requires the existence of two observers already trained on its evaluation.
One observer assesses variables related to driving performance considered as standard (driver condescending
316 Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
behaviour and choice of speed), while the other performs the registration of non-standard behaviours, such as the
occurrence of risk situations or conflict with other road users. If there is only one observer, the protocol part
considered as non-standard can be excluded. This evaluation method is difficult to apply on simulator or on
laboratory, since it implies the existence of an observer inside the vehicle. If video recordings are made, sensitivity
and reliability of the method can become smaller (Östlund & Törnros, 2002).
Considering measures that include the introduction of a secondary task, they seek to evaluate saturation point of
the limited capacity of mental workload (Cantin et al., 2009), which reflects an operation of single channel for
performing mental tasks and seeks to estimate the required proportion of this channel for a successful performance
of primary task (Kahneman, 1973; O’Donnell and Cohen, 1993). To assess mental workload required by primary
task, performance levels may be compared, that is, comparison between performance when secondary task (or load
task) is performed alone and performance achieved when it is performed simultaneously with primary task (Cantin
et al., 2009). The purpose of introducing a secondary task is not to disturb the main task (i.e., driving task), but
rather to quantify mental workload imposed by various levels of complexity in driving, through changes in
performance of secondary task (Cantin et al., 2009). Often, secondary tasks lead to the need to resort to memory,
mental calculation, attention, etc. (Pereira da Silva, 2003). An example of this can be related to peripheral detection
task, where it is required a manual (or verbal) response to a stimulus presented in the visual (or auditory) field (e.g.,
Jahn et al., 2005; Makishita & Matsunaga, 2008; Patten et al., 2004; Recarte & Nunes, 2003; Verwey, 2000).
It is important to state that the introduction of a secondary task becomes useful and relevant for researches that
aim to study the relationship between intelligent systems introduced in vehicles and mental workload (Verwey &
Veltman, 1996). Although those technological systems bring advantages, they may lead to greater distraction and
driver mental overworkload, and may be considered as risk factors for accident occurrences (Ma & Kaber, 2005;
Pauzié & Manzano, 2007; Verwey & Veltman, 1996).
3.2.3. Physiological measures
Physiological measures evaluate the answers given by peripheral and central nervous system during driving
performance, and may involve, in particular: (a) heartbeat measuring; (b) electroencephalogram; (c) electro-
myogram; (d) evaluation of eye movement and pupil dilation; (e) blood pressure evaluation; (f) assessment of breath
levels; (g) assessment of electro-dermal activity; and (h) determination of hormone levels. Their main advantage lies
in the fact that they do not involve a clear and objective response from the driver (De Waard, 2002). According
Mehler et al. (2009), given the results obtained in their study, physiological measures are sensitive to changes in
mental workload levels before registering clear decrements in driving performance. On the other hand, their
application requires the use of a complex methodology, difficult to implement in real traffic situations, which is
appointed as a disadvantage (Pauzié & Manzano, 2007).
In summary, it is noteworthy that in the study developed by Verwey & Veltman (1996), which evaluated the
sensitivity and diagnosticity of a set of measures, the authors concluded that subjective measures (notably RSME-
Rating Scale Mental Effort) are presented as the most sensitive to mental workload.
Considering the combined use of several types of measures, De Waard (2002) states that “the most important
factor in determining the mental workload is change” (p. 171), and for coping with change in a dynamic
environment with permanent changes it is justified the use of different types of measures in the evaluation of driver
mental workload.
4. Conclusion
This article aimed to contribute for sustaining the relevance of mental workload concept under the road traffic
literature, given theoretical and evaluation issues that have been addressed in this field. Thus, it is relevant to
emphasize two main conclusions. Firstly, it persists the need to clarify mental workload concept, since it still exists
an absence of a satisfactory and consensual definition for it (Parasuraman & Hancock, 2001). In this context, it is
necessary a better understanding on the relationship between mental workload and other processes, including those
that are cognitive in nature (e.g., attention, effort). Further clarification of mental workload role within a wider
relational and interdependent puzzle can be considered a major factor for effectiveness and efficiency of its
Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
assessment and its implications on driver behaviour. If virtually all forms of man-machine interface imply
interaction, mental workload assessment becomes a relational form and an important tool for understanding the
demands placed on human performance (Pereira da Silva, 2003). Thus, it should be noted that mental workload
could become an indicator of the relational effectiveness between new technologies demands and human capacity.
The second conclusion is related to measures for assessing mental workload. Literature review allowed to verify
that measures that involve an introduction of a secondary task, as well as physiological measures are among the
most used in recent research. Concomitantly, also because of difficulties associated to application of those measures,
driving simulators can be identified as the most widely used methodological environment in researches. As referred
by Brookhuis & De Waard (2010), the study of certain variables on an ecological context (i.e., real road situation),
such as the case of mental workload, is difficult and often considered ethically unacceptable (or even impossible).
Thus, according to the authors, simulators allow the creation of real conditions and situations without any objective
risk, functioning as instrumentally important research laboratories.
Current demands of driving task are no longer only physical and became, par excellence, cognitive (Brookhuis &
De Waard, 2010; Parasuraman & Hancock, 2001). Thus, it is necessary to understand the kind of specificity and
demand that is currently placed on driver, on the limits of his performance capability and on the amount of mental
workload that he can process without diminishing performance. Underlying current demands, we also can find the
dominant and growing up introduction of automation and complex technological systems in vehicles. The main
reason for electronics systems development corresponds to security support, insofar as human factor is appointed as
the main cause of accidents (e.g., Brookhuis et al., 2009). These systems seek to reduce human error in information
processing required by complex traffic conditions (Hancock & Verwey, 1997). However, they can contribute, on the
one hand, for mental underworkload and, secondly, to its overworkload. Thus, some researchers put forward the
hypothesis that deal with mental underworkload can become a problem as complex and difficult as dealing with
mental overworkload (e.g., Young & Stanton, 2007). For example, Engström et al. (2005) and Brookhuis et al.
(2009) state that those systems induce secondary tasks that are concurrent to primary task of driving. This point has
rose, according to the authors, increased concerns about potential negative effects particularly related to excessive
workload and distraction, especially in potentially dangerous situations. That is, they may inherently contribute to
increased levels of mental workload, to the extent that they add information on those situations (Hancock &
Verwey, 1997; Jahn et al., 2005; Pauzié & Manzano, 2007; Verwey, 2000).
It is therefore important to clarify and obtain a detailed understanding of the relationship between different
technological systems introduced in vehicles, their demands, and their effect on levels of driver mental
overworkload and driver performance (Engström et al., 2005; Jahn et al., 2005; Ma & Kaber, 2005). Hence, it is
important to take into account all specific characteristics of different groups of operators. Definition and delimitation
of a comprehensive path are, thus, presented as particularly interesting and relevant for designing and
implementating new technological systems that have continuously been created and should be adjusted to
expectations, needs, requirements and capacities of drivers (Pauzié & Manzano, 2007; Piechulla et al., 2003; Liu
and Wu, 2009). That is, creation and application of man-centered systems. Thus, it emerges the need to design
optimized vehicles where information systems aim to help driver, delegating their intervention to strategic decisions
(Pauzié & Manzano, 2007). For effective technology, it is important to attend to the way driver concurrently
manages driving task. For a continuous level of optimization achievement, mental workload should be continuously
asserted as a relevant area of research interest, where theoretical and empirical knowledge needs further
Baldwin, C. L. & Coyne, J. T. (2003). Mental workload as a function of traffic density: comparison of physiological, behavioral, and subjective
indices. Proceedings of the Second International Driving Symposium on Human Factors, 19-24.
Brookhuis, K. A. & De Waard, D. (2000). Assessment of driver´s workload: performance and subjective and physiological indexes. In P.A.
Hancock & P. A. Desmond (Eds.), Stress, Workload, and Fatigue (pp. 321-333). London: Lawrence Erlbaum Associates, Inc.
Brookhuis, K. A. & De Waard, D. (1993). The use of psychophysiology to assess driver status. Ergonomics 36(9), 1099-1110.
Brookhuis, K. A., Louwerens, J.W. & O’ Hanlon, J. F. (1985). The effect of several antidepressants on EEG and performance in a prolonged car
driving task. In W.P. Koella, E. Ruther and H. Schulz (Eds.), Sleep’ 84 (pp. 129-131). Stuttgart : Gustav Fisher Verlag.
Brookhuis, K. A., De Waard, D. & Mulder, L. J. M. (1994). Measuring driving performance by car-following in traffic. Ergonomics 37(3), 427-
318 Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
Brookhuis, K. A. & De Waard, D. (2002). On the assessment of (mental) workload and other subjective qualifications. Ergonomics 45(14), 1026-
Brookhuis, K. A. & De Waard, D. (2010). Monitoring drivers’ mental workload in driving simulators using physiological measures. Accident
Analysis & Prevention 42(3), 898-903.
Brookhuis, K. A., Van Driel, C. J., Hof, T., Van Arem, B. & Hoedemaeker, M. (2009). Driving with a congestion assistant, mental workload and
acceptance. Applied Ergonomics 40(6), 1019-1025.
Cantin, V., Lavallière, M., Simoneau, M. & Teasdale, N. (2009). Mental workload when driving in a simulator: effects of age and driving
complexity. Accident Analysis & Prevention 41(4), 763-771.
Carvalhais, J. D. (2002). WP2 - Pilot Dependent Measures-Internal Deliverable. Human Machine Interface and the Safety of Traffic in Europe
(HASTE) Project (Report no. GRD1/2000/25361). Institute for Transport Studies, University of Leeds, Leeds, UK.
Cooper, G. E. & HARPER, R. P. (1969). The use of pilot rating in the evaluation of aircraft handling qualities . Moffett Field, C. A. National
Aeronautics and Space Administration, Ames Research Centre, NASA Report Tn-D-5153.
De Waard, D. (1996). The Measurement of Driver’s Mental Workload (PhD thesis). Traffic Research Centre, University of Groningen, Haren,
De Waard, D. (2002). Mental workload. In R. Fuller and J. A. Santos (Eds.) Human Factors for Highway Engineers. (pp. 161-175). Netherlands:
Pergamon Press.
De Waard, D. & Brookhuis, K. A. (1997). On the measurement of driver mental workload. In J.A. Rothengatter and E. Carbonell Vaya (Eds.)
Traffic and Transport Psychology (pp. 161-171). Amsterdam, N. L. : Pergamon.
Engström, J., Johansson, E. & Östlund, J. (2005). Effects of visual and cognitive load in real and simulated motorway driving. Transportation
Research Part F: Traffic Psychology and Behaviour 8(2), 97-120.
Hancock, P. A. (1996). Effect of control order, augmented feedback, input device, and pratice on tracking performance and perceived workl oad.
Ergonomics 39(9), 1146-1162.
Hancock, P. A. & Verwey, W. B. (1997). Fatigue, workload and adaptive driver systems. Accident Analysis & Prevention 29(4), 495-506.
Hart, S. G. & Staveland, L. E. (1988). Development of the NASA-TLX (Task Load Index): results of empirical and theoretical research. In P.A.
Hancock & N. Meshkati (Eds.) Human Mental Workload (pp. 139-183). North Holland, Amsterdam: N. L..
Hicks, T. G. & Wierwille, W. W. (1979). Comparison of five mental workload assessment procedures in a moving-base driving simulator.
Human Factors: The Journal of the Human Factors and Ergonomics Society, 21 (2), 129-143.
Hoedemaeker, M. (2002). Summary Description of Workload Indicators: WP1 Workload Measures. Human Machine Interface and the Safety of
Traffic in Europe Growth Project. GRD1-2000-25361. HASTE. Institute for Transport Studies. Leeds, UK: University of Leeds.
Jahn, G., Oehme, A., Krems, J. F. & Gelau, C. (2005). Peripheral detection as a workload measure in driving: effects of traffic complexity and
route guidance system use in a driving study. Transportation Research Part F: Traffic Psychology and Behaviour, 8(3), 255-275.
Kahneman, D. (1973). Attention and Effort. New Jersey: Prentice Hall.
Kantowitz, B. H. (2000). Attention and mental workload. Proceedings of the Human Factors and Ergonomics Society (pp.456-460). Annual
Meeting. Human Factors and Ergonomics Society, Santa Monica, C.A.
Kantowitz, B. H. & Simsek, O. (2001). Secondary-task measures of driver workload. In P. A. Hancock & P. A. Desmond (Eds.), Stress,
Workload, and Fatigue (pp. 395-407). London: Lawrence Erlbaum Associates, Inc.
Lenné, M., Triggs, T. J. & Redman, J. R. (1997). Time of day variations in driving performance. Accident Analysis & Prevention 29(4), 431-437.
Leung, S. & Starmer, G. (2005). Gap acceptance and risk-taking by young and mature drivers, both sober and alcohol-intoxicated, in a simulated
driving task. Accident Analysis & Prevention 37(6), 1056-1065.
Loft, S., Sanderson, P., Neal, A. & Mooi J, M. (2007). Modeling and predicting mental workload in en route air traffic control: critical review and
broader implications. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49 (3), 376-399.
Ma, R. & Kaber, D. B. (2005). Situation awareness and workload in driving while using adaptive cruise control and a cell phone. International
Journal of Industrial Ergonomics, 35 (10), 939-953.
Makishita, H. & Matsunaga, K. (2008). Differences of drivers’ reaction times according to age and mental workload. Accident Analysis &
Prevention, 40 (2), 567-575.
Mehler, B., Reimer, B., Coughlin, J. F. & Dusek, J. A. (2009). Impact of incremental increases in cognitive workload on physiological arousal
and performance in young adult drivers. Transportation Research Record: Journal of the Transportation Research Board 2138(1), pp. 6-12.
Meister, D. (1976). Behavioral Foundations of Systems Development. New York, NY: Wiley.
Merat, N., Antilla, V. & Luoma, J. (2005). Comparing the driving performance of average and older drivers: the effect of surrogate in-vehicle
information system. Transportation Research Part F: Traffic Psychology and Behaviour, 8 (2), 147-166.
Michon, J. A. (1985). A critical view of driver behaviour models: what do we know, what should we do? In L. Evans & R. C. Schwing (Eds.),
Human Behavior & Traffic Safety (pp. 485-524). New York, N. Y.: Plenum Publishing Company.
Moray, N. (1979). Mental Workload. New York, N.Y.: Plenum.
Mulder, G. (1986). The concept and measurement of mental effort. In G. R. J. Hockey, A. W. K. Gaillard & M. G. H. Coles (Eds.), Energetics
and Human Information Processing (pp. 175-198). Dordrecht, N. L.: Martinus Nijhoff Publishers.
Ng Boyle, L., Tippin, J., Paul, A. & Rizzo, M. (2008). Driver performance in the moments surrounding a microsleep. Transportation Research
Part F: Traffic Psychology and Behaviour 11 (2), 126-136.
Nilsson, T., Nelson, T.M. & Carlson, D. (1997). Development of fatigue symptoms during simulated driving. Accident Analysis & Prevention 29
(4), 479-488.
O’Donnell, B. F. & Cohen, R. A. (1993). Attention: a component of information processing. In R. A. Cohen (Eds.), The Neuropsychology of
Attention (pp. 11-48). New York, N. Y.: Plenum Press.
O’Donnell, R. D. & Eggemeier, F. T. (1986). Workload assessment methodology. In K. R. Boff, L. Kaufman & J. P. Thomas (Eds.), Handbook
of Perception and Human Performance - Cognitive Processes and Performance (pp. 1-49). New York, N. Y.: Wiley.
O’ Hanlon, J. F. (1984). Driving performance under the influence of drugs: rationale for, and applications of, a new test. British Journal of
Clinical Pharmacology 18 (S1), 121S-129S.
Fátima Pereira da Silva / Procedia - Social and Behavioral Sciences 162 ( 2014 ) 310 – 319
O’Hanlon, J. F. O., Haak, T. W., Blaauw, G. J. & Riemersma, J. B. J. (1982). Diazepam impairs lateral position control in highway driving.
Science, 217(4554), 79-80.
Östlund, J. & Tornros, J. (2002). WP1 - Driving Performance Measures. Human Machine Interface and the Safety of Traffic in Europe (HASTE)
Project (Report no. GRD1/2000/25361). Institute for Transport Studies, University of Leeds, Leeds, UK.
Parasuraman, R. & Hancock, P. A. (2001). Adaptive control of mental workload. In: B.H. KANTOWITZ (Eds.), Stress, Workload, and Fatigue
(pp. 305-320). London, U. K.: Lawrence Erlbaum Associates, Inc.
Patten, C. J., Kircher, A., Östlund, J. & Nilsson, L. (2004). Using mobile telephones: cognitive workload and attention resource allocation.
Accident Analysis & Prevention 36(3), 341-350.
Pauzié, A. & Manzano, J. (2007). Evaluation of driver mental workload facing new in-vehicle information and communication
technology. Proceedings of the 20th enhanced safety of vehicles conference (ESV20), Lyon, FR.
Pauzié, A. & Pachiaudi, G. (1996). Subjective evaluation of the mental workload in driving context. In Traffic and Transport Psychology: Theory
and Application (pp. 173-181). Berksire, U. K.: University of Derby.
Pereira da Silva, M. F. (2003). Aprendizagem e Comportamentos na Condução Automóvel (Learning and Behaviours in Automobile Driving )
(Non-published master thesis). Faculty of Psychology and Sciences of Education, University of Coimbra, Coimbra, PT.
Piechulla, W., Mayser, C., Gehrke, H. & König, W. (2003). Reducing drivers’ mental workload by means of an adaptive manmachine interface.
Transportation Research Part F: Traffic Psychology and Behaviour 6(4), 233-248.
Rakauskas, M. E., Ward, N.J., Boer, E. R., Bernat, E. M., Cadwallader, M. & Patrick, C. J. (2008). Combined effects of alcohol and distraction
on driving performance. Accident Analysis & Prevention 40 (5), 1742-1749.
Recarte, M. A. & Nunes, L. M. (2003). Mental workload while driving: effects on visual search, discrimination, and decision making. Journal of
Experimental Psychology: Applied 9(2), 119-137.
Reid, G. B. & Colle, H. A. (1988). Critical SWAT values for predicting operator overload. In Proceedings of the Human Factors Society 32nd
Annual Meeting. Human Factors Society. Santa Monica, C. A.
Reid, G. B. & Nygren, T. E. (1988). The subjective workload assessment technique: a scaling procedure for measuring mental workload. In P. A.
Hancock & N. Meshkati (Eds.), Human Mental Workload (pp.185-218). Amsterdam, N. L.: North Holland.
Risser, R. (1985). Behaviour in traffic conflict situations. Accident Analysis & Prevention 17 (2), 179-197.
Rouse, W. B., Edwards, S. L. & Hammer, J. M. (1993). Modelling the dynamics of mental workload and human performance in complex systems.
IEEE Transactions on systems, cybernetics, 23, 1662-1671.
Smiley, A. & Brookhuis, K. A. (1987). Alcohol, drugs and traffic safety. In J. A. Rothengatter & R. A. De Bruin (Eds.), Road Users and Traffic
Safety (pp. 83-105). Assen, N. L.: Van Gorcum.
Theeuwes, J. (2002). Sampling information from the road environment. In R. Fuller & J. A. Santos (Eds.), Human Factors for Highway
Engineers (pp. 131-145). Amsterdam, N.L.: Pergamon Press.
Thiffault, P. & Bergeron, J. (2003). Monotony of road environment and driver fatigue: a simulator study. Accident Analysis & Prevention 35 (3),
Verwey, W. B. (2000). On-line driver workload estimation. Effects of road situation and age on secondary task measures. Ergonomics 43(2),
Verwey, W. B. & Veltman, H. A. (1996). Detecting short periods of elevated workload: a comparison of nine workload assessment techniques.
Journal of Experimental Psychology: Applied 2 (3), 270-285.
Verwey, W. B. & ZaideL, D. M. (1999). Preventing drowsiness accidents by an alertness maintenance device. Accident Analysis & Prevention 31
(3), 199-211.
Wu, C. & Liu, Y. (2009). Development and evaluation of an ergonomic software package for predicting multiple-task human performance and
mental workload in human-machine interface design and evaluation. Computers & Industrial Engineering 56 (1), 323-333.
Young, M. S. & Stanton N. A. (2007). What's skill got to do with it? Vehicle automation and driver mental workload. Ergonomics 50 (8), 1324-
Yeh, Y. Y. & Wickens, C. D. (1988). Dissociation of performance and subjective measures of workload. Human Factors: The Journal of the
Human Factors and Ergonomics Society 30(1), 111-120.
Zijlstra, H. & Van Doorm, L. (1985). The Construction of a Scale to Measure Perceived Effort. Department of Philosophy and Social Sciences,
Delft University of Technology, Delft, NL.
Zijlstra, H. (1993). Efficiency in Work Behavior. A Design Approach for Modern Tools (Published doctoral thesis). Delft University of
Technology, Deft University Press, Delft, NL.
... Factors that affect driver's attention while driving, also affect traffic safety. Drivers face many overlapping and often competing demands on their limited information processing resources while navigating the driving environment (Silva, 2014;Metz et al., 2011;Regan et al., 2011;Young et al., 2007). ...
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This research aim is to evaluate how deaf and hearing traffic participants assess their driving abilities and to determine frequency of communication, traffic violations and traffic accidents that respondents are involved in. Sample of respondents was comprised of 60 drivers, 30 deaf and 30 hearing. Qualitative and quantitative analysis was used in data processing. Self-assessment was conducted by conducting a survey about their driving abilities and abilities of the other subsample of respondents. Differences in answers were determined with t-test. Results indicate that both subsamples assess their driving abilities as very good and their driving as safe. There are differences in the safety aspect of the driving, where hearing drivers expressed some doubt about this aspect when it comes to deaf drivers. By assessing frequency of communication during driving, it has been concluded that hearing drivers communicate more often and have more involvement in traffic accidents. When it comes to correlation between frequency of communication and number of committed traffic violations, no significant differences were observed between the two subsamples. Despite self-assessment of driving ability being very good and safe by both subsamples of respondents, traffic accidents are a very frequent occurrence. Although, conversation with passengers is not prohibited, drivers should be advised to reduce it to minimum, as it has proven to be one of the significant distracting factors when it comes to driving.
... Road accidents, indicated as one of the main causes of injury and death, are frequently related to the underestimation of drivers' mental workload (MW) and fatigue [1]. The world of research is consistent in assuming that crash risks are strongly related to driver mental workload [2,3]. Hence, predicting cognitive states, such as mental overload, could be fundamental to prevent traffic accidents. ...
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Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.
... The finished model of the workplace with the component was subsequently transferred to the Tecnomatix software environment, where a simulation and animation of the operators' movements on the selected work section was created [1], [4]. Based on the performed analyzes, problematic actions were identified and subsequently improvements were proposed to eliminate these problems. ...
The article describes the analysis of a work operation on a virtual model of a production workplace. In order for the virtual workplace to correspond to reality as much as possible, 3D scanning was used to create a digital model of the part that the worker is manipulating. Tecnomatix Jack software was used to create a virtual workplace model and simulate worker movements. The operation was analyzed by four different methods, namely RULA analysis, OWAS analysis, Comfort Assessment analysis and Lower Back analysis. The results of all analyzes for the same task show that the job is not properly designed and it is necessary to propose workplace changes to eliminate the identified problems.
... In recent decades, the roles of operators tend to be as decision makers and monitors instead of manual workers in a modern human-machine system. The mental workload (MW) can be regarded as the proportion of information processing capability used to perform a task, which is determined by the amount of resources required by a set of concurrent tasks, as well as by the use of resources needed to perform them [1]. Studies have shown that an excessive MW lasting for a long time can cause rapid fatigue, frustration, reduced flexibility and increased mistakes; but too low a MW can lead to the waste of resources and dissatisfaction, which might reduce job performance [2]. ...
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Mental workload (MW) assessment has been widely studied in various human–machine interaction tasks. The existing researches on MW classification mostly use non-invasive electroencephalography (EEG) caps to collect EEG signals and identify MW levels. However, the activation region of the brain stimulated by MW tasks is not the same for every subject. It may be inappropriate to use EEG signals from all electrode channels to identify MW. In this paper, an EEG rhythm energy heatmap is first established to visually show the change trends in the energy of four EEG rhythms with time, EEG channels and MW levels. It can be concluded from the presented heatmaps that this change trend varies with subjects, rhythms and channels. Based on the analysis, a double threshold method is proposed to select sensitive channels for MW assessment. The EEG signals of personalized selected channels, named positive sensitive channels (PSCs) and negative sensitive channels (NSCs), are used for MW classification using the Support Vector Machine (SVM) algorithm. The results show that the selection of personalized sensitive channels generally contributes to improving the performance of MW classification.
A common cause of road traffic incidents is driver distraction, which can occur when the driver’s attention is engaged in a concurrent secondary task. However, the relationship between cognitive demands, individual differences in working memory capacity and driving performance has received little research attention. Using a fixed-base driving simulator, the aim of this study was to use a combination of self-report, functional near infrared spectroscopy (fNIRS) and mobile eye tracking data to investigate the impact of cognitive load on drivers’ gaze behaviour and driving performance, as well as explore the relationship between working memory capacity and driving performance under increased cognitive load. Twenty-one participants with a range of driving experiences (e.g., 0–14 h per week) completed a simulated driving task in a simple environment (i.e., country highway) under single-task (driving only) and dual-task (driving + modified 2-back task) conditions. Cognitive load was assessed via fNIRS data that manifested as changes in regional oxygen saturation (rSO2) in prefrontal cortex as well as self-report data of perceived mental effort. Participants’ working memory capacity was assessed using the Operation Span Task. Findings showed that prefrontal rSO2 and perceived mental effort was significantly greater under dual-task conditions compared to the single-task condition. In the dual-task condition, participants’ gaze dwelled for longer on the road and they made fewer fixations, of longer durations. Participants were able to maintain driving performance in this condition, although this was at the expense of secondary task performance. Interestingly, driving infractions under dual-task conditions were negatively correlated with participants’ working memory capacity. The findings suggest that engaging with distracting secondary tasks while driving may increase drivers’ cognitive load and change their gaze behaviour. Driving performance can seemingly be maintained under such conditions, but this may be partly determined by the driver’s working memory capacity.
The design of advanced driver-assistance systems (ADASs) and driving monitoring systems can be improved with a better understanding of on-road driving behavior. Information on traffic environment, health, engagement in secondary tasks, and corresponding eye glance locations can provide important insights into driver’s in-vehicle movements and status. This study examines the in-vehicle head movement of the driver and the factors affecting it using data from a naturalistic driving study. Linear mixed models were used to examine head position at a given point in time, and hierarchical clustering with dynamic time warping (DTW) was used to explore the trajectories of the head position. The findings show that some health conditions (e.g., limited flexibility, cancer) were correlated to restrictive head position during driving, and vehicle acceleration and traffic environment factors contributed to the variation in head positions.
We investigated the effect of time-on-task on driver’s mental workload and driving performance during a simulated driving task. The extent of mental workload was estimated from steering entropy, while driver performance was measured from the Standard Deviation of Lateral Position (SDLP) and the Standard Deviation (SD) Sterring Angle. Seventeen participants underwent a simulated highway driving task for 60 min. The results show that mental workload increased significantly after 15 min, whereas driving performance did not degrade until 30 min. These results suggest that when drivers first affected by time-on-task, they can cope with the situation by increasing mental effort investment and can manage to perform normally for a while (15 min). Since changes in steering entropy precede changes in driving performance, this measure of mental workload may have utility as a predictor of increased accident risk.
Young drivers are more likely to be involved in traffic accidents. The study aims to explore mechanisms behind distracted driving behaviour, traffic safety environment, driving responsibility, and hazard perception. A conceptual model is proposed based on Stimulus-Organism-Response (S-O-R) theory. The self-reported data from 367 drivers are used to estimate and modify the model based on exploratory factor analysis, structural equation modelling, and bias-corrected bootstrap method. The regression relationships and the mediators have been identified. The traffic safety environment including the traffic enforcement and the driving condition isn’t related to the distracted driving behaviour. The traffic enforcement is associated the driving responsibility, the relationships between the driving responsibility, the hazard perception and the driving condition are significant, and the relationships between the distracted driving behaviour, the driving responsibility and the hazard perception are noteworthy. A positive traffic safety environment is beneficial to the safety of young drivers. The sense of driving responsibility and the self-cognition of hazard perception need attention for the early intervention of young drivers’ distracted driving behaviours.
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Background and objective: The cognitive workload is an important component in performance psychology, ergonomics, and human factors. Publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad estimation based on Eye-Tracking dataset is presented. Methods: Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search activities of varying complexity and duration. The participants' cognitive workload level was evaluated with the subjective test of NASA-TLX and this score is used as an annotation of the activity. Extensive data analysis was performed in order to derive eye and gaze features from low-level eye recorded metrics, and a range of machine learning models were evaluated and tested regarding the estimation of the cognitive workload level. Results: The activities induced four different levels of cognitive workload. Multi tasking and time pressure have induced a higher level of cognitive workload than the one induced by single tasking and absence of time pressure. Multi tasking had a significant effect on 17 eye features while time pressure had a significant effect on 7 eye features. Both binary and multi-class identification attempts were performed by testing a variety of well-known classifiers, resulting in encouraging results towards cognitive workload levels estimation, with up to 88% correct predictions between low and high cognitive workload. Conclusions: Machine learning analysis demonstrated potential in discriminating cognitive workload levels using only eye-tracking characteristics. The proposed dataset includes a much higher sample size and a wider spectrum of eye and gaze metrics than other similar datasets, allowing for the examination of their relations with various cognitive states.
Electric vehicle being one of the leading green technologies nowadays, is leaving a humongous amount of spent lithium-ion batteries untreated. Current research on lithium-ion battery waste management is at its minimal because the huge power range of the battery is much attractive than the battery waste dismantling process. Treating these battery wastes are crucial for rare metal recovery due to its limited resources on land. Thus, this study aims to propose an eco-design battery pack to ease the recycling process in a more economical and sustainable manner. SolidWorks is used to generate the 3D modelling and ANSYS is utilized to carry out the simulation of the product’s mechanical performance in a drop and impact tests. Results shows that the proposed design of EV battery pack has a design efficiency of one with Easy Fixings indicator of 28%. In the drop test of 0.3 m height, it yields a maximum deformation of 1.015e−3m and a generated Von-Mises stress of 4.827e 8N/m2. Other than that, 2.5227e6 N/m2 of Von-Mises stress is obtained in the impact frontal test. With a great impact of cruising at a speed of 15.6464 m/s, 5.6053e−8 m deformation is obtained in the same test. As a result, the proposed EV battery pack design has showed the potential to improve the sustainability, performance, and ease of disassembly.
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The Subjective Workload Assessment Technique (SWAT) has been used to assess mental workload in a variety of situations. As with subjective techniques generally, use of SWAT has emphasized relative comparisons of task conditions. For example, it has been possible to determine if one task or display required a greater mental worklaod than another. For many applications, however, it would be useful to have identified a critical SWAT level that indicates likely performance degradation caused by operator overload. A review of previously completed studies suggests a range of SWAT scores that were predictive of operator overload.
reviews major categories of empirical workload measurement techniques and provides guidelines for the choice of appropriate assessment procedures for particular applications sensitivity / diagnosticity rating scales / psychometric techniques (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Five methods of measuring mental workload (secondary task performance, visual occlusion, cardiac arrhythmia, subjective opinion rating scales, and primary task performance) were compared for sensitivity to changes in operator loading. Each was used to differentiate among low, medium, and high levels of workload defined in terms of the application point of crosswind gusts in a driving task. The driving task was produced using an automobile driving simulator with a six-degree of freedom computer generated display, a four-degree of freedom physical motion system, and a four-channel sound system. Techniques of mental workload measurement that have shown promise in previous studies were used as a between-subjects factor, and subjects were presented with a within-subject factor of wind gust placement. Gusts at the front of the vehicle represented high workload levels, and gusts toward the center of the vehicle represented progressively lower levels of workload. The results showed significant differences among workload levels for subjective opinion scales and primary performance measures of lateral deviation, yaw deviation, and steering reversals. A relative sensitivity estimate of these would be, from highest to lowest sensitivity, steering reversals and yaw deviation, rating scales, and lateral deviation. The techniques of occlusion, cardiac arrhythmia, and secondary task performance yielded no significant workload effect.
This chapter will address the following questions: What is meant by mental effort? What is its role in human information processing? How can effort be measured? The chapter will consider the ways in which the concept of effort has been used in different theories of human information processing. Effort may be measured in behavioural, subjective and physiological domains. The present approach will focus on the physiological indices. The concept of mental effort has been thought necessary in theories of behaviour for several reasons: (1) To account for individual differences in intellectual performance in terms of differences in motivation and in the availability of mental resources. (2) As an analogy for physical effort in theories of mental workload, and (3) To explain the effects of stressors such as noise, lack of sleep, time-on-task, etc, on human performance.