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Workload-Adaptive Cruise Control
Dissertation
zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.)
vorgelegt der Fakultät für Human- und Sozialwissenschaften der
Technischen Universität Chemnitz
im Juli 2014
von Wilfried Hajek, geboren am 30.12.1984 in Graz
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Zusammenfassung
In dieser Dissertation wird eine Abwandlung des Active Cruise Control (ACC) untersucht,
das zusätzlich die Belastung (Workload) des Fahrers als Parameter betrachtet, um den
Abstand zum Vordermann automatisiert zu verändern. Bei diesem ACC handelt es sich um
ein Fahrerassistenzsystem, das automatisiert die eingestellte Geschwindigkeit hält und eine
manuelle (durch den Nutzer ausgelöste) Abstandsveränderung zum Vordermann ermöglicht.
Da sich die Bremsreaktionszeit von Fahrern in hohen Belastungssituationen verschlechtert,
soll das entwickelte Workload-adaptive Cruise Control (WACC) in Situationen hoher
Belastung den Abstand zum Vordermann automatisiert erhöhen. Die Belastung des Fahrers
soll durch physiologische Daten ermittelt werden. Die vorliegende Arbeit untersucht die
Möglichkeit eines solchen Systems unter der Annahme, dass in Zukunft geeignete
physiologische Sensoren ins Auto eingebaut werden können.
Die Arbeit besteht aus drei Teilen:
Im ersten Teil wird der theoretische Hintergrund beschrieben und ein passendes
theoretisches Modell entwickelt.
Im zweiten Teil werden die durchgeführten Experimente beschrieben.
Im dritten Teil werden die Ergebnisse diskutiert.
Insgesamt wurden im Rahmen dieser Arbeit vier Experimente durchgeführt:
Das erste Experiment beschäftigte sich mit den grundlegenden Zusammenhängen zwischen
Physiologie, Bremsreaktionszeit und Belastungslevel. Wie die Ergebnisse der im Simulator
durchgeführten Studie zeigen, können mit physiologischen Daten wie Herzrate,
Herzratenvariabilität und Hautleitfähigkeit unterschiedliche Workloadlevel identifiziert
werden. Darüber hinaus wurden die Ergebnisse anderer Studien bestätigt, die belegen, dass
Workload die Bremsreaktionszeit erhöht, wobei dies nur im Kontrast zwischen den
Extrembereichen „kein Workload“ und „hoher Workload“ nachweisbar ist.
Das zweite Experiment diente der Simulierung eines perfekten WACC. Im Simulator wurden
Akzeptanz und Systemwahrnehmung getestet, um vor der Implementierung in ein
Realfahrzeug weitere Erkenntnisse zu gewinnen. Im Vergleich zum ACC wurde das WACC
von den Probanden besser akzeptiert, nachdem sie zusätzliche Informationen zum neuen
System erhalten hatten. Der wesentliche Grund dafür ist, dass die Probanden ohne
Informationen die Abstandsveränderung bei hohem Workload nicht realisieren.
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Das dritte Experiment fokussierte auf die Akzeptanz des simulierten Systems unter
Realbedingungen. Das WACC wurde in das Auto integriert und durch ein MATLAB Modell
gesteuert. Als Ergebnis zeigte sich, dass unter Realbedingungen mehr Probanden die
Abstandsveränderung realisieren als im Simulator. Generell wird das WACC präferiert – vor
allem jene Probanden, die die Abstandsveränderung nicht realisieren, bewerten das WACC
besser als das ACC. Mit den in diesem Teilexperiment erhobenen Daten wurde ein
Algorithmus zur Workloaderkennung entwickelt. Auf dieser Basis konnte im letzten
Experiment ein Realsystem implementiert werden, das aufgrund physiologischer Daten den
Abstand verändert.
Das vierte Experiment beschäftigte sich mit der Validierung des Algorithmus zur
Workloaderkennung. Die Ergebnisse des Algorithmus wurden mit der aufgezeichneten
Aktivierung des Workloadtasks verglichen und eine Detektionsrate ermittelt. Die Detektion
der Workloadperioden gelingt in fast allen Fällen und die Detektionsrate ist vielversprechend,
gerade wenn man Verzögerungen berücksichtigt, die wegen der Latenzzeit körperlicher
Reaktionen nicht verbesserungsfähig sind.
In den vorliegenden Experimenten konnte gezeigt werden, dass Workload über die
Physiologie messbar ist und sich auf die Bremsreaktionszeit auswirkt. Darüber hinaus wurde
gezeigt, dass ein WACC technisch machbar ist und die Ergebnisse lassen außerdem auf eine
hohe Akzeptanz schließen.
Die Forschungsergebnisse wurden in einem Artikel publiziert, der auch in dieser Dissertation
zu finden ist. Teile der vorliegenden Arbeit wurden außerdem als Buchkapitel veröffentlicht
(siehe Fußnoten), eine weitere Publikation mit den vom Autor umfassend betreuten
Diplomanden ist in Ausarbeitung. Um die Nachvollziehbarkeit zu gewährleisten, wurde auf
die Seitenzahlen der entsprechenden Diplomarbeiten verwiesen.
Als Autor dieser Dissertation habe ich das Thema WACC von Anfang bis Ende selbst
erarbeitet bzw. wurden Studenten eingesetzt und angeleitet, wo es sinnvoll erschien. Neben
der fachlichen wie personellen Führung der Studenten umfassten meine Aufgaben die
Planung des Gesamtprojekts, das Durchführen der Studien und die Berechnung von
Kennzahlen – sowohl in meinem eigenen psychologischen Fachgebiet als auch
interdisziplinär mit Hilfe von Experten aus der Informatik. Jedes Ergebnis, das in dieser
Arbeit präsentiert wird, wurde entweder von mir selbst erzielt oder – sofern ich dabei von
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Studenten unterstützt wurde – mit mir in wöchentlichen (oft auch mehrmals täglich
stattfindenden Meetings) besprochen.
Wichtig war mir als Autor, einen durchgängigen Weg zur Entwicklung eines WACC zu
wahren, mein psychologisches Fachwissen täglich anzuwenden und in interdisziplinären
Aufgaben und Diskussionen meine Perspektive einzubringen. Besonders wichtig war dabei
die Diskussion des Gesamtprojekts und der Details mit externen Partnern wie dem MIT
AgeLab oder Professoren aus der Europäischen Union im Rahmen des Adaptation Projekts
(ein von der EU gefördertes Projekt zur Ausbildung von Forschern, unter Einbindung
wirtschaftlich und wissenschaftlich hochrangiger Partner). Durch die Präsentation der
Ergebnisse auf Konferenzen, in Workshops und Publikationen konnte ich einen Beitrag dazu
leisten, um die Adaptation-Ziele zu erreichen. Innerhalb der BMW Group Forschung und
Technik habe ich darauf geachtet, relevante Schnittstellen- und Projektpartner zu
identifizieren und das erlangte Wissen zu einem Ergebnis zu verbinden, das Wissenschaft
und Wirtschaft gleichermaßen nützt.
Abstract
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This doctoral thesis focuses on the modification of ACC to include actual driver workload in
the context of automatic headway change. ACC is a driver assistance system that
automatically maintains a preliminary defined speed and permits a driver to perform manual
headway changes. As drivers show worse (brake) reaction times under high workload
situations, the system increases headway accordingly. Driver workload is estimated based on
physiological data. Here, we investigate the possibilities of such a system, assuming that
physiological sensors can be implemented in future vehicles.
The thesis consists of three parts: In the first part, the theoretical background is described and
a suitable theoretical model is developed; in the second part, experiments are described, and
in the last part, results are discussed. Altogether four experiments support this thesis:
1. The first experiment investigates the foundational relationships between physiology,
brake reaction time and workload level. The study employs a simulator setting and
results show that physiological data, such as heart rate and skin conductance, permit
the identification of different workload levels. These findings validate the results of
other studies showing that workload leads to an increase in reaction time. These
results could only be validated between the extremes “no-workload” and “high
workload” situations.
2. The second experiment simulates an ideal workload-adaptive cruise control
(WACC) system. In a simulator setting, system acceptance and awareness are studied,
with a view toward future implementation in a real car. The results show better
acceptance of WACC in comparison with ACC when subjects receive additional
information about the new system. This is because subjects do not perceive changes in
distance under high workload conditions.
3. The third experiment focuses on acceptance of the simulated system in on-road
conditions. In this study, WACC is integrated in the car and is operated using a
MATLAB model. The experiment shows that more subjects notice changes in
distance in the on-road condition. In general WACC is preferred over ACC; it is
especially these subjects who do not notice changes in distance, who value WACC
more than ACC. With the aim of implementing an operational WACC that is capable
of adjusting distance according to changes in physiological data, a workload
algorithm is developed.
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4. The fourth experiment validates the workload algorithm. Results of the algorithm
are compared with recordings of the activated workload task and detection rate is
calculated. The detection of workload periods was feasible in nearly every case and
detection rate was favorable, especially if one considers lags due to design-related
latency periods.
The experiments presented here indicate that workload is detectable in physiological data and
that it influences brake reaction time. Further, we provide evidence pointing to the technical
possibility of implementing WACC as well as positive acceptance.
The results have been published as an article and are part of this thesis. Also, some parts of
the thesis are published as a book chapter (see footnotes). Another publication is in
preparation, coauthored by diploma thesis students, who are supervised by the author (consult
footnotes). This dissertation is composed, in part, of these publications. References to page
numbers of the diploma theses are given to ensure correspondence.
The author escorted the topic WACC from the beginning to the end. Sometimes students
were involved and intensively supervised, from a thematic as well as a personnel guidance
perspective. The author planned the whole project and executed studies and calculations. His
psychology insights were not only limited to the discipline of psychology but were
furthermore, with the help of students, interdisciplinarily expanded to the subject of
informatics. Every study and every result which is presented within this work, was conducted
or achieved by the author or (if students supported him) was discussed with the author in
weekly discussions (and often several times a day). In these discussions the author provided
new ideas and corrections if necessary. Apart from that, the author looked after the
fulfillment of the central theme, implemented his psychological knowledge on a daily basis
and provided his expertise to complement interdisciplinary point of views. He discussed the
central theme as well as details with external partners like the MIT AgeLab as well as
professors of the European Union from the adaption project (a project aimed at educating
future researchers which includes involvement of highly important commercial and
educational partners) and beyond. In this time he also visited conferences and accumulated
knowledge which led to the successful achievements of the main objective and he was
relevant in reaching the common goals of the adaption project. Furthermore he presented the
results of the scientific work on a conference, workshops and in written publications. Within
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BMW Group Research and Technology, he identified important department- and project-
partners and combined the knowledge to a result which benefits science and economy.
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Danksagung
Zuerst möchte ich mich bei meinem Professor Josef Krems für die Betreuung dieser Arbeit und sein Feedback bedanken. Mein Dank gilt auch Ralf Decke und Bernhard Niedermaier für die intensive Betreuung und Unterstützung seitens der BMW Group.
Bryan Reimer und Bruce Mehler vom MIT AgeLab haben mit ihrer langjährigen Erfahrung viel zum Gelingen der entstandenen Experimente beigetragen. Für die gute Zusammenarbeit danke ich besonders Karl Heinz Fleischer, Hanna Bellem, Andreas Trzuskowsky, Florian Krins und Irina Gaponova.
Danke an die Adaption Mitglieder, die mich mit Feedback und Kritik zu meinem Vorhaben unterstützt haben, sowie an die LT-Z-3 Kollegen, mit denen ich kontinuierlich an neuen spannenden Ideen arbeiten durfte.
Allen Studienteilnehmern und allen, die auf die eine oder andere Art und Weise am Gelingen dieser Arbeit beteiligt waren – dankeschön!
Abschließend danke ich meinen Eltern, meiner Familie und meinen Freunden für ihre kontinuierliche und andauernde positive Unterstützung.
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Table of Content
1 Introduction 10
2 Goal And Research Questions 13
3 Theoretical And Empirical Background 153.1 Theoretical Model 153.2 Workload Measurement over Physiology 183.3 Secondary Task: The N-Back Task 21
4 Forward Collision Warning Experiment 224.1 Introduction and Objectives 224.2 Method 224.3 Procedure 254.4 Results 304.5 Discussion and Conclusion 33
5 Workload-adaptive cruise control - A new generation of advanced driver assistance systems 35
5.1 Introduction 365.2 Material 395.3 Measurements 405.4 The Secondary Task: N-Back Task 415.5 WACC system 425.6 Procedure 425.7 Results 475.8 Discussion and conclusion 58
6 On-Road Study Of The Simulated WACC 616.1 Introduction and Objectives 616.2 Method 616.3 Results 656.4 Discussion and conclusion 70
7 Online detection of workload in an on-road setting 737.1 Introduction and objectives 737.2 Method 737.3 Results 757.4 Discussion and conclusion 79
8 General discussion 818.1 Background and chosen approach 818.2 Summary of findings 838.3 Discussion and conclusion 90
9 References 96
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1 Introduction1
The world is full of ongoing technical developments, especially in the automotive,
space and aerospace sector and humans are becoming (or already are) reliant on the support
of these systems. In the automotive sector these systems are called Advanced Driver
Assistance Systems (ADAS).
ACC systems are able to maintain vehicular speed and adapt velocity according to a
leading vehicle. Lane departure systems alarm drivers when he/she moves out of his/her lane
without using the turn signal. Automated parking systems steer vehicles into a given parking
space whereby a driver only needs to adjust speed.
These are just a few examples of the overall development within the automotive
sector concerning the usage of semi-automatic systems. Even though the goal of these
systems is to raise vehicle safety and comfortability, these developments also introduce a new
set of problems that require solutions. Technology is developing rapidly, but complex
capabilities are required to achieve these goals.
One such capability is based on the fact that driver assistance systems work semi-
automatically and therefore require driver supervision to ensure accident prevention and
compliance with legal requirements. One of the advantages of driver supervision is the
possibility of intervening when confronted with a potentially dangerous situation. On the
other hand, people generally display poor performance completing and supervising
continuous and monotone tasks, which is also known as the out-of-the-loop problem (Endsley
& Kiris, 1995); this has led to a demand for the development of fully automatic systems.
Nevertheless, a system that perfectly reacts to every possible situation and that protects
passengers from all harm is a long way off.
In the automotive sector, the realization of the above described circumstances is
triggering the development of systems that support drivers in performing monotonous tasks,
while allowing them to take control in those critical situations that could provoke an accident
(e.g. active cruise control, lane keeping systems). It is a major challenge to define those
points in time when a driver should resume vehicular control as well as defining optimal
security and time thresholds that would allow drivers sufficient time to both resume control
and take appropriate action to prevent an accident.
1 Part of the Introduction was published in a book chapter in a revised version (cf. Hajek, 2014, p. 197-198). Reproduced by permission of the Institution of Engineering & Technology.
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Currently these thresholds are mainly calculated with respect to environmental
conditions (distance to a leading car, own vehicle speed, negative acceleration of a leading
car, distance to the lane markings). Some modern systems, such as active cruise control, give
the driver the opportunity to change safety parameters manually, if desired. In this case,
drivers can adjust headway, and therefore distance, to a leading car manually.
However, there are, for example, very stressful situations, when a driver is disturbed
by passengers or preoccupied with complex emotional or logical problems, which could lead
to high workload situations. Compared to nonstressful situations, a higher safety margin
would be needed to react in a critical situation. Previous research has demonstrated the
impact of high workload on brake reaction time; e.g. Lamble, Kauranen, Laakso and
Summala (1999) found increased brake reaction time for drivers engaged in a number dialing
or addition task.
As a driver is already under high workload in such conditions, it is not possible to
further adjust safety margins. Such an action would increase already high workload, even if a
driver were to think about acting upon such safety measures. Automatic continuous detection
of actual workload levels, as well as automatic action of a car to enhance safety parameters or
warn the driver could help. This action comprises informing the driver of high workload,
which could be dangerous in the case of a sudden environmental change (e.g. accident of a
leading car), as well as increasing distance and thereby preventing an accident. Drivers with
such a system would have a higher time gap to resume control as well as more time to choose
an appropriate reaction to prevent an accident.
Coughlin, Reimer and Mehler (2009) of the MIT AgeLab have postulated a so-called
AwareCar, which is designed to continually detect driver state. According to fixed
implemented guidelines, the AwareCar should display the driver’s state and constantly
refresh (i.e., alerting or calming) the status of the driver, thus ensuring an optimal
performance range and reaction time in case of a critical situation. The AwareCar would
prevent accidents due to over- and under-loaded drivers. This thesis shows similarities as well
as differences to the MIT Agelab approach:
Coughlin et al. (2009) presents the developed adapted Yerkes–Dodson Law and
postulated the idea of bringing the driver back to an optimal (reaction time) range by alerting
or calming the driver. Although such an approach is preferable, limitations remain (e.g.
knowing the exact source of workload in order to determine the correct action needed to
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return the driver back to an optimal performance level). Our concept has been modified
according to the so-called flower model, and our research is only focused on the overloaded
side of the model. The flower model is reproduced from nature and raises safety margins of
the assistance systems if external conditions (driver workload) are worsening. According to
the flower model, the car adapts itself to the drivers’ limitations instead of bringing the driver
back in his optimal performance range.
An AwareCar should be able to continuously detect the driver’s state, and thus detect actual
driver workload at all times during driving. Concerning this “detection” mode, low-interfer-
ence techniques are necessary for continual detection of the driver’s state. The driving task it-
self should not be disturbed by this continuous detection. Therefore detection methods that do
not interfere with driving are necessary. After an extensive literature review, we concur with
MIT AgeLab researchers, who consider physiological parameters to be the best solution in
terms of low-interference measurements ― if actual developments are taken into account
concerning future technical feasibility (cf. Wartzek et al., 2011). Furthermore, physiological
measurements are currently used for examining workload during the driving context (e.g.
Brookhuis & de Waard, 2010; de Waard & Brookhuis, 1991; Clarion et al., 2009; Dusek,
Coughlin, Reimer, & Mehler, 2009; Lenneman & Backs, 2009; Mayser, Piechulla, Weiss, &
König, 2003; Mehler, Reimer, & Coughlin, 2010; Rakauskas, Ward, Bernat, Cadwallader,
Patrick, & de Waard, 2005)
The AwareCar also features a “refresh” function, which keeps the driver in an optimal
workload state and therefore, optimal performance. Even though this feature would be highly
preferable, it seems to be problematic in ensuring correct actions. The detection of reasons for
overload/underload is not easily established, and thus it is questionable if one strategy would
be feasible for all types of workload situations. The flower model approach was used as
compensatory action if high cognitive workload (comparable with a telephone call) is
detected. This approach was validated for the ACC system as a first attempt. To validate the
conceptual approach of the flower model, further research is necessary.
This thesis represents the accumulated knowledge of three years of research in driver
state detection employing physiology, workload algorithm development and compensatory
measures for high workload in the automotive sector.
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2 Goal And Research Questions
The main goal of this dissertation is to evaluate the possibility of employing
physiology as an input parameter to monitor automatic threshold changes with active cruise
control in response to different workload levels. The system we developed is called
workload-adaptive cruise control (WACC).
From a theoretical perspective, the system developed responds to the changing
capability of a driver according to the relationship between arousal and performance, also
known as the Yerkes–Dodson Law (Yerkes & Dodson, 1908). By integrating driver
capability as an input parameter in threshold calculations, this study attempts to reveal the
potential of a new generation of developed driver assistance systems. Four experiments were
performed beginning with the establishment of a theoretical foundation for the development
of simulator-based WACC and ending with the integration of the system in an on-road
vehicle.
In a first step, a driving simulator experiment was conducted to establish a correlation
between workload and changes in physiological data and brake reaction time. Two main
research questions were posed:
1. Do increases in workload levels lead to increases in brake reaction time?
2. Does each workload level lead to physiological data changes and therefore does this
stepwise change enable detection of different workload levels according to
physiological data?
After establishing the interdependency of workload, physiological data and brake reaction
time, WACC was simulated and tested in a further simulator experiment. The main research
questions were:
1. Do physiological data enable the detection of high workload levels during use of an
ACC/WACC system?
2. Do drivers in a critical situation use the higher safety gap to calculate a more
appropriate reaction instead of adapting accordingly to the risk homeostasis theory
due to lower risk?
3. Is WACC better accepted than ACC?
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After promising results of the above-mentioned simulator experiment, a simulated
WACC was implemented in a real car for an on-road experiment on the highway. The main
research questions were:
1. Is system awareness higher than in the simulator experiment?
2. Is WACC better accepted than ACC in general and in particular, if people are
unaware of what the system is capable of? Real-life data was collected for the
development of a real-time workload detection algorithm.
In the last experiment the developed algorithm was tested in an on-road highway
setting. The main research question concerned the detection rate of the driving algorithm over
the whole driving period: Therefore the question was, if overall detection rate is >70% over
the whole driving period and thus providing the evidence for technical feasibility in real-life
settings.
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3 Theoretical And Empirical Background
3.1 Theoretical Model2
The flower model, which was used as a theoretical foundation for the compensation
strategy, is an enhancement of previously developed theories. This chapter describes
differences as well as new developments to ensure that the reader obtains an in-depth
understanding of the development process, as well as the resulting outcome.
3.1.1 The Yerkes–Dodson Law.
The connection between arousal and performance is well researched since the midst
of the last century (Duffy, 1957; Freeman, 1940). The Yerkes–Dodson Law which is well
known in psychology as one of the most famous laws was even documented earlier (Yerkes
& Dodson, 1908).
The Yerkes–Dodson Law postulates an inverted u-shaped relationship between
arousal and performance, indicating that optimal performance is reached in the middle of the
graph (see curve in figure 1). On the right side of the curve, arousal is too high which
indicates that subjects are in an overload state that decreases performance. In contrast, on the
left side of the graph, arousal is too low which results in decreasing performance. The most
favorable situation is a medium level of arousal in which optimal performance is possible.
3.1.2 The adapted Yerkes–Dodson law.
Coughlin, Reimer and Mehler (2011) of the MIT AgeLab postulated an adaption of
the original Yerkes–Dodson Law for the driving task. Instead of arousal, the x-axis indicates
workload/stress. Otherwise, it is closely related to the Yerkes–Dodson Law. Too high
workload/stress leads to far too high activation or overload, and thus to a decrease in
performance. Low levels of workload/stress lead to fatigue, which corresponds to underload
in the original Yerkes–Dodson Law; this is a result of a too low activity level. Inattention and
active distraction are further implemented to visualize increments of workload changes in the
driving process.
2 This section concerning the theoretical model was published as part of a book chapter in a revised version (cf. Hajek, 2014, p. 199-200). Reproduced by permission of the Institution of Engineering & Technology.
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Figure 1 Adapted Yerkes-Dodson Law by MIT AgeLab figure published in Coughlin,
J.F., Reimer, B. & Mehler, B. (2011). Monitoring, Managing and Motivating Driver
Safety and Well-Being. IEEE Pervasive Computing, 10(3), pp. 14-21. © 2011 IEEE
Researchers of the MIT AgeLab also postulated compensational measures (alerting
and calming the driver). In general drivers should be brought back to an optimal range in the
middle, which permits optimal performance according to compensatory measures. Therefore,
if a driver is in a state of overload, calming to a lower activity level is necessary. Conversely,
if a driver is fatigued due to low activity levels, activation is needed to reach an optimal
range, and in turn, an optimal performance level.
As already mentioned, this theory faces a major problem: On the one hand
compensatory measures are strongly dependent on the reason for workload and on the other
hand it is questionable if such compensatory measures exist or whether they can be
implemented without leading to even more problems (e.g. opening the window as a
compensatory measure against fatigue could be disturbing on a rainy day; minimizing
displayed information in the central display during overload could lead to even higher
workload if the confused driver does not understand the reason for this change). Therefore a
new model is presented which aims at adapting the car to actual workload level, instead of
bringing the driver back to his optimal workload level.
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3.1.3 The flower model.
The flower model is based on both the adapted and the original Yerkes–Dodson Law
models, but only considers compensatory strategies for high workload periods. As already
described, the Yerkes–Dodson Law dictates a decrease in performance under high workload
conditions. Researchers of the MIT AgeLab postulated a theoretical model for compensatory
strategies, which seek to calm the driver and bring him back to optimal performance, that is,
the grey area in the middle of figure 1. Analyzing the problem from many different
perspectives, we have come to the conclusion that it would be preferable to bring the driver
back to optimal performance. However, without knowing the kind of (cognitive) workload
being in effect, the right compensation strategy applicable for all high workload periods
cannot be determined. Furthermore, in our opinion, workload can only be reduced through
workload-reducing measures: that is, eliminating the reason for workload. We chose another
approach due to the fact that many factors contribute to workload and and a car able to
determine all sources of workload is currently unavailable. With the continual advancement
of ADAS and emerging availability of half or fully automatic vehicles (e.g. active cruise
control, blind spot warning, active lane assist), systems simply have to choose optimal safety
parameters matched to a driver’s state of actual workload. That is, if a critical event occurs
and the driver has to resume control of the car, he must have enough time for an appropriate
reaction to prevent an accident. This approach originally occurs in nature. The hedge
bindweed (Calystegia sepium) opens its calyxes to let the pollen fly under favorable
circumstances: that is, the sun is shining. It closes the calyxes under unfavorable
circumstances, that is, rain (Mao & Huang, 2009).
In parallel, and according to this behaviour, the safety measures of ADAS, (i.e. safety
margins and overall assistance) should be low in low workload (left hand side of figure 2)
and high in high workload (right-hand side of figure 2).
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Figure 2 Flower model for safety measures of ADAS (redrawn version)
In a first validation study, the flower model was evaluated with respect to the ACC
systems’ safety margins in dependence of workload conditions (high and low workload).
3.2 Workload Measurement over Physiology3
Several studies have demonstrated that cognitive workload has a measurable effect on
physiological arousal (Brookhuis & de Waard, 2010; de Waard, 1996; Mayser et al., 2003;
Veltman & Gaillard, 1998). At the beginning of this research an online workload algorithm
was unavailable. Therefore, our research started with validating the effects of workload on
physiological arousal. Then, the collected data was used to estimate the informative potential
of physiological data for algorithm development.
3 This section was published as part of the book chapter in a revised version (cf. Hajek, 2014, p. 200-202). Reproduced by permission of the Institution of Engineering & Technology.
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3.2.1 Measuring physiological data.
Several studies show interesting results concerning workload detection, which is not
very surprising as there is a long history in measuring workload over physiological data (e.g.
Brookhuis, Driel, Hof, Arem, & Hoedemaeker, 2009; Katsis, Ganiatsas, & Fotiadis, 2006;
Mehler, Reimer, Coughlin, & Dusek, 2009; Mehler, Reimer, D’Ambrosio, Pina, & Coughlin,
2010; Mehler, Reimer, & Coughlin, 2012; Mulder, Dijksterhuis, Stuiver, & de Waard, 2009;
Liu & Lee, 2006; Wang, Sagawa, & Inooka, 1998).
To develop a robust detection algorithm, central problems in this area of research
were identified: First, much of the research has been done with very few participants; second,
physiological data are very sensitive, especially concerning participant movements, time and
environmental issues. Third, secondary tasks have to be sensitive and stable concerning
workload induction.
As the MIT AgeLab researched the n-back task very well and because of the ongoing
technical development in simulation technique a robust approach seems possible. The
following aspects were respected to enable the development of a robust detection algorithm:
1. A high number of participants: Most of the studies conducted for this thesis
provided a high (60 – 100) number of participants;
2. Accurate physiological data sampling: Certified physiological measurement devices
(g.tec USBamp) were used in performing these studies. Accordingly it was tried to eliminate
movement artifacts because of the study design which led to clear instructions concerning
major movements within the driving context.
3. Environmental issues: Because the first studies were conducted in the simulator, the
surroundings (e.g. color of the cars, trees) could be programmed in detail.
4. Data synchronization: Split second temporal synchronization of simulator and
physiological data streams enabled very detailed and reliable (scripted) analysis afterwards.
Physiological data used for recording arousal (and later on algorithm development) will be
described in the following paragraphs.
3.2.1.1 Heart rate.
The frequency of heart contractions of a human body is called heart rate (HR). The
circulatory system of the human body is provided with blood by each contraction. Electrical
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impulses causing these contractions can be measured with an electrocardiogram (ECG). HR
is measured in beats per time interval (usually minutes) (de Waard, 1996; Mulder, de Waard,
& Brookhuis, 2005).
The autonomous nervous system, consisting of sympathetic and parasympathetic
activity, is responsible for the HR modulation. Physical and/or mental effort can lead to
changes in the modulation: HR increases with higher effort compared to lower effort, or a
resting situation (Brookhuis & de Waard, 2011; de Waard, 1996).
3.2.1.2 Heart rate variability.
Heart rate variability (HRV) is derived from changing oscillation patterns because of
variable HR time durations. These patterns and frequency content is called HRV and can, like
HR, be measured by an ECG which monitors electrical impulses of the heart. With increasing
mental effort, HRV (especially the 0.10 Hz band) decreases compared to low mental effort (;
Brookhuis & de Waard, 2011; de Waard, 1996; Jahn, Krems, & Gelau, 2003).
3.2.1.3 Electrodermal activity.
Electric changes in the skin, achieved by autonomous nervous system activity, are
called electrodermal activity (EDA; also known as galvanic skin response, GSR). In general
there are two different types of EDA, one before and one after exposure to a stimulus. The
average or baseline EDA level in resting situations is called tonic EDA (also known as
electrodermal level or skin conduction level). The EDA level after exposing the subject to a
stimulus is called phasic EDA and includes the electrodermal response (EDR). EDR is the
response to a stimulus and has a high latency time of 1.3–2.5 s. Electrodermal activity is
usually measured on the hand or foot (de Waard, 1996).
3.2.1.4 Respiration.
For providing the body with oxygen and for expelling carbon dioxide the body uses
the respiratory system. Respiration can be measured by two different assessments: The first
assessment measures the depth and frequency of breathing while the second measures gas
exchange during breathing (Brookhuis & de Waard, 2010). The frequency of breathing within
a certain interval (usually a minute) is called respiration rate and is the most used assessment
measures (de Waard, 1996).
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3.3 Secondary Task: The N-Back Task4
The n-back task, as workload induction method, is a widely used secondary task,
especially as the original n-back task of Kirchner and Wayne (1958) was refined by
researchers of the MIT AgeLab. Three difficulty levels of workload, called 0-back, 1-back
and 2-back, enable a distinctive workload induction (cf. Mehler, Reimer, & Dusek, 2011). At
low workload (0-back task level) participants had to repeat the actually presented number. At
medium workload (1-back task level) participants had to keep the numbers they actually
heard in short-term memory for later presentation, and at the same time, repeat the number
they had heard before the presented number. At high workload (2-back task level)
participants had to remember two acoustically presented numbers in short-term memory and
repeat the third number verbally every 2.25 s. That is, the first and second repeated number
had to be remembered first. With the presentation of the third number they also had to
remember it but must verbally present the first number and then forget it. This procedure was
repeated continually with each new number over the duration of the whole secondary task.
Therefore after the presentation of the first two numbers, people had to verbally repeat the
number they heard two numbers before and remember the actually presented number for later
presentation. In the experiment, lengths of 1 and 2 min of n-back task duration were found to
be feasible for the experimental procedure.
The task involved a verbal presentation with an auditory response and is in line with
requirements of an ideal secondary task according to Zeitlin (1993). He postulated that an
ideal secondary task should be as minimally interfering with the primary task as possible,
easy to use and be accomplished by participants within a minimum of learning phases. The n-
back task meets all of these requirements in the form presented here.
4 As task descriptions are per definition often similar to each other, there are similarities in form and content to other publications (cf. Hajek, 2014, p. 200-201; Hajek 2013, p. 111-112)
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4 Forward Collision Warning Experiment
4.1 Introduction and Objectives
The first experiment had two objectives: (1) evaluating the potential of physiological
data as workload detection method and (2) deciding if workload-adaptive cruise control
(WACC) should only enhance distance under high workload conditions or if it should
continually adjust its distance. The latter case would occur, for example, in reacting in a
stepwise manner to increases in workload. Similar to ACC, the WACCs’ main objective as a
safety system is to provide drivers with higher reaction time gaps to perform brake
maneuvers. The answer to (2) lies in observing changes in brake reaction time under higher
workload conditions and therefore determining if brake reaction time increases in a stepwise
manner or if it increases only under high workload conditions.
4.2 Method
4.2.1 Participants.
Altogether 88 participants took part in the study. All participants were BMW
employees and were not paid for participation in this experiment. Fifteen of the 88
participants had to be excluded, 10 due to technical malfunctions and 5 due to not following
instructions and to participating in the wrong n-back task. The remaining 73 participants
drove in two conditions: one group with a forward collision warning system (n=31) and one
without such a system (n=42). The analysis of brake reaction time was only performed for
those participants without the forward collision warning system. The mean age of the overall
group was 30.3 years with a standard deviation (SD) of 8.2 years. A total of 72.6% of
participants were male and 27.4% were female. There were no significant differences in
demographic variables between the group with and group without the forward collision
warning system.
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4.2.2 Design and hypothesis.
This experiment was designed with two factors in mind. The first factor was warning,
which is a between-subject factor and has two levels: warning/no-warning. As in this text
only the no-warning level is analyzed, this factor will be ignored for further analysis.
The second factor, workload level, consists of four levels. On the first level, a cue task
was implemented that did not induce workload but provided the same setting (one number is
repeated once) as used during the other workload levels. As workload levels the 0-back task
represents low workload, the 1-back task represents medium workload and the 2-back task
represents high workload. The order of workload levels was randomized over participants.
The first hypothesis proposed that brake reaction time increases as the workload level
increases. rake reaction, measured from the point of active brake lights until the brake pedal
angle reached >1°, was chosen as dependent variable.
The second hypothesis stated that workload level can be determined by physiological
data. As dependent variables beat-to-beat HR as HR measurement, root mean square of suc-
cessive differences as HRV measurement and GSR as skin conductance measurement were
chosen.
4.2.3 Driving simulator.
A fixed-base custom built driving simulator with three 40-inch plasma screens,
steering wheel with control functions and original BMW car seat was used as experimental
setting. The driving track was designed as a three-lane straight highway with white cars
differentiated by speed, which in general drove slightly faster than the participant’s car (70
km/h). Familiar objects like trees and houses were designed to simulate a realistic
environment. Traffic density was moderate with one car in the same lane as the driver but
located far ahead and near the skyline. This car was included to give participants the feeling
of not driving alone on their lane of the straight stretch of the highway.
4.2.4 Vital sign measurement device.
Physiological data was recorded with a g.USBamp biosignal amplifying device by a
Vienna-based company called g.tec. It was used for monitoring ECG, GSR and respiration
measurements. Standard GSR electrodes were replaced with gold electrodes by Zynex
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Medical to reduce hand gripping artifacts at the steering wheel. Sampling rate was 256 Hz
and data were streamed to a MATLAB Simulink Model for post-processing of data. Data
were stored on a notebook for later analysis and an integrated filter variable in g.tec post-
processing Simulink box established the quality of the ECG signal. The occurrence of double
beats, skipped beats and other etopic beats were recorded and eliminated, ensuring a high
quality ECG signal. GSR change rate according to the following formula was derived for the
analysis of the GSR signal:
GSR change rate = Y−Y Base
Y Base100
where Y is defined as a sliding window of 2-s lengths and Y Base as a fixed window of
20-s length. The later window was immediately recorded with the start of the measurement.
Further analysis included HR measurements, root mean square of successive differences
(RMMSD) as HRV measurement and respiration rate as respiration measurement from the
respiration belt equipped with pressure electrodes (cf. Hajek, Gaponova, Fleischer, & Krems,
2013).
4.2.5 The secondary task: n-back task.5
The n-back task used herein is described chapter 3.3. Please consult this chapter for
theoretical and foundational explanations.
Four 30-s segments with 10 numbers each were presented within one workload
period. Different workload levels of the n-back task were chosen to find the workload cut off,
that is, establishing the point when mental workload became too high to compensate for,
without decreases in performance. Auditory stimuli presentation and verbal answering
behaviour were used to induce response behaviour similar to that of a demanding phone call.
This task combination has been used in various studies (Mehler et al., 2009; Mehler, Reimer,
& Coughlin, 2010; Mehler, Reimer, & Wang, 2011; Reimer, Mehler, Coughlin, Godfrey, &
Tan, 2009). First, participants were trained until they responded correctly to at least 80% of
the questions before starting the experiment. Numbers were translated from original protocol
to German language and the recording of these numbers was played with a volume
distinguishable from street sounds so that participants could clearly hear the numbers apart 5 As task descriptions are per definition often similar to each other, there are similarities in form and content to other publications (cf. Hajek, 2014, p. 200-201; Hajek 2013, p. 111-112)
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from other sounds (cf. Hajek et al., 2013). This design was followed consistent (apart from
changes in the presentation of different workload levels) throughout this thesis.
4.3 Procedure
Before starting, participants were told that the purpose of the experiment was to
gather first results in the area of vital sign detection. Participants were prevented from
learning what the main focus of the experiment was, and therefore did not receive further
information that could lead to artifacts in measurement data. Furthermore, participants were
instructed that a secondary task had to be solved at certain times along the route. After filling
out a demographic survey, participants received information about the placement of
physiological sensors and then had to fix them to their bodies themselves. Information
concerning anonymity of the recording, telephone usage and instructions concerning
simulator sickness were provided. After this, it was confirmed that physiological data
recorded by the notebook was in the correct value range. Then, participants were trained to
perform the n-back task until they were able to repeat >80% of the answers correctly for each
n-back task level.
Shortly before beginning the introductory session, participants received information
about the simulator’s automatic transmission and were instructed to respect road traffic
regulations. During the introductory session, participants also learned all of the n-back task
levels and experienced the three events that could occur within the simulator experiment.
Participants were instructed to maintain a speed of 70 km/h. To ensure that they were
able to fulfill this task, the simulator protocol was scripted in the following way: After
reaching 70 km/h only a gas pedal change >15° or use of the brake pedal led to an increase or
decrease in speed. If participants stayed within 15° gas pedal change, the car would
constantly drive 70 km/h. Approximately every 3 minutes, a car switched from the middle
lane to the right lane and performed one of the three events that had been experienced in the
introductory session.
4.3.1 Noncritical event 1.
In noncritical event 1 (NBE1), the foreign vehicle (FV) starts rising its speed until it
drives 10 km/h faster (80 km/h) than the participant’s vehicle (PV). Then the FV changes
lanes with a vertical velocity of 5.4 km/h and with a headway of 0.9 s to the PV. It then
accelerates further on with 0.5 m/s² until it is out of sight.
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4.3.2 Noncritical event 2.
In noncritical event 2 (NBE2), the FV starts increasing its speed as in NBE1 reaching
a 10-km/h speed difference to the PV. After changing lanes in front of the participant it also
accelerates with 0.5 m/s² until it reaches a headway of 1.8 s then decelerates moderately until
it reaches a speed of 55 km/h. After this maneuver it accelerates again until it is out of sight.
4.3.3 Critical event.
In the critical event (CE) the FV starts increasing its speed until it is 10 km/h faster
than the PV and changes lanes with a vertical velocity of 5.4 km/h and a headway of 0.9 s to
the PV. Afterwards it accelerates with 0.5 m/s² until it reaches a headway of 1.5 s and then
decelerates at –5.04 m/s² until it reaches 25 km/h or until the PV passes the FV.
The events defined here are based on an earlier workload study by Engström, Aust
and Viström (2010). The exact sequence of workload periods, with and without CEs, is
outlined in table 1.
Phase Name Starting
Time
Duration Workload Description
Intro
duct
ion
N-back task training 00:00:00 5 min
Training of all workload
levels (cue, 0-, 1-, 2-back
task)
Introduction to the
simulator00:05:00 5 min
Showing the participant
the setting; letting him
experience all three
maneuvers and the n-
back task
Break Break 00:10:00 1 min
Ada
ptio
n
Adaption 00:11:00 3 minGetting used to the
simulator (physiology) –
driving single task
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One
Ses
sion
Cou
nter
bala
nced
by
4 w
orkl
oad
leve
ls(C
ue b
ack)
Separation period 00:14:00 30 s Driving single task
Reference 00:14:30 30 s Driving single task
Separation period 00:15:00 30 s Driving single task
Instructions cue 00:15:30 20 s cue Hearing Instructions
Start of the cue 00:15:50 2 min cue Repeating one number
Recovery 00:17:50 30 s Driving single task
Driving single task 00:18:20 30 s Driving single task
Separation period 00:18:50 30 s Driving single task
Instructions cue 00:19:20 20 s cue Hearing Instructions
Start of the cue 00:19:40 2 min cue Repeating one number
Cut in 00:21:10 (15 s) cueCut in of the foreign
vehicle - braking event!
Recovery 00:21:40 30 s Driving single task
Driving single task 00:22:10 30 s Driving single task
Separation period 00:22:40 30 s Driving single task
Driving single task 00:23:10 1 min Driving single task
Cut in 00:23:40 (15 s)Cut in of the foreign
vehicle – acceleration
Recovery 00:24:10 30 s Driving single task
Driving single task 00:24:40 30 s Driving single task
Separation period 00:25:10 30 s Driving single task
One
Ses
sion
Cou
nter
bala
nc
Instructions 0-back task 00:25:40 20 s 0-back Hearing Instructions
Start of the 0-back task 00:26:00 2 min 0-back Doing 0- back task
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ed b
y 4
wor
kloa
d le
vels
(0-b
ack)
Recovery 00:28:00 30 s Driving single task
Driving single task 00:28:30 30 s Driving single task
Separation period 00:29:00 30 s Driving single task
Instructions 0-back task 00:29:30 20 s 0-back Hearing Instructions
Start of the 0-back task 00:29:50 1 min 0-back Doing 0- back task
Cut in 00:30:20 (15 s) 0-backCut in of the foreign
vehicle – deceleration
Recovery 00:30:50 30 s Driving single task
Driving single task 00:31:20 30 s Driving single task
Separation period 00:31:50 30 s Driving single task
Instructions 0-back task 00:32:20 20 s 0-back Hearing Instructions
Start of the 0-back task 00:32:40 2 min 0-back Doing 0-back task
Cut in 00:34:10 (15 s) 0-backCut in of the foreign
vehicle - braking event!
Recovery 00:34:40 30 s Driving single task
Driving single task 00:35:10 30 s Driving single task
Separation period 00:35:40 30 s Driving single task
Instructions 1-back task 00:36:10 20 s 1-back Hearing Instructions
Start of the 1-back task 00:36:30 2 min 1-back Doing 1-back task
One
Ses
sion
Cou
nter
bala
nced
by
4
wor
kloa
d le
vels
Recovery 00:38:30 30 s Driving single task
Driving single task 00:39:00 30 s Driving single task
Separation period 00:39:30 30 s Driving single task
Instructions 1-back task 00:40:00 20 s 1-back Hearing Instructions
Start of the 1-back task 00:40:20 1 min 1-back Doing 1-back task
Cut in 00:40:50 (15 s) 1-backCut in of the foreign
vehicle - acceleration
Recovery 00:41:20 30 s Driving single task
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(1-b
ack)
Driving single task 00:41:50 30 s Driving single task
Separation period 00:42:20 30 s Driving single task
Instructions 1-back task 00:42:50 20 s 1-back Hearing Instructions
Start of the 1-back task 00:43:10 2 min 1-back Doing 1-back task
Cut in 00:44:40 (15 s) 1-backCut in of the foreign
vehicle - braking event!
One
Ses
sion
Cou
nter
bala
nced
by
4 w
orkl
oad
leve
ls(2
-bac
k)
Recovery 00:45:10 30 s Driving single task
Driving single task 00:45:40 30 s Driving single task
Separation period 00:46:10 30 s Driving single task
Instructions 1-back task 00:46:4020 s
2-back Hearing Instructions
Start of the 1-back task 00:47:00 2 min 2-back Doing 2-back task
Recovery 00:49:00 30 s Driving single task
Driving single task 00:49:30 30 s Driving single task
Separation period 00:50:00 30 s Driving single task
Instructions 2-back task 00:50:30 20 s 2-back Hearing Instructions
Start of the 2-back task 00:50:50 2 min 2-back Doing 2-back task
Cut in 00:52:20 (15 s) 2-backCut in of the foreign
vehicle – braking event
Recovery 00:52:50 30 s Driving single task
Driving single task 00:53:20 30 s Driving single task
Separation period 00:53:50 30 s Driving single task
Driving single task 00:54:20 1 min Driving single task
Cut in 00:54:50 (15 s)Cut in of the foreign
vehicle – deceleration
Cal
min
g
Recovery 00:55:20 30 s Driving single task
Driving single task 00:55:50 30 s Driving single task
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Separation period 00:56:20 30 s Driving single task
Sum 00:56:50 56 min 50 s
Table 1: Sequence of events
Workload sessions were counterbalanced over participants to eliminate systematic
errors resulting from order effects.
4.4 Results
4.4.1 Analysis of physiological data.
Data were split into the according workload periods without events (straight driving
and doing secondary task without any events) and means were calculated over all participants
and compared over all workload levels to establish the effect of workload on physiological
data. The use of verbal answering behaviour resulted in the exclusion of respiration data from
the analysis. That means, as an effect, respiration could not be clearly related to workload as
it might have resulted from speaking and therefore may have altered breathing patterns.
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Figure 3 Physiological data versus workload level
ANOVA results show highly significant results for BTB rate [F(2.2, 157.3)=103.78 p
< 0.001], root mean square of successive differences (RMSSD) [F(2.8, 200.8)=21.1 p <
0.001] and significant results for GSR change rate [F(3, 216)=3.045 p < 0.05]. These results
are first indicators that workload levels can be distinguished according to physiological data
and are in line with other studies, indicating that physiological data are responsive to
workload changes. Our demographic analyses support these results (cf. figure 3)
4.4.2 Analysis of brake reaction time.
To establish workload cutoff, that is, the point at which performance decreased and
participants were no longer able to compensate additional workload while maintaining their
performance, brake reaction time was measured. Brake reaction time was derived only from
CE, from the point when the leading car was braking to the point when the brake pedal angle
was >1°. Other events like NBE1 and NBE2 were included to make the occurrence of the
braking events less predictable. From the 42 participants, 6 participants did not use the gas
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pedal at the point of the measurement (decelerating very soon through slow release of the
foot on the gas pedal). Therefore reaction times of these participants were excluded from the
analysis to eliminate data from those who did not display the same initial situation as
everybody else.
Figure 4 Mean brake reaction time and workload level
ANOVA results show no significant results [F(2.4, 83.6) = 1.667, p >.05, n.s.]
suggesting that there is not a significant constant increase in reaction time, even though the
graph suggests this (cf. figure 4). Contrast analysis between cue and 2-back task show
significant results [F(1,35) = 6.008, p < .05] and therefore indicate that an increase in reaction
time occurred only at the highest workload level.
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4.5 Discussion and Conclusion
The study presented here is the first of several experiments investigating the
possibility of developing WACC. Our two major hypotheses have been answered. The first
hypothesis was, that brake reaction time increases with increasing workload. We found that
brake reaction time is only influenced at a higher workload level, which was simulated by
employing the 2-back task in this experiment. The second hypothesis was that workload level
can be determined by physiological data. The analysis suggests an overall effect during all
stages; workload versus physiology is not only identifiable for high workload periods but also
for lower periods.
These results show that increases in workload can be detected before performance de-
creases are revealed. Moreover, certain countermeasures could be implemented to prevent
drivers from reaching high workload states, which in turn would lead to performance de-
creases, that is, brake reaction time increases.
These findings are not new. As stated in the chapter “Workload measurement over
physiological data”, several studies have found an impact of workload on physiological data.
Furthermore effects of the n-back task on certain driving parameters have been investigated
by researchers of the MIT AgeLab (Mehler et al, 2009; Mehler, Reimer, & Coughlin, 2010;
Reimer, Mehler, Wang, & Coughlin, 2012).
This study therefore aims to establish functionality of the used WACC design for further stud-
ies and provide deeper insights for the overall development of WACC. In general, WACC
should be used as a simulation of the compensation behaviour. For future experiments we
successfully confirmed that the n-back task influences physiological data. These results also
confirmed that the setting was valid and that noise in physiological data was low enough to
obtain reliable results.
We describe and employ a novel method, the refined n-back task, in estimating its in-
fluence on brake reaction time. The results presented herein show that brake reaction time is
indeed influenced. Statistical analyses indicate a significant difference between high and low
workload levels. The visual analysis, as presented in figure 4, indicates a continual increase
of brake reaction time over workload levels. As brake reaction time experiments are very sen-
sitive, the possibility of a continual increase of brake reaction time with continual increase of
workload levels is not out of the question and thus warrants future experiments beyond this
thesis.
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Our findings indicate that a gradual WACC system, which only adjusts its distance be-
tween low and high workload levels, is the right way to go from a driving parameters point of
view.
The development of a more finely graduated WACC, is supported by the results of
these experiments in the detection of lower workload levels over physiological data. In partic-
ular, heart measurements (i.e. BTB, RMSSD) provide the foundation for continual detection
of different workload levels.
In sum, the design of the research setting was found appropriate and findings from
other studies, especially on influence of workload on physiological data, were confirmed. Fu-
ture experiments concerning the development of a WACC using this setting are possible.
Findings concerning the preference for continual or gradual WACC are mixed, but among
driving data, brake reaction time seems to be more in favor of a binary WACC.
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5 Workload-adaptive cruise control - A new generation of advanced driver assistance systems6
W. Hajek1, I. Gaponova1, K. H. Fleischer1 and J. Krems ²
1
BMW Group Research and Technology
Munich, Germany
{Wilfried.Hajek, Irina Gaponova, Karl-Heinz.Fleischer}@bmw.de
²
Department of Cognitive and Work Psychology
Chemnitz University of Technology
Chemnitz, Germany
Corresponding author Wilfried Hajek, BMW Group Research and Technology,
Hanauerstraße 46, 80992 Munich, email: [email protected], Tel.: +49-171-3050149
Abstract
A foreseeable development of ADAS is the adaptation of ADAS’s control parameters
to the actual workload of the human operator, enabling a level of assistance appropriately
gauged to a driver’s current resources. Before such a feature can be introduced, however,
three questions must be answered: (1) Is it technically possible to detect high workload levels
using low-interference techniques? (2) Can such a system increase safety? (3) How can
acceptance of such a system be optimized and confusion minimized?
To answer these questions a simulator study was conducted using two systems: first, regular
ACC and second, workload-adaptive cruise control (WACC). Participants were connected to
a physiological signal measurement device that recorded heart rate, galvanic skin response
and respiration. Participants also filled out subjective questionnaires to establish acceptance
and system awareness. In cases for which usable physiological data were available, high
workload conditions were identified in 83.7% of the classification sample by an algorithm
6 Published in Transportation and Research Part F: Traffic psychology and Behaviour (cf. Hajek, Gaponova, Fleischer, & Krems, 2013, p. 108-120)
W o r k l o a d A d a p t i v e C r u i s e C o n t r o l P a g e | 36
based on physiological measurements. These data show that it is technologically possible to
adapt driver assistance systems that employ physiological data for the detection of driver
workload.
Subjective measurements showed a preference for the WACC system. Moreover,
objective data measurements revealed a safety advantage of the WACC over the ACC
system: using WACC, no significant difference in brake reaction time, but a significant
lower rate of deceleration, was found. Furthermore, 85.1% of participants were unaware of
the adaptive behaviour of the WACC, which was simulated by a change from a 1-s to a 2-s
headway. These results suggest that a nondetectable change in regulation parameters led to
higher safety in critical situations. Therefore, WACC systems should be considered as a next
step in the development of ADAS.
Keywords: Adaptation, Advanced Driving Assistant system, Physiological
Measurement, ACC, Vital Data, Safety, Workload.
5.1 Introduction
ADAS aim to prevent vehicular accidents by, for example, providing proximity
warnings and maintaining safe distances to other vehicles or road objects using time-to-
collision measurements. Currently, parameters that inform these types of warnings are based
on chosen preferences of drivers or static preferences of car manufacturers. Thus, these
parameters are not automatically adapted to changes in a driver’s state. This is a critical issue,
because a driver’s reaction- and control-related abilities are dependent on actual workload
(Jamson & Merat, 2005; Lamble et al., 1999).
Mental workload, also called mental effort, is the sum of the costs of cognitive
processing and is reflected in physiological measurements, such as heart rate (HR), HR
variability (HRV), respiration and galvanic skin response (GSR) (de Waard, 1996; Veltman
& Gaillard, 1998; Mayser et al., 2003; Brookhuis & de Waard, 2010). According to Rouse,
Edwards, and Hammer (1993), the term "experienced load" includes task-specific, as well as
person-specific capabilities that differ from human to human and, therefore, leads to a better
understanding of inter-individual differences in the experience of workload. The limited
capacity processing theory (Kahneman, 1973) postulates an overall capacity from which
resources are extracted to accomplish demands, which lead to high workload. The amount of
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energy needed to accomplish these demands is defined as effort, and is two-fold: state-related
effort, which is defined as the amount of energy necessary to maintain an optimal state for
performance; and task-related effort, defined as the energy necessary for controlled
information processing. Our research focuses on task-related effort, which is manipulated
through changes in a secondary task.
Physiological indicators of arousal can be measured by HR, respiration and GSR,
which are indicators for changes in mental workload-induced by secondary tasks (Brookhuis,
de Vries, & de Waard, 1991; Liu & Lee, 2006; Mehler et al., 2009, 2011; Mehler, Reimer, &
Coughlin, 2012). The connection between arousal and performance has been investigated for
decades (Duffy, 1957; Freeman, 1940). A relationship between these two variables was
introduced as the Yerkes–Dodson Law (Yerkes & Dodson, 1908) in the early 20th century,
showing a reverse U-shaped relationship between arousal and performance. That is, subjects
show optimal performance with a medium level of arousal, whereas too low or too high
arousal leads to a decrease in performance. The Yerkes–Dodson Law was adapted for the
task of driving by Coughlin et al. (2011); for a detailed graphic depiction, see referenced
paper. According to this theory, overload is characterized by a high level of stress, which
should be decreased by calming interventions to return to optimal performance. Underload is
characterized by fatigue, boredom or a state of over-relaxedness. Under these conditions, a
driver has to be alerted to reach optimal performance. While underload and overload may not
pose a danger in noncritical driving situations, they may indeed become dangerous if a
critical situation does arise. Clearly, workload-adaptive systems should support optimal
performance levels of drivers, as dangerous situations may occur at any time while driving.
Today, the task of driving modern vehicles is characterized by the use of several
automatic and semi-automatic driver assistance systems, which operate independent from a
driver’s workload level (e.g. ACC, lane departure control). Fixed timed warning parameters
can be manually adjusted or generally set according to human-independent measures of the
car and its environment. Tests with automatic and semi-automatic systems show in particular
that human drivers cannot easily and continually supervise systems and then regain control
when a situation demands it. This is better known as the out-of-the-loop problem (Kaber &
Endsley, 1997; Endsley & Kiris, 1995) and is more problematic when, during the driving
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task, critical situations occur with low frequency, which is common in real-life settings on the
road.
Future highly automatic ADAS should be developed in light of driver workload such
that a driver’s workload level is optimized and out-of-the-loop events are prevented. In a
novel proposal by MIT, the so-called AwareCar (Coughlin et al., 2009), detects, displays and
refreshes (i.e. alerting or calming) the driver to ensure that he/she is capable of optimal
performance, representing one method of addressing this issue.
Based on these theoretical approaches, this paper describes the experimental results of
a driving simulator study with a system adapted to a driver’s current workload level. The
system assists drivers in longitudinal control, increases the distance in high workload
conditions and therefore is called workload-adaptive active cruise control (WACC). If a
critical situation occurs in front of the driver, then a greater safety distance to the leading car
is maintained and more time remains to execute an appropriate reaction. Therefore, the
WACC system used in the experiment kept a minimum distance to the leading car, as long as
a high workload period was not in effect. After inducing high workload by means of a
secondary numerical memorizing task, headway to the leading car was increased (cf. figure
5). This was done using the “Wizard of Oz” method as no working workload algorithm
employing physiological data was available at the time. In this study, WACC and ACC (as
control variable) systems were evaluated in several moderate, noncritical braking situations
as well as in an emergency braking situation on the highway.
The theoretical foundation of WACC design is based on three general areas of
research: The first area investigates increased driver headway as compensatory behaviour. A
number of studies have surveyed headway increases under high workload conditions (Horrey
& Simons, 2007; Horrey, Simons, Buschmann, & Zinter, 2006). As such, WACC system
design increases driver headway in high workload situations to simulate this natural
compensatory behaviour in humans. The second area of research addresses speed-keeping
behaviour as a natural compensatory measure. Under high workload, participants reduce
speed to compensate for high workload conditions (Brookhuis et. al., 1991; Engström,
Johansson, & Östlund, 2005; Reimer et al., 2012). Based on these results, under high
workload conditions WACC reduces vehicle speed, which in turn automatically leads to an
increase in headway to the leading car. The third area of research investigates increased brake
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reaction time under high workload conditions. (Lamble et al., 1999; Jamson & Merat, 2005;
Watson & Strayer, 2010). That is, in WACC, under high workload conditions, the driver
maintains an increased headway to a leading car. This is equivalent to a higher safety gap,
which ensures that the driver has more time to make an appropriate reaction should a critical
situation occur (e.g. emergency braking of the leading car).
5.2 Material
5.2.1 Participants.
A total of 65 subjects took part in the experiment. All participants were BMW
employees, who took part in the experiment without receiving any compensation. Eighteen
subjects were excluded due to technical problems with the vital data measurement device and
simulator hardware. Therefore, data of 47 subjects were analyzed, with an age distribution
between 19 and 55 years, a mean (m) age of 28.5 years, and a standard deviation (SD) of 8.7
years. Thirty-four subjects (72.3%) were male and thirteen (27.7%) were female. All
participants possessed a valid driving license and 50% of participants had experience in a
driving simulator. Twenty-nine (61.7%) participants had no real-life experience with ACC.
5.2.2 Driving simulator.
A fixed-based custom built driving simulator with a 50-inch plasma screen, steering
wheel with control functions, and an original car seat comprised the experimental setting. A
circular highway track surrounded with familiar objects, such as trees and houses, was
designed to simulate the feeling of a real driving environment (cf. figure 5). Furthermore,
subjects drove in alternate directions to minimize recollection of the track. Moderate traffic
density in the middle and left lanes was implemented. Speed of encountered cars varied, but
in general was slightly faster (around 140 km/h) than the participant’s car. In each driving
segment, a secondary task was integrated at a certain time point to induce workload (cf. table
2).
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5.3 Measurements
5.3.1 Vital sign measurement device.
A g.USBamp biosignal amplifying device from g.tec in Vienna was used to monitor
electrocardiogram (ECG), GSR, and respiration signals. The GSR amplifier provided a skin
conductance signal (as opposed to skin resistance or skin potential). The standard GSR
electrodes from g.tec were replaced with thin gold electrodes (NeuroDyne Medical; now
Zynex NeuroDiagnostics) to minimize interference arising from the hand due to grip-related
movements on the steering wheel. The sampling rate of the physiological measurement
device was 256 Hz and data were streamed to a MATLAB Simulink Model. After
preprocessing the raw signals to calculated measurements by an encrypted Simulink Block,
data was stored on the notebook for later analysis. G.tec’s integrated filter variable fitted to
their Simulink Block established the quality of the processed ECG signal. As a result, the
frequency and occurrence of double beats, skipped beats and other ectopic beat variations
were recorded, and then eliminated. Then, to temporally synchronize both vital and simulator
data, the sampling rate in the model was decreased to 32 Hz, which matched the sampling
rate of the simulator. Furthermore, triggers were integrated according to the experimental
design for extracting all data belonging to a specific segment.
The following calculated measurements were used in the analysis: GSR change rate
calculated from raw GSR signal derived from the GSR electrodes. Although GSR change rate
is not a common measurement in this area of research, subjective analysis of GSR
measurement plots showed that GSR change rate was useful for indicating differences in
workload. GSR change rate is defined as changes in GSR signal with respect to the baseline
segment at the beginning of the measure,
GSR Change Rate = Y−Y Base
Y Base100
Y is a sliding window of 2 s lengths and Y Base is a fixed window of 20 s immediately
recorded with the start of the measurement. Further measurements used in our analysis
included HR and, as HRV measurement, root mean square of successive differences
(RMSSD) derived from ECG sensors, and respiration rate as respiration measurement derived
from the respiration belt.
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5.3.2 Subjective measurements.
In the first part of the experiment, subjects drove both systems. The first system that
drivers encountered was counterbalanced between participants. After experiencing each
system, subjects filled out the AttrakDiff (cf. Hassenzahl, Burmester, & Koller, 2003), which
interrogated the pragmatic quality of the system. After subjects were briefed on how to
operate both systems, they were asked to judge usefulness, helpfulness, comfortableness,
subjective stress, and distance to the leading car on a self-constructed 10-point semantic
differential.
5.4 The Secondary Task: N-Back Task
The n-back task, which was used as secondary task in this experiment, was originally
introduced in a visual presentation, manual response format by Kirchner and Wayne (1958).
Background on the development of the auditory presentation (i.e. verbal response form of the
task, along with an established protocol) can be downloaded from the white paper section of
the MIT Agelab (Mehler et al., 2011). In the 2-back level of the task, participants listened to
single-digit numbers (0–9 randomly presented at a rate of one every 2.25 s). In short,
participants are required to retain the most recently presented numbers in short-term memory
and verbally repeat the number presented two items back in the presented sequence, each
time a new number is presented. That is, when the initial two numbers are acoustically
presented, participants simply have to remember the numbers in order. With the presentation
of a third number, they are asked to state the first number in the sequence while keeping the
second and third numbers in short-term memory. With the presentation of each new number,
participants are required to state the number that was presented two numbers before. Four 30-
s segments with 10 numbers each were presented. Hence, one secondary task session
consisted of 2 min of continual workload induction and every 2.25 s a new number was
presented.
The 2-back task was chosen because it represented the maximum workload employed
in comparable studies (Mehler et al., 2009, 2011, 2012; Reimer, 2009, Reimer et al., 2012).
Furthermore, we selected auditory stimuli and a verbal response behaviour to induce
workload, which would be cognitively similar to a driver experiencing a demanding phone
call. This task combination has been used in several studies employing physiological data
recording, which validate the effect of a task on cognitive loading, expressed in changes of
physiological data occurring in simulator (Mehler et al., 2009) and roadway contexts (Mehler
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et al., 2011, 2012; Reimer et al., 2009). The original numbers were translated into German
and recorded as audio files to ensure the same conditions for all participants. The audio files
were played over the simulator’s loudspeakers at a volume that could be heard above road
sounds to ensure a clear understanding of the numbers.
5.5 WACC system
As described earlier, a WACC system was designed such that distance to a leading car
increased automatically from a 1- to a 2-s headway if a participant experienced high
workload conditions (cf. figure 5). The change in distance was accomplished by
implementing a modest speed reduction until the 2-s headway was reached. As the speed of
the leading vehicle remained the same, the change in headway led to a higher safety distance
to the leading car. In contrast to the WACC system, the nonadaptive ACC system maintained
the same safety distance for the duration of the task, independent of workload level.
Figure 5 Left: WACC and nonadaptive ACC system in low workload conditions. Right:
WACC system in the high workload condition
5.6 Procedure
Participants were informed that they would be driving with an ACC system capable of
maintaining speed and distance relative to a leading car, but unable to detect stationary
objects. When encountering a stationary object, participants were instructed to use the brakes.
Participants were not given any prior information regarding the actual focus of the
experiment, nor that they would be driving with different systems for purposes of eliminating
effects of previous knowledge.
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First, participants were given an overview of the experimental procedure and
answered a questionnaire concerning demographic data and previous driving experience.
Participants also learned about the possible occurrence of simulator sickness and were told
that they could stop the experiment at any time if they experienced such problems. Then,
participants were connected to the vital sign measurement device. First they learned how to
correctly place the EKG sensors and then proceeded to place them as instructed. Further, they
were informed how to place the respiration belt around their stomach and how to place GSR
electrodes on the middle and index finger of their left hand. After participants were seated in
the simulator, signals generated by the physiological sensors were checked to ensure that the
values fell in the correct range. Moreover, drivers’ freedom of movement (hands and feet)
was ensured.
After establishing participant well-being, training of the 2-back task was started. For a
better understanding of the secondary task, the 0-back task, the 1-back task and the 2-back
task were trained until every participant answered >80% of answers in all tasks correctly.
This was followed by an introductory session in which participants had an opportunity to
become accustomed to the simulator, to experience engaging in the 2-back task while driving,
and to the usage of ACC (which was also introduced in briefings before driving took place).
Further, participants were shown how a critical event would likely appear: as a large truck
located diagonally on the same lane with its hazard lights activated. As a result, the truck
blocked the driver’s lane and, because the participant was instructed not to change lanes, he
had to brake. This introductory session was done with ACC at a low speed (60 km/h) to
ensure that there would not be any accidents before the experiment started. The experiment
itself was driven at a speed of 130 km/h, which was set by the participants and controlled by
the experimenter. Headway settings were changed automatically at the start and end of the
secondary task in the WACC condition and remained fixed throughout the ACC condition.
The experiment was planned as a mixed design study. Part 1 was a within-subject
design, where every participant experienced both systems. The order of the system presented
was randomized. Participants were only informed that they had been driving different
systems, after experiencing both systems. Therefore, participants indeed experienced WACC
and ACC systems without receiving prior information about the difference between each
system. Accordingly, we investigated system awareness, the influence of system information,
and the impact of system on acceptance. Since participants in Part 2 experienced a critical
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situation under high workload conditions, whereby driver action was needed to prevent an
accident, a between-subject design was used to examine system mode. This ensured that there
would be no effects of learning. That is, participants would not react the same way after
experiencing a critical event a second time. Half of the participants experienced the critical
situation with ACC and the other half with WACC. The order of periods and a detailed
overview of the experimental procedure are outlined in table 2.
In Part 1, participants experienced four noncritical braking events (i.e. two events for
each of ACC and WACC), during which the system prevented an accident from taking place
without interference of the driver. A primary goal was to establish whether participants
developed an awareness of differences between WACC and ACC systems. Sequencing of the
systems was counterbalanced over participants. A noncritical situation was generated as
follows: a controlled braking maneuver of the leading car decelerated at a rate of –3 m/s²
from 130 km/h to 50 km/h, and then accelerated again to 130 km/h at a rate of 1.5 m/s².
Participants experienced this braking maneuver in the introductory session and, therefore,
were familiar with it. Furthermore, participants were instructed not to apply the brake
themselves because the ACC system would brake in sufficient time.
In Part 2, participants were instructed at the beginning of every session that the
systems they used could only detect moving vehicles and not stationary ones. They also
learned this in the introductory session. In the high workload situation in Part 2, a stationary
car appeared in the middle of the right lane. Then, a traffic jam occurred on the middle lane at
the same time. Since participants were required to drive on the right-hand lane at all times it
was not possible to change lanes to prevent an accident. Thus, the only way participants
could prevent an accident was to perform a braking maneuver. When the leading car changed
lanes and faded out of sight, participants could then see a stationary car with flashing hazard
lights in their lane. When the leading car changed lanes, the onset of brake reaction time was
logged. That is, at this time, participants could see the stationary vehicle and then react.
In Part 2, this braking maneuver occurred 50 s after the start of the 2-back task,
whereas in Part 1 the braking maneuvers occurred 45 s after the start of the 2-back task
period. This approach was employed to prevent unconscious adaptation to a braking
maneuver that always occurred after 45 s in the 2-back task (cf. table 2, red rows in Parts 1
and 2).
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Part PeriodDuration
(min:sec)Content
Number
of
brakes
Distance
ACC
Intro
duct
ion
Introduction to the
simulator5:00 Single task driving X Min
Driving (single
task)1:15
Reference/
separationMin
Driving (single
task)0:20 Task instructions Min
Driving + 2-back
task2:00 Four 10-item trails Min
Driving (single
task)1:30
Reference/
separationMin
Break Questionnaire
Part
1
Driving (single
task)3:00 Acclimatization Min
Driving (single
task)2:30
Reference/
separationMin
Driving (single
task) 3:15
Single time task
driving (brake at
50-s time point)
X Min
Driving (single
task)1:15
Recovery/
separationMin
Driving (single
task)0:20 Task instructions Min
Driving + 2-back
task 2:00
Four 10-item trails
(brake at 45-s time
point)
X Min/max
Driving (single
task)1:30
Recovery/
separationMin
Break + Questionnaire
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Questionnaires
Driving (single
task)3:00 Acclimatization Min
Driving (single2:30
Reference/
separationMin
task)
Driving (single
task) 3:15
Single task driving
(brake at 50-s time
point)
X Min
Driving (single
task)1:15
Recovery/
referenceMin
Driving (single
task)0:20 Task instructions Min
Driving + 2-back
task 2:00
Four 10-item trails
(brake at 45-s time
point)
X Min/max
Driving (single
task)1:30
Recovery/
separationMin
Break +
QuestionnairesQuestionnaire
Part
2
Driving (single
task)3:00 Acclimatization Min
Driving (single
task)2:30
Reference/
separationMin
Driving (single
task) 3:15
Single task driving
- ( brake at 50-s
time point)
X Min
Driving (single
task)2:00
Recovery/
separationMin
Driving (single
task)0:20 Task instruction Min
Driving + 2-back
task
0:50 Four 10-item trails
(brake at 50-s time
X Min /max
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point)
Standing + 2-back
task 1:10
Completion of 2-
back task behind
stationary car
ACC off
Driving (single
task)1:30
Recovery/
separationMin
Table 2 Description of the experimental procedure
5.7 Results
5.7.1 Technical detection of high workload situations.
Respiration rate, RMSSD, HR and GSR data collapsed across participants and
presented in absolute units are shown in figure 6. Note in particular the marked increase in
HR and GSR during the 2-back task (workload) period relative to the preceding reference
period, suggesting that the cognitive task did impact on the state of the participants. For
statistical analyses, the different physiological measures were converted to standardized
values using the formula:
X i−X reference
X reference100
where X i represents each data point and X reference represents the mean of the reference
period before the workload period. For each participant, a mean reference value was then
calculated for the ACC reference period prior to the workload period (for the reference
period, see figure 6; left from grey area) from Part 1 and compared against the mean
workload value of the ACC workload period (figure 6, grey area) of Part 1. A repeated
measurements ANOVA showed that overall physiological data are significantly affected by
workload, F(1, 46) = 73.61, p < .001. Bonferroni-adjusted post-hoc t-tests show significant
results in HR, respiration, and GSR [HR: t(46) = – 9.042, p < .001, r = .80, GSR: t(46) = –
4.99, p < .001, r = 0.59, respiration: t(46) = –3.67, p < .01, r = .34 t(46) = 1.48, ns, r = .21]
and no significant results in RMSSD [t(46) = 1.48, ns, r = .84].
In order to detect high workload periods for each participant, our results indicate HR
and GSR as predictors. RMSSD does not show significant results and therefore is not
considered in the algorithm development. Furthermore, respiration showed significant
differences, but the verbal response component of the secondary task had a confounding
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effect on respiration. Therefore, we excluded it from further analysis as the possibility of
generalization for the developed algorithm would be very limited.
Figure 6 Absolute physiological data of the ACC condition for all participants; Top, left
to right: Respiration rate, RMSSD (averaged over 60 s). Bottom, left to right: HR
(averaged over 60 s), GSR change rate.
5.7.1.1 Introduction.
Previous studies showed significant differences in physiological data between
workload and reference periods induced by the n-back task (Mehler, Reimer, & Coughlin,
2010; Reimer et al., 2009). These findings were validated in this study as described in Section
4.1. To evaluate the potential of physiological data as stable individual data for subsequent
application, we aimed to develop a detection algorithm. Here, we demonstrate the possibility
of developing an algorithm for detecting high workload periods in real-time using individual
intra-personal data, laying the foundation for the development of a workload-based system
beyond purely experimental settings.
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We use data from Part 1 to develop our algorithm in order to recognize patterns
characterizing workload, or absence of workload, and noncritical situations. To this end, we
applied a standard approach (Bishop, 2006; MacKay, 2003) from the machine learning
community to find specific patterns in the physiological measurements, exhibiting workload
or non-workload conditions. First, collected data were preprocessed. Then data were labelled
as workload or reference as stated in the experimental design (cf. table 2). Workload data
comprised two 2-min intervals, where subjects drove and responded to the 2-back task (cf.
table 2 “Driving + 2-back task” period). Non-workload data included two 2-min intervals
occurring immediately before the workload situation (cf. table 2 “Reference/Separation”
periods). This label refers to 2 min of non-workload followed by a 30-s separation interval.
This separation period was integrated to ensure a clear cut off period between these two
contradicting periods without one period affecting physiology data of the other. Thus, only
the first 2 min of data, from the 2.5-min interval labelled as reference, were used for
algorithm development.
Second, features that reflected changes in workload level were calculated. In the
machine learning community, “features” stand for derived features of measured signals.
Third, the labelled data were divided into test and training data. The algorithm was trained
based on the training data and afterwards its performance was evaluated on the test data.
As discussed in 2.3.1 an integrated filter variable classified and processed the data.
This filter variable was not working well and was not able to classify all of the RR peaks
correctly. As a result, for nondetected RR peaks, the last peak was used for calculation of
ECG measures. This problem was detected in post-processing of the data. This has now been
solved for future experiments. In this study, however, it was decided to only use the data of
these participants for development of the detection algorithm, where the filter assessed it as
correct over the whole period and no peaks were replaced with old values, as this would
confound the classification results. Therefore the data set was reduced to those 18 participants
without any data artifacts ensuring high accuracy in the raw data for further algorithm
development.
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5.7.1.2 Data preprocessing.
Data were stored as a time series: X = x1, where xn is an ordered set of n real-valued
variables. We normalized all measured signals to baseline, that is, physiological data samples
from each test participant were normalized to the mean reference value X Ref of the
corresponding subjects (cf. Wang et al., 1998). The normalized time Xnorm series is
calculated as follows:
X norm=X
X Ref∗100
where X is either time series or a calculated value from the analyzed signal. This was
done to compensate for intra-personal differences in data.
Data were split into overlapping windows as shown in the figure 7. Each window
contains 30 s of measurement data. The offset between two consecutive windows is 10 s.
This concept is also known as sliding window. The advantage of this method over
nonoverlapping windows is that a new result is obtained every 10 s instead of every 30 s.
Including multiple consecutive intervals allows for an increased confidence rate as shown
below in this section. Since our intervals are short, considering several intervals it is a still
acceptable timeframe for real-time application.
Figure 7 Sliding window concept.
2.1.1. Feature extraction
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We derived the following features from the electrocardiogram (ECG) and from the
GSR signals.
5.7.1.3 Electrocardiogram.
Figure 8 Top: ECG signal. Bottom, left to right: Power spectrum, cepstrum.
Temporal domain features of the ECG figure prominently in the literature (Clarion, et
al., 2009; Healey & Picard, 2000; Mandryk & Atkins, 2007; Mehler, Reimer, & Coughlin,
2010; Reimer et al., 2009; Wang et al., 1998). As can be seen in figure 8 (top), the ECG is a
periodic signal. Thus, it is reasonable to examine it in both temporal and frequency domains.
We first conducted standard frequency analysis on the ECG signal, where features of the
QRS complex of the raw ECG are used for identifying the time interval between adjacent R-
peaks. This may be referred to as RR intervals or the inter-beat interval (IBI). Thus, we
applied heart rate variability (HRV) analysis. Second, we performed calculations on the raw
ECG signal, known from the signal processing community to perform well on periodic
signals. These two calculations comprised power spectrum and cepstrum. The power
spectrum comparison for a workload (blue, dashed) and a reference (grey, solid) phase is
clearly depicted (figure 8, bottom left). In the workload phase, the power spectrum of the
ECG is shifted to the right, such that it achieves its maximum at a higher frequency. The
difference between workload and reference phases can also be seen in the ECG cepstrum (see
figure 8, bottom right). The analyzed frequency-based features are: (i) Power spectrum
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(frequency where the maximum power resides); (ii) Cepstrum (maximum); and (iii) HRV,
RMSSD, calculated as shown in (1), where RR is the interval between successive Rs and R is
a peak of the QRS complex of the ECG signal).
RMSSD=√ ∑n=1
NN−1
( R Rn−R Rn−1 )2
NN−1(1)
The considered time domain features are: (i) Mean beat-to-beat HR; (ii) First
difference in beat-to-beat HR, δx, calculated as shown in (2), where w is the length of the
analyzed time series and xw is a w-th measurement); (iii) Second difference of the beat-to-
beat HR, γx, calculated as shown in (3), where w is the length of the analyzed time series and
xw is a w-th measurement); (iv) Maximum beat-to-beat HR and (v) Minimum beat-to-beat
HR and (vi) difference between maximum and minimum beat-to-beat HR. Heart rate values
such as the mean, min and max can were calculated from the RR series.
δ x=1
w−1∗∑
n=1
w−1
|xn+1−xn|
(2)
γ x=1
w−2∗∑
n=1
w−2
|xn+2−xn|
(3)
5.7.1.4 Galvanic skin response (GSR).
Figure 9 left: Different average windows for the GSR signal; right: GSR (averaged over
3 s) for workload and reference.
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GSR is a nonperiodic signal in which the raw GSR signal has multiple fluctuations
(see figure 9). To mitigate this effect, an averaging operation can be used. The impact of
different averaging intervals can be seen in figure 9 (left side). Since latency is 1–3 s (Picard,
Vyzas & Healey, 2001), the average should be taken over at least 3 s, since shorter time
fluctuation might result in noise. If the averaging interval is too large, the local maximum is
shifted on the time axis to the right, which means a delay in the data evaluation. For all
subsequent discussions, the GSR signal is averaged over the last 3 s. Figure 9 (right) depicts
the difference between a workload (blue, dashed) and a reference (grey, solid) phase in the
GSR signal. To describe and quantify this difference, the following features were extracted
from the GSR signal, as well as from the calculated running rate of the GSR signal: (i) First
difference of the GSR signal, calculated as shown in (2), (ii) second difference of the GSR
signal, calculated as shown in (3), (iii) maximum of the GSR signal, (iv) minimum of the
GSR signal (not included in final feature set as it is always zero), (v) difference between
maximum and minimum of the GSR signal. The running rate is calculated as given in (4):
X run=X−X Ref
X Ref(4)
Where X is the mean of the time series and X Ref is the mean value of the previous
signal window.
5.7.1.5 Feature selection and classification.
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0102030405060708090
Single feature accuracy, percentage(%)
Figure 10 Single feature accuracies
First, we evaluated the classification accuracy of single features (cf. figure 10). These
values are calculated as percentages: the number of correctly classified examples with a
single feature divided by the total number of examples, multiplied by 100. Since it is known
that classification can be done more accurately using multiple features, multiple-feature
classification was evaluated. To this end, feature selection was done first, with the aim of
achieving an optimal feature set where every single feature contributes to greater accuracy
and where features are consistent. A forward feature selection algorithm (sequential forward
selection, SFS) was done to find the best reduced feature set of nine features (Healey &
Picard, 2000): (1) ECG: Cepstrum max; (2) ECG: frequency with max amplitude; (3)
RMSSD; (4) beat-to-beat (BB) HR: mean; (5) beat-to-beat HR: first difference; (6) beat-to-
beat HR: max; (7) beat-to-beat HR: min; (8) GSR: mean; (9) GSR: first difference.
Furthermore, two machine learning algorithms were applied with the selected
features: Decision Tree and Naive Bayes classifiers. Both machine learning methods were
implemented using MATLAB: Naive Bayes and decision tree. Naive Bayes assumes that
features are independent. But this was not the case in our classification problem, since we
extracted multiple features from two initial signals. The validation was done using 100-fold
cross-validation where the test set contained 30% of the available data (cf. Forman & Scholz,
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2010; Crowther & Cox, 2005). The accuracy of the Naive Bayes algorithm was 78.6%. The
final classification with decision tree also using multiple features (83.7% accuracy)
outperformed each single feature classifier (cf. figure 10) with maximum accuracy of 81%
and the Naive Bayes approach.
To increase confidence, multiple consecutive windows can be considered. For
example, if we take three consecutive sequences with the calculated accuracy ACC on each
sequence, the accuracy of the majority voting AccMaj is calculated as:
AccMaj=1−(1− Acc100
)3
Decision tree classification was then evaluated at 97.3%; this high accuracy was
achieved after 50 s (i.e. three consecutive sequences of 30 s with a 10-s offsets, see figure 7).
5.7.2 Security of WACC.
To interrogate safety-related aspects of WACC, subjects experienced a critical
situation to observe how they adapted to the WACC system. Risk homeostasis theory (Wilde,
1982), as applied to driver assistance systems, suggests that people will (1) adapt to a lower
critical situation with increased brake reaction time, or (2) adjust their general braking
behaviour until the situation is as critical as without the adaptation to generate a lower risk
level in the critical situation. Therefore, we decided to investigate whether participants using
the WACC system in a critical situation would adapt their behaviour according to risk
homeostasis theory. In reality, critical situations occur rarely and therefore, such a critical
situation was simulated in our experiment with low frequency.
Before the emergency braking situation was introduced, participants experienced
(W)ACC brakings five times without participant engagement, and were instructed
accordingly. That is, participants were instructed to brake only in the event of a situation
occurring outside of the limitations of the (W)ACC system (e.g. standing object). Brake
reaction time was measured from the time of lane change until the brake pedal angle was >1°.
Furthermore, negative velocity occurring after the lane change of the leading car was
calculated to estimate the smoothness of the braking maneuver. T-tests were calculated using
a between-group design, comparing one group driving with a 1-s headway (ACC) to a second
group driving with a 2-s headway (WACC). No significant results were found for brake
reaction time: t(42) = 1.039, n.s., r = .16 (ACC m = 2.09 s, SD = 0.43 s; WACC m = 2.21 s,
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SD = 0.31 s), arguing against adaptive human behaviour in brake reaction time, during
critical situations. Over the entire sample, participants reacted at the same time independent
of headway spacing and, therefore, distance to the standing object. Furthermore, deceleration
during braking with the WACC system was highly significant: t(42) = 12.85, p < .001, r = .89
and smoother (m = –210.42 mm/s², SD = 11.08 mm/s²) than with the ACC system (ACC m =
–260.35 mm/s², SD = 14.12 mm/s²), which effectively minimized the risk of rear-end crashes
and suggests that participants had more situational control using the WACC system. Both of
these results support the hypothesis that participants do not adapt to lower risk levels (i.e.
evidenced by a greater distance to a critical situation) as might be expected from
homoeostasis theory. Therefore, WACC might provide a safety advantage in critical driving
situations.
5.7.3 Acceptance of WACC.
No significant differences in AttrakDiff questionnaires were found for the two
systems after participants had experienced both systems. That is, participants did not receive
any additional information apart from the ACC instruction guidelines (e.g. ACC does not
brake for standing objects). This was implemented to ensure that participants remained
unaware that one of the systems was functionally adapted to assist in high workload
conditions. After subjects received an explanation concerning the mode of operation of both
systems (ACC and WACC), they were asked to rate usefulness, helpfulness, comfortableness,
subjective stress and distance to the leading car (cf. figure 11).
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WACCACC
Figure 11 Subjective rating after explanation of system mode
From left to right, Reasonable (U = 9.000, z = –5.667, p = 0.000, r = –.827), Helpful
(U = 0.000, z = –5.105, p = 0.000, r = –.745), Comfortable (U = 64.000, z = –4.078 p =
0.000, r = –.595), Stress Level (U = 277, z = 2.619, p = 0.009, r = .382) and Distance
Sensation (U = 22.500, z = –3.927, p = 0.000, r = –.573). Furthermore, subjects were asked
how much money they would spend on each system. Although this kind of measure is not in
line with real spending behaviour, a clear preference could be established for one system. The
difference between the WACC (m=777.84) and ACC (m = 533.41) systems was highly
significant: t(43) = 4.337, p = 0.000, r = .552, which suggests that WACC is preferable to
ACC. The finding that no significant differences in pragmatic system quality were found
before the explanation and that preference for one of the systems was observed after the
explanation, is best explained by system awareness, which was the only changed variable.
Subjects were asked if they noticed any changes in either system before they received further
information on both systems. Only 7 subjects (14.9%) from a total of 47 participants realized
any change in distance. Furthermore, 6 subjects thought that the system would brake earlier
or that the drive was generally more comfortable. One subject recognized a change in the
ACC status display, one subject thought the ACC icon was blinking, and one subject thought
the WACC behaved more aggressively than the ACC system.
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5.8 Discussion and conclusion
Mental workload, also called mental effort, is the sum of the costs of cognitive
processing and has been found, under various conditions, to be reflected by several
physiological measurements, such as HR, HRV, respiration, and GSR (Brookhuis & de
Waard, 2010; de Waard, 1996; Mayser et al., 2003; Mehler et al., 2012; Veltman & Gaillard,
1998). Workload can be examined through questionnaires, performing secondary tasks and
measuring physiological parameters, which are the least invasive. The Rheinisch-
Westfaelische Technische Hochschule (RWTH University) Aachen evaluated the effects of
integrated capacitive ECG electrodes located in seats (Wartzek et al., 2011). Although the
detection rate of this measure was not flawless, it shows that measuring HR and HRV –
without wearing sensors on the skin – could become a reality in the near future. BMW also is
working on measuring GSR at the steering wheel (D'Angelo, Parlow, Spiessl, Hoch, & Lueth,
2011). Alternatively, sensors incorporated in clothing are already available on the market.
This development shows that non- or low-interference measurement of vital data could
become possible in the future. Thus, our research focuses on the development of a workload
algorithm founded on validated vital measurement equipment. Further, we aimed to establish
the effects of workload, as an optional parameter, in the calculation of ACC headway. To this
end, three underlying research questions were addressed.
(a) The first question examined the technical possibility of workload detection based
on physiological data. Our results showed highly significant differences in physiological data
between low workload, single task driving segments and objectively higher workload, dual-
task segments when participants engaged in a 2-back working memory task. A programmed
decision tree algorithm correctly identified these high workload periods in 83.7z% of all
instances for the 18 subjects for whom usable physiological data was available. This supports
the conclusion that workload detection based on physiological data should be possible if the
right physiological sensors deliver data at a sufficient quality. However, it should be noted
that the high percentage of workload detection found using the algorithm was based on a
known reference period in the car. In other words, real-time detection on the street requires
that a reference session be identified and that reference data be collected. A further critical
point influencing the use of physiological data is the implementation of in-vehicle sensors or
sensors worn on the body. Both methods could be used for collecting physiological data in
the future, but currently, these methods are very artifact-sensitive to movements of subjects
and surrounding electronic devices. Furthermore, implementing these sensors in vehicles
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must be made more affordable. Future development in this sector will determine if this kind
of continuous detection is technically and financially possible, that is, feasible for serial
production.
(b) The second research question asked if such an ADAS increases safety. Our results
suggest that participants capitalized on the possibility of maintaining a higher distance to a
leading car. They decelerated slowly, reducing the risk of rear-end collisions. Furthermore
our study showed no delay in brake reaction time under higher headway conditions. Thus,
participants also decreased their risk of front-end collisions. The experiment was designed to
simulate real-life conditions with an ACC system. Accordingly, five noncritical braking
maneuvers were designed to enhance trust in the system and a critical braking event occurred
once, and therefore was a rare event. Nevertheless, the experimental results demonstrate
participants’ first encounter with such a WACC system and does not reveal participants’
adaptation to such a system over longer periods of time. It is possible that they begin to trust
the system more because it has compensated for their behaviour previously, and they then
start to act according to the risk homeostasis theory.
(c) The third question concerned the acceptance of such a new ADAS. As most
participants did not report a change in the system related to distance to the leading car, no
significant differences were found between the two systems before receiving a system
explanation. After receiving an unbiased explanation, people preferred the WACC system
over the ACC system in each of the questioned items. The most interesting result was that
people were not aware of the adaptation and did not notice that the system increased safety.
This is precisely how an ADAS should support drivers.
Subjective data showed very promising results concerning safety and acceptance of
the WACC system. Nevertheless, further research should examine whether participants’ vital
data measurements in the simulator reveal the same pattern of results in reality. If we detect a
high workload situation in the simulator, but fail to adapt and apply these results in real-life
settings, then this simulator-specific artifact would have no research value. The MIT AgeLab
has shown that physiological data changes follow a similar pattern in simulated and real-life
vehicular settings (cf. Reimer & Mehler, 2011). These results suggest that the developed
workload algorithm could also be applied (with minor changes) in on the road settings, but
this assumption remains to be validated under real traffic conditions.
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Acknowledgments
We thank Ralf Decke and Bernhard Niedermaier at BMW Group Research and
Technology GmbH for organizational and thematic input, as well as for help solving both
minor and major problems. Furthermore, we are grateful to Bryan Reimer and Bruce Mehler
from the MIT AgeLab for sharing valuable insights and for eye-opening discussions.
This research received funding from the European Community's Seventh Framework
Programme (FP7/2007-2013) under grant agreement n°238833/ ADAPTATION project.
www.adaptation-itn.eu.
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6 On-Road Study Of The Simulated WACC7
6.1 Introduction and Objectives
Two experiments interrogated the foundations of workload-adaptive cruise control
(WACC): In the first experiment, the relationship between physiology [especially heart rate
(HR) and heart rate variability (HRV)], workload level and brake reaction time was the main
focus. In the second experiment, the hypotheses concentrated on system awareness, accep-
tance and the possibility of the development of a workload algorithm. Although results were
promising, it should be noted that the first two experiments were simulator experiments, and
not reality-based.
Therefore the next experiment investigated the implementation of WACC in a real car on a
highway, focusing on system awareness, trust and acceptance. Furthermore, WACC was only
simulated using the Wizard of Oz method and physiological data were collected for the devel-
opment of a workload algorithm.
6.2 Method
6.2.1 Participants.8
All participants were BMW employees and received no compensation for
participation in the experiment. A total of 38 BMW employees took part in the experiment.
Eight participants were excluded: 2 participants had no experience with ACC or were already
familiar with the WACC system; 1 participant was excluded due to technical reasons; and 5
answered <80% of the 2-back tasks correctly and therefore mental workload could not be
decisively established. Twenty-nine participants were male and 1 participant was female,
between 24 and 58 years old with a mean age of 35.4 years and SD = 10.33 years. All
participants had experience with a regular ACC system.
7 Currently, no publication concerning the diploma thesis of Bellem (2013), which was supervised by the author of this thesis, is available. A summary of details (not a complete report), which are important to an understanding of this dissertation, is given. Footnotes corresponding to pages of the diploma thesis are provided throughout the chapter. The revised summary presented herein is part of the paper in preparation by Hajek, W., Bellem, H., Trzuskowsky A., & Krems, J. (n.d.) entitled “Workload-adaptive cruise control – The development of a driver assistance system of the future”. This paper will be sent to Transportation and Research Part F, for peer review.8 cf. Bellem, 2013, p.22
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6.2.2 Design.9
A one-factorial within-subject design was chosen with the factor information, consist-
ing of two levels: with/without information.
The first hypothesis stated that in reality, more people notice a change in distance un-
der high workload conditions (which was not the case for most people in the simulator). A
question concerning system awareness was chosen as dependent variable.
The second hypothesis comprised hypotheses 2a and 2b. Hypothesis 2a concerns all
people who indeed realize a change in distance. This hypothesis states that acceptance and
trust decrease as long as subjects are uninformed, as they may suspect a system malfunction.
Hypothesis 2b considers people who do not notice a change in distance. Accordingly, accep-
tance of these subjects is not expected to change.
The third hypothesis posted an overall increase of acceptance for all participants, after
they obtain further information concerning system mode. Those who do not notice a change
in distance will likely appreciate a system that raises safety without the awareness of the
driver, and those who are confused will understand reasons for the system’s behaviour.
The fourth hypothesis states that participants generally prefer WACC over ACC.
6.2.3 WACC.10
In this study WACC was simulated using the Wizard of Oz method. WACC was
integrated in a BMW 5 series vehicle in place of the usual ACC system. Every time workload
was implemented by starting the secondary task, the distance to the leading car was
increased. As soon as the secondary task ended the distance was decreased to minimum
headway, that is, the original setting. In the experiment standard headway was 1 s. With a
delay of 1 s after the start of the 2-back task, the headway was increased to 2-s headway. The
reason for the delay is that participants would not experience the same workload with the
start of the audio file as they would during the subsequent duration of the playback. The
9 cf. Bellem, 2013, p.20-2110 cf. Bellem, 2013, p.23-24
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change in distance was not only carried out but also visible in the instrumental display area
where the icon of the ACC changed from a one-bar to three-bar distance.
6.2.4 Car.11
A BMW 530d was used as an experimental vehicle and the ACC system was
connected to a notebook, running MATLAB. Thus, the notebook communicated with the
ACC system and altered headway distance with the start of the audio file.
6.2.5 Route.12
An 18.8-km stretch of the A8 highway from Munich to Stuttgart (exit Sulzemoos) was
used as experimental track. One direction (Munich to Sulzemoos) was driven uninformed and
the other direction (Sulzemoos to Munich) was driven informed. Informed meant that
participants were provided with information that the ACC they used had an extended
workload component, and therefore, they were driving with a WACC system. The
experimental highway section was characterized by medium traffic density and therefore
consistently provided the opportunity to create a car-following scenario on the right lane of
the highway. Specific road signs were used as starting cues for the 2-back task to ensure the
same timing over all participants.
6.2.6 Questionnaires.13
Three questionnaires were handed to the participants out at three times: (1) Before the
experiment, consisting of demographic questions, control variables (sensation seeking and
affinity towards technology), ACC acceptance and trust for the system (from prior experi-
ences). (2) In the middle of the experiment after experiencing the WACC system but before
participants received information that they were driving in a system different than a normal
ACC system. In this case, the same acceptance questionnaire as with the ACC system was
presented. (3) At the end of the experiment after participants were given further information
concerning the WACC system and after they had experienced the system. WACC acceptance,
trust and a direct comparison scale with ACC were presented.
Acceptance was tested with a self-constructed 10-point scale for an overall rating and
acceptance scale by van der Laan, Heino and de Waard (1997) for a more detailed rating.
11 cf. Bellem, 2013, p.2412 cf. Bellem, 2013, p.2513 cf. Bellem, 2013, p.24-25
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Sensation seeking was measured by the Sensations Seeking Scale Form V (Zuckermann,
Eysenck, & Eysenck, 1978) with the subscales Thrill and Adventure Seeking and Boredom
Susceptibility. Affinity towards technology was measured by the questionnaire
Technikaffinität (Karrer, Glaser, Clemens, & Bruder, 2009). Trust was measured with the
trust in automated system scale (Jian, Bisantz, Drury, & Llinas, 2000).
6.2.7 Procedure.14
Participants were first asked to fill out questionnaires on demographics, control,
driving experience, ACC experience, trust and acceptance. Afterwards general instructions
concerning the driving task were given, without informing participants that the ACC system
was changed to a WACC system. This was followed by an introduction of the 2-back version
of the n-back task (cf. experimental description of Experiments 1 and 2). After training was
successfully completed, further information concerning the car, safety issues and the ACC
system was given. Participants were instructed only to participate in the driving task if they
felt completely secure. In the 15-min drive to the highway, participants had the possibility to
refresh their usage of the ACC system. As only participants with ACC experience were
chosen for this experiment, this short introductory drive was sufficient to become familiar
with the car. Participants were told to turn on the WACC, which operated like a normal ACC.
The only difference between ACC and WACC was the automatic change of headway, and
thus, participants could not adjust distance themselves.
Once drivers reached the experimental section of the highway, participants were
instructed to follow another vehicle using WACC. In addition they were instructed to
participate in the 2-back task when it occurred and when they felt safe to do so. After 18.80
km, participants took the exit to Sulzemoos and were directed to a small parking lot, where
they filled out the second questionnaire (acceptance) and took a short break. At this point in
time participants were not informed of the adaptive behaviour of the WACC with which they
had driven. Therefore, a short interview was also done to gain further insight into system
awareness in drivers without information. In particular, questions identified whether
participants noticed the adaptive behaviour of the WACC system, and if they attributed it to
added workload or if participants perceived it as a malfunction of the system.
After the interview, participants were informed about the WACC but not that the
system was simulated as a Wizard of Oz solution. A Wizard of Oz solution is a way of 14 cf. Bellem, 2013, p.27-29
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implementing a system in such a way that it reacts like the real system, without having the
foundation, which would be necessary to build the system in reality. Therefore it mainly is a
workaround to find out, if the function of a system is worth to be developed.
Our way to conduct the experiment, that is keeping information from the participant,
created the risk that participants would try to activate the adaptive behaviour of the WACC
outside from the 2-back task segment. As it was a simulation, any action apart from the 2-
back task would not lead to a change in headway and this issue could therefore undermine the
acceptance rating. Therefore participants were purposefully instructed to avoid engaging in
any other workload-inducing behaviour apart from driving and solving the 2-back task.
At the end of the experiment, participants completed the third questionnaire
concerning acceptance and trust. Another short interview was done to gather information
about how participants perceived the system and what they saw as advantages or
disadvantages of such a system.
6.3 Results15
In total, 17 (56.67%) participants did not notice any change in headway compared to
13 (43.33%) participants who did. In the latter group, only 6 individuals attributed this
headway change to an increase in workload. According to these findings and the implications
to the acceptance ratings, the overall sample is divided into the following subgroups for
further analyses:
1. Not noticed group consisting (nn) of 17 participants (56.67%)
2. Noticed incorrectly (ni) group consisting of 7 participants (23.33%)
3. Noticed correctly (nc) group consisting of 6 participants (20%)
6.3.1 Control variables.16
6.3.1.1 Sensation seeking.17
Overall reliability analysis of the sensation seeking test shows low values for
Boredom Susceptibility (Cronbach’s α=.523) and medium values for Thrill and Adventure
Seeking, which is in line with literature (Zuckermann et al., 1978; Beauducel, Strobel, &
15 cf. Bellem, 2013,p.28-2916 cf. Bellem, 2013, p.29-3017 cf. Bellem, 2013, p.29-30
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Brocke, 2003). The Kolmogorov–Smirnov test showed significant values (from p <.001 to p
= .200) and therefore indicated violation of a normal distribution, which supports using
nonparametric tests. The subscale Boredom Susceptibility (nn: m = 7.06, SD = 2.19; ni: m =
6.14, SD = 1.07; nc: m = 9.17, SD = 2.04) showed no significant results [H(2) = 0.05, p
= .976)] whereas the subscale Thrill and Adventure Seeking (nn: m = 4.00, SD = 1.70; ni: m
= 4.57, SD = 3.16; nc: m = 3.36, SD = 1.75) showed a significant effect between the three
groups [H(2) = 7.52, p = .023]. This effect was not found in Bonferroni-adjusted post-hoc
tests (adjusted significance level: p = .017; group nc vs ni: U = 39.50, z = –1.28, p = .209, r =
–0.23; group nn vs nc: U = 84.00, z = 2.36, p = .020, r = 0.43; ni vs. nc: U = 36.00, z = 2.23,
p = 035, r = 0.41).
6.3.1.2 Affinity towards technology.18
The subscales Competence in Handling Technological Devices (nn: m = 4.38, SD =
0.69; ni: m = 4.32, SD = 0.57; nc: m = 4.63, SD = 0.38; Cronbach’s α = .825), Enthusiasm for
Technology (nn: m = 3.85, SD = 0.78; ni: m = 3.80, SD = 0.81; nc: m = 4.00, SD = 0.84;
Cronbach’s α = .813) and Negative Effects of Technology (nn: m = 3.65; SD = 0.76; ni: m =
3.40, SD = 0.37; nc: m = 4.03, SD = 0.34; Cronbach’s α = .789) showed medium to high
reliability results whereas the subscale Positive Effects of Technology (nn: m = 3.89, SD =
0.47; ni: m = 4.06, SD = 0.36; nc: m = 4.00, SD = 0.47; Cronbach’s α = .651) showed low
and therefore questionable results. Because results of the Kolmogorov–Smirnov test departed
from a normal distribution, the Kruskal Wallis test was used for significance tests. No
significant differences were found for Enthusiasm for Technology [H(2) = 0.33, p = .0847],
Competence in Handling Technological Devices [H(2) = 0.96, p = .618], Positive Effects of
Technology [H(2) = 0.84, p = .657] and Negative Effects of Technology [H(2) = 5.02, p
= .081].
Altogether, no significant differences in control variables could be found in control
variables and participants showed medium to high scores for affinity towards technology and
sensation seeking variables.
.
18 cf. Bellem, 2013, p.30-31
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6.3.2 System awareness.19
A total of 17 participants did not notice a change in distance, whereas 13 participants
did realize a change in distance (group nc and group ni). Chi² goodness of fit tests were
conducted for different distributions. No significant differences for an equal distribution
[χ²(1) = 0.53, p = .465, Φ = 0.02] and 75% not noticed and 25% noticed distributions [χ²(1) =
5.51, p = .064, Φ = 0.43] were found. The distributions 25% not noticed and 75% noticed
[χ²(1) = 16.04, p < .001, Φ = 0.73] show significant results and are therefore rejected. This
reveals a tendency of more participants to not notice WACC.
6.3.3 Acceptance.20
6.3.3.1 Ten-point acceptance scale.21
Because of the low number of participants in the subgroups and mixed results of the
Kolmogorov–Smirnov test, nonparametric tests were chosen. Overall three groups (nn, nc, ni)
a significant increase in trust were found between before the experiment (t0) and after the
experiment (t2) with Mann-Withney test (z=2.32, p=.021, r=0.30), which seems to be
connected to the not noticed group (nn: Z = –2.58, p = .010, r = –0.44; ni: z = 0.00, p = 1.000,
r = 0.00; nc: z = -0.69, p = .492, r = –0.20).
For the group not noticed, between t0 and before participants obtained information
(t1) significant increases can be found (z = –2.87, p = .004, r = –0.51) in contrast to
nonsignificant results between t1 and t2 for this group (z = 0.38, p = .705, r = –0.07).
Friedman’s test showed no significant changes for the groups noticed incorrectly and noticed
correctly regardless of whether they were analyzed together [χ²(2) = 1.56, p = .458] or
separately [ni: χ²(2) = 0.50, p = .779; nc: χ²(2) = 2.38, p = .305].
Further tests concentrated on effects between groups. No significant change could be
found at t0 [H(2) = 1.06, p = .588] whereas a significant effect was found at t1 [H(2) = 8.41,
p = .015]. A Bonferroni-corrected (p = .017) test attributes this effect to a significant
difference between the groups not noticed and noticed incorrectly (U = 19.50, z = -2.72, p
= .007) whereas no differences were found for other group combinations (nn vs nc: U =
32.50, z = -1.39, p = .166; ni vs nc: U = 24.50, z = 0.52, p = .604). A significant overall effect
was found at t2 [H(2)=6.99, p=.030] which nevertheless does not persist at the Bonferroni-
19 cf. Bellem, 2013, p.3120 cf. Bellem, 2013, p.31-3621 cf. Bellem, 2013, p.31-33
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corrected post-hoc tests (nn vs ni: U = 30.00, z = –2.07, p = .039; nn vs nc: U = 28.50, z = –
1.69, p=–0.92; ni vs nc: U = 19,00, z = –0.30, p = .757).
The results for acceptance can be summarized as showing a significant increase for
the group not noticed for the first questionnaire compared with the second and third
questionnaires. Furthermore over all groups WACC is better rated than ACC which possible
can be attributed to the group not noticed.
6.3.3.2 Van der Laan (van der Laan et al. 1997).22
A reliability analysis provided low to medium results for Usefulness (.539 ≤
Cronbach’s α ≥ .739) and medium to good results for Satisfying (.631 ≤ Cronbach’s α
≥ .843). In-depth normality analysis conducted with Kolmogorov–Smirnov test violates the
normal distribution for the Satisfying (.028 ≥ p ≥ .014) and the Usefulness rating of the
noticed incorrectly group. Therefore nonparametric tests are conducted for further analysis.
In general all mean values lied above the scale’s median. As can be seen in table 3 no
significant effects in the subgroups over all three measurement points could be found.
t0 – t1 T0 – t2 T1 – t2
Subscale Group T z P T z p T Z p
22 cf. Bellem, 2013, p.33-34
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Usefulness nn 28.50 0.10 .918 31.50 0.41 .679 35.00 0.18 .855
ni 8.00 0.14 .891 4.50 -0.82 .414 7.50 -0.65 .518
nc 9.00 1.47 .141 11.50 0.21 .833 4.00 -0.96 .336
Satisfying nn 46.00 0.57 .570 38.50 0.49 .622 30.00 -0.27 .786
ni 6.00 -0.41 .680 4.00 -1.38 .168 2.50 -0.92 .357
nc 0.00 -1.00 .317 2.00 -0.54 .593 3.00 0.00 1.00
Table 3 Bellem H. (2013). An on-road study of system awareness, acceptance, and trust of
a simulated workload-adaptive cruise control. Unpublished master’s thesis, Technical
University of Chemnitz, Chemnitz, Germany
6.3.3.3 ACC – WACC Direct Comparison.23
Two of the groups evaluated with Kolmogorov–Smirnov tests violated a normal
distribution (nn: p < .001; nc: p = .036). Thus, nonparametric tests were used. No significant
differences were found at the two test points (t0 and t2) over all three groups [t0: H(2) = 0.26,
p = .880; t2: H(2) = 3.56, p = .169] tested with Kruskal Wallis test. A significant effect could
be found when testing both systems (ACC – WACC) against the scale’s media of 4 favoring
WACC (z = 4.193, p < .001, r = 9.54). A detailed analysis showed this effect could be
associated with the group not noticed, which showed the only significant effect of all groups
(nn: z = 3.76, p < .001; ni: z = 1.73, p = .084; nc: z = 1.28, p = .202).
In sum, only the 10-point acceptance scale showed significant effects: In a direct
comparison, WACC was rated better than ACC. Furthermore the group not noticed showed a
significant effect for t0 to t1 and t0 to t2.
6.3.3.4 Trust.24
Reliability analysis showed high results (t0: Cronbach’s α = .857; t2: Cronbach’s α
= .901) and Kolmogorov–Smirnoff tests revealed violations of a normal distribution prior to
the trip (nn: p < .001; ni: p < .001) as well as after the trip (nn: p = .005). Therefore
nonparametric tests were used.
23 cf. Bellem, 2013, p.34-3624 cf. Bellem, 2013, p.36-37
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The mean values lie above the scale’s median of 4 and no significant effects could be
found between the two test points (nn: z = –1.38, p = .169, r = –0.24; ni: z = –1.09, p = .276, r
= –0.29; nc: z = –0.85, p = .396, r = –0.25) or the group against each other [t0: H(2) = 0.26, p
=.880; t2: H(2) = 3.56, p = .169]. In Summary there are no significant effects in trust.
6.4 Discussion and conclusion
We examined system awareness, trust and acceptance of the WACC system both in
general and in detail, compared to an existing ACC system. Therefore only participants with
experience using ACC were considered in our sample.
The results are interpreted in the context of the generated hypothesis. Concerning the
first hypothesis (system awareness), we found that in the on-road experiment 56.67% did not
realize a change in distance whereas the percentage in the simulator experiment was 85.1%
(cf. chapter Workload-Adaptive Cruise Control). Furthermore 20% realized the reason for the
change in distance compared with 23.33% who did not understand what was happening or at-
tributed the change in distance to a “wrong” reason. Even if statistical analyses indicate that
more participants do not realize a change in distance, results show that there is a higher per-
centage of people who realize a change in distance in reality compared with the simulator set-
ting. The reason for this likely lies in acoustic experience of motor sounds as well as sensa-
tions experienced when a car decelerates and accelerates in reality. It is postulated that if
changes in distance were more subtle, the number of people who did not realize a change in
distance would increase. This is, however, beyond the scope of this research. Nevertheless
from a security perspective, one has to assume that at this point of the research, a (low) per-
centage of people will always notice a change in distance and attribute this to an incorrect
cause (e.g. system failure). Therefore appropriate measures should be taken if such a system
is implemented for future cars (e.g. provide an explanation for system behaviour).
The second hypothesis addresses acceptance and trust separately for people who do
realize (group noticed) and who do not realize (group not noticed and group noticed incor-
rectly) the change in distance. Concerning this hypothesis, significant increases in acceptance
were only found for those who did not realize a change in distance on a 10-point acceptance
scale. This increase was consistent over both time points of the following measurements com-
pared with the initial measurement. No significant increases could be found in trust for any of
the groups. In general, these results support designing a system in such a way that its behav-
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iour will not be noticed by the drivers. As stated, few and subtle changes could make this pos-
sible.
The reason that the difference could only be found on a 10-point acceptance scale
could lie in the fact that the 10-point scale allows a more detailed assessment of acceptance
than the 6-point van der Laan scale. Furthermore one should keep in mind that ratings are
generally higher than the scale’s median and therefore point to overall high acceptance.
The third hypothesis proposed an overall increase in acceptance for all participants.
This hypothesis could not be confirmed by the presented data. Results concerning acceptance
show stability after the first measurement for all groups, apart from group not noticed (which
shows a significant increase between the first and the second and the first and the third time
points measured). Nevertheless no decrease in acceptance, which would lead to refusal of the
system, was found. In general this finding also seems to support the design of an unnoticeable
system. Furthermore it suggests that providing information does not harm system usage but
also does not increase its acceptance.
The fourth hypothesis is about the general preference of WACC over ACC. Our find-
ings suggest a preference for WACC on the 10-point acceptance scale but not on the 6-point
van der Laan scale. Again, as acceptance results are generally high, we argue that differentia-
tion in the 6-point scale is too low to detect the subtle changes, and thus preference. The re-
sults for trust are unexpected and do not support our hypothesis. No increases or decreases in
trust could be due to the fact that first, mean values all lie above the median, which support
trust in the system, and second, other confounding factors may influence the trust rating,
which are independent from changes due to the WACC (e.g. as people were experienced with
WACC they could have already experienced system malfunctioning).
In sum, our results concerning the use of WACC in on-road conditions are
encouraging. In general a security-related advantage have been established concerning the
WACC results in the simulator experiment (cf. chapter Workload-Adaptive Cruise Control).
Furthermore acceptance of WACC increased rather than decreased compared with ACC in
on-road conditions. Moreover, ready-to-use WACC should be designed in such a way that the
changes are more subtle. This should be a topic of experiments in future research.
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7 Online detection of workload in an on-road setting25
7.1 Introduction and objectives
The foundations of WACC acceptance were evaluated in the first three experiments.
First, the relationship between physiology, workload and brake reaction time was established
and second acceptance and system awareness were tested in a simulator as well as in on-road
conditions. Promising results in these experiments raise the question whether a working
workload algorithm can be programmed, and used for application in WACC distance
changes, in reality. Therefore, this last experiment attempts to validate a workload algorithm
designed using data of previous experiments.25 Currently, no publication concerning the diploma thesis of Trzuskowsky (2012), which was supervised by the author of this thesis, is available. A German to English-translated summary of details (not a complete report) which are important for understanding of this dissertation is given. Footnotes corresponding to pages of the diploma thesis are provided throughout the chapter. The revised summary presented herein is part of the paper in preparation by Hajek, W., Bellem, H., Trzuskowsky A., & Krems, J. (n.d.) entitled “Workload-adaptive cruise control – The development of a driver assistance system of the future”. This paper will be sent to Transportation and Research Part F, for peer review.
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Some data (cf. chapter preliminary algorithm development) is not discussed in the
diploma thesis of Trzuskowsky (2013), but is provided within this thesis for a better
understanding of the whole process of algorithm development. New graphics for a better
visualization of results and new thoughts on the meaning of these results are provided.
General thoughts that are important for the central concept and details of the WACC are
interwoven in the presented summary.
7.2 Method
7.2.1 Design.26
In the last experiment, physiological data was logged and a preliminary detection
algorithm was developed. The results, especially for false-positives, were not satisfying.
(This will be presented within the results section of this experiment, because of a better
thematic connection) Physiological data were not linked specifically to the occurrence of the
2-back task but were linked to the occurrence of higher demand in general, which induces
higher workload. Therefore a more detailed analysis of other workload factors was necessary
in this experiment:
The factor workload was established with three levels: “No-workload” represented a
period without workload, “Workload” represented a workload-induced period by the
secondary track and “extra trigger” represented periods during which there could be a
workload-induced period because of external influences (e.g. taking over, seeing police on
the side of the street). The hypothesis was that the detection algorithm would be able to detect
>70% of the workload-classified periods. The overall detection rate was used as dependent
variable.
7.2.2 Participants.27
All participants were BMW employees and were not paid for participation in this
experiment. Altogether 10 participants comprised the sample for the validation study.
Participant ages ranged from 25 to 54 years with a mean age of 38.6 years and SD = 10.59
years, 7 participants took part in Experiment 2, and all participants were informed about the
WACC. During the experiment, due to a time-limited hardware defect, the (W)ACC distance
was not displayed correctly for approximately 15 s for each participant.
26 cf. Trzuskowsky, 2012, p.38-3927 cf. Trzuskowsky, 2012, p.38
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7.2.3 Procedure and equipment.28
The procedure and equipment used were the same as in Experiment 3 and will
therefore not be explained in further detail. To obtain additional workload phases for
validation, the track was extended to the next exit. Therefore, instead of two workload
phases, three workload phases were generated. The validation was calculated from the three
workload periods measured in only one direction during this experiment.
7.2.4 Methodical background.29
Four measurements were used as indicators for the evaluation of the workload
algorithm:
TN = true negative
where TN indicates the percentage of true negative-classified data from the algorithm,
that is, the percentage of the overall time of the measurement when there was no-workload
and the algorithm confirmed this.
TP = true positive
where TP indicates the percentage of true positive-classified data from the algorithm,
that is, the percentage of overall time of the measurement when there was workload and the
algorithm confirmed this.
FN = false negative
where FN indicates the percentage of false negative-classified data from the
algorithm, that is, percentage of the overall time of the measurement when there was
workload and the algorithm declined this.
FP = false positive
where FP indicates the percentage of false positive-classified data from the algorithm,
that is, percentage of the overall time of the measurement when there was no-workload and
the algorithm indicated that the driver is under workload.
R = classification rate
where R indicates how much of the data is correctly classified and is calculated with
the following formula:
28 cf. Trzuskowsky, 2012, p.3829 cf. Trzuskowsky, 2012, p.4-5
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r=Number of correct classificationsNumber of all classifications
x100%
7.3 Results30
7.3.1 Preliminary algorithm development.
Physiological data were recorded in the last experiment to create the first detection
algorithm. The classification rates are shown in table 4
No-workload TN: 66.23% FP: 33.77%
Workload FN: 16.63% TP: 83.37%
Table 4 Algorithm classification results of the preliminary experiment
Classification rates of the preliminary experiment were too low to be considered
feasible ― even when false-negative classifications, in part, could be explained due to design
issues, and latency periods due to experimental design (cf. chapter Refined algorithm
development). False-positive detection could not be explained with this concept. As the on-
road experiment was conducted on a real highway situation with all of the influencing factors
of a real world, it stood to reason that there were other confounding factors influencing the
experiment and which could not be restricted due to the design of the experiment. This factor
was considered in future algorithm development.
7.3.2 Refined algorithm development.31
The algorithm presented here is based on physiological data collected in this
experiment as well as on experiences of the preceding experiment (cf. Preliminary algorithm
development).
When playback duration of the n-back task is viewed as the workload-inducing
period, then the classification rate based on this data set is r = 72.10% calculated from
classification measurements TN =70.21%, FN = 20.86%, FP = 29.79% and TP = 79.14%.
This is a very conservative approach as the first number of the n-back task is played after 3 s
and the second number after 6 s (cf. Trzuskowsky, 2012, p.38). Therefore three factors led to 30 cf. Trzuskowsky, 2012, p.38-4031 cf. Trzuskowsky, 2012, p.38-40
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a high false-negative rate: (1) our workload task did not induce workload from the first
second. High workload was only reached (due to the design of the secondary task) after the
third number when participants had to keep two numbers in his/her short memory storage (i.e.
a latency time of minimum 9 s). With this assumption 15% of workload periods would be no-
workload periods; (2) the human body reacts with latency in expressing physiological
changes according to workload; (3) an in-depth analysis showed that in general, all but one
workload period was identified (cf. figure 12). A large portion of the false-negative
percentage derives from this one unidentified workload period. Furthermore this means that
in general, most of the workload periods were detected even if only part of the workload
period was classified correctly.
Figure 12 Left: visualized percentage of classification rates in absolute time without
logic. Right: relation between detected and nondetected workload (WL) periods.
In this, and the preceding study, short false-positive detections of 1-2 s occurred in the
no-workload periods. To eliminate this problem, logic was implemented which delayed the
detection of workload. This means that workload is only classified as workload after a certain
time range of continuous workload detection. Furthermore, within the workload periods the
algorithm showed short periods of false-negative classifications. Therefore the logic was
expanded to switch from a workload to a no-workload period only after several continuous
seconds of no-workload detections. This behaviour is in line with the usage of workload for
WACC. WACC has been created to compensate for longer high workload periods and not for
short peaks of workload. To implement this logic a counter was applied before the workload
classification was done ensuring a 0.626-s delay and 1.25-s hold period before classifying
workload and before indicating a no-workload period. Results for this setting led to an
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improvement in classification rate with r = 81.37% based on TN = 86.93%, FN = 39.27%, FP
= 13.07% and TP = 60.73%. Through this logic implementation the false-positive workload
detection rate was reduced to half of the first rate, from 29.79% to 13.07%. Conversely, the
false-negative detection rate also nearly doubled from 20.86% to 39.27%. Nevertheless the
overall classification rate was improved from 79.14% to 81.37%.
Apart from this the approach presented herein shows that indeed design possibilities
exist to adapt the algorithm according to the later usage (e.g. one could accept a later
detection of workload to minimize wrong alarms. In this case one would have to accept more
false-negative detections over the duration of the whole classification).
The high false-positive alarms can be explained by another approach. The detection
rates described here are based on an optimal experimental setting and workload is only
induced in the 2-back task period. However, mental workload, which leads to changes in
physiological data, could be induced not only by the 2-back task, but also by other street
events. Because of experiences in the Wizard of Oz study, different events were classified as
confounding factors or workload-inducing events: Change of lanes, experimenter
instructions, leading vehicle changing lanes, new leading vehicle, advancing vehicle, reeving
vehicle, participant questions, overtaking maneuvers, rain, coughing. These events were
logged by the experimenter and were later implemented in the data analyses as extra triggers
(cf. figure 13).
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Figure 13 Exemplary visualization of the influence of confounding factors (trigger in
this text is referred to as extra trigger)
Therefore, of all workload detections, 52.56% could be explained by the sound trigger
(that is 2-back task), 26.97% could be explained by the extra trigger, based on the described
events 20.47% could not be explained. Thus, more than half (56.85%) of false-positive
detections could be explained through the extra trigger (cf. figure 14). If these situations are
classified as correctly identified workload periods, than the false-positive rate is reduced to
7.43% and therefore the true negative rate increases to 92.57% leading to an overall detection
rate of r = 85.82%.
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Figure 14 Left: visualized percentage of classification rates in absolute time with logic.
Right: visualized proportion of extra trigger and not explainable percentage
7.4 Discussion and conclusion
In the first experiment the physiological foundation between vital data and the 2-back
task were researched. A first simulator experiment was conducted to evaluate the final
situation with a Wizard of Oz method. Preliminary analysis showed a general possibility for
statistical detection of high workload periods. Nevertheless, this analysis was done afterwards
and in a static way with respect to the whole workload period. This approach cannot be used
for real-life application. Furthermore the second and the third experiments showed a general
high acceptance and a security advantage. Therefore this experiment was done with respect to
general feasibility in on-road conditions.
As mentioned in the previous experiment, algorithm development showed promising
but not overall satisfying classification results. Accordingly, several issues were addressed
during the algorithm validation stage. First logic was implemented, which ignored very short
workload periods unsuitable for WACC distance changes (which itself would need some time
to realize a higher distance to the leading car). Furthermore, in our experimental design
workload was not only induced by the 2-back task but could have been induced by several
events. That is, physiological data may reflect general demand increases and are not only
bound to effects of a certain task.
The implementation of the described feature led to a continual increase of overall
detection rate. At the beginning of algorithm development, the overall classification rate was
79.14%. After the implementation of logic to ignore short false-positive detection it increased
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to 81.37%. The consideration of other confounding workload factors and the implementation
of the extra trigger led to an overall detection rate of 85.82%.
Our results indicate that it is technically feasible to detect workload, as it would be
necessary for a real-time WACC, with a high rate over 70%. An important point to consider
is signal quality. Here, signal quality was high because of the adherence to medicinal
standards in vital sign detection equipment. If industrial equipment were used, the noise ratio
could have been much higher. On the other hand, future development of better sensors and
signal quality appear quite promising.
We wonder if an algorithm that works correctly at all times is essential, or, if false
alarms can be accepted by the driver and therefore workload periods are detected with high
accuracy. The reason for this approach is, that our work with the algorithm showed that a
higher false positive rate would also lead to a higher true positive rate. In particular, if
WACC is designed in a way that headway change is not noticeable, this approach could be a
possible solution for the development of an actual WACC system.
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8 General discussion
After in-depth presentation of experimental background and design, the following
chapter summarizes theory and experimental findings, and then discusses implications.
8.1 Background and chosen approach
ADAS assist drivers in the execution of the driving task, increasing safety and
supporting the driver in different situations (e.g. blind spot detection that warns a driver of
lane-changing vehicles that might be overlooked; active cruise control for maintaining safe
distance to a leading car, etc).
To date ADAS address needs of different vehicles or environmental conditions but do not
respond to the needs of drivers as changeable variables. In particular, driver workload is
ignored as a changing variable with a great influence on the execution of an appropriate
reaction. Several studies suggest that high workload indeed influences drivers in the correct
execution of the driving task (Engström et al, 2005; Horrey et al., 2006; Horrey & Simons,
2007; Jamson & Merat, 2005; Lamble et al., 1999).
The correlation of workload and performance was identified over a century ago, in the
so-called Yerkes–Dodson Law (Yerkes & Dodson 1908). The MIT AgeLab developed a new
model of the Yerkes–Dodson Law for the driving task (Coughlin et al., 2009) and developed
a so-called aware car based on this approach. The aware car keeps drivers within an optimal
performance range by continually bringing them back to this range. As such, adaption of a
car which in turn leads to adaptation of the driver has important implications for model
development for high workload situations (figure 15). Specifically, would a driver
effortlessly adapt to his/her vehicle’s adaptation? What kind of adaption – according to the
source of workload – would be necessary to enable the correct adaptation signal for and
enactment of the driver? Etc. The model presented here states that under high workload
conditions, a vehicle’s safety parameters should be increased (that means adapting the car to
the high workload and therefore high reaction time of the driver, instead of trying to get the
driver to adapt himself), and conversely lowered under low workload conditions. Adhering to
this approach would ensure that appropriate safety parameters are associated with a driver’s
workload condition.
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Figure 15 In dependence on the flower model from Hajek, W. (2014). Evaluating the
potential for workload-based driving assistance systems from a psychological,
technological and physiological perspective. In A. Stevens, C. Brusque & J. Krems
(Eds.), Driver Adaptation to Information and Assistance Systems (pp. 197-214). London,
United Kingdom: The Institution of Engineering and Technology.
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The advantage of the flower model is that the driver does not have to adapt (i.e. a
compensation rather than adaptation strategy) and further that it is not absolutely necessary to
know the reason for high workload.
In order to bring a driver under high workload back to optimal range with the aware
car concept based on the adapted Yerkes–Dodson Law, the vehicle must “know” precisely
what the driver is doing to implement the correct workload-lowering countermeasure and
enhance performance, or signal the driver regarding a required adaptive response. According
to the flower model, this would not be compulsory; to raise security parameters and therefore
enhance general safety for a short time period would – in most cases (if not all) – help the
driver prevent an accident, but would not worsen the situation. Under low workload
conditions, certain safety parameters would be lowered so that the driver could automatically
direct his attention back to the roadway (e.g. lower the ACC distance to a minimum). This
part of the model should be the subject of future research.
We developed a WACC system using a flower model-based approach of high
workload compensation. It was expected that this WACC system would increase distance and
therefore raise safety parameters in the case of high workload periods. When a driver’s
workload is lowered to an optimal level (derived from physiological data), then headway is
correspondingly lowered to prevent drivers from entering a too low workload condition.
There are several methods available to measure mental workload. Here, we sought a
measurement approach with low-interference. Thus, physiological data were chosen as our
workload measurement method. Physiological measurements for detecting workload have
been widely used (Brookhuis et al., 2009; Katsis et al., 2006; Liu & Lee 2006; Mehler et al.,
2009; Mehler, Reimer & Coughlin, 2010; Mulder et al., 2005; Wang et al., 1998) and
therefore are feasible as foundation for an online workload algorithm.
Four experiments (i.e. two simulator and two on-road) are presented and insights
gained are presented in the following.
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8.2 Summary of findings32
After in-depth discussions of the experiments of this thesis in Chapter 4-7 the
following sections provides a short summary of the main goals and findings to ensure a good
basis for the discussion and conclusion section.
8.2.1 Experiment 1 – Relationship between brake reaction time and workload level.
The main goal of this experiment is to validate the connection of physiological data
and different levels of cognitive workload. Furthermore, we aimed to determine if all
workload levels can be distinguished or if this is only possible between baseline and high
workload levels. To develop an online workload detection algorithm, it first has to be proved
that offline detection of different workload levels is possible, and second, that a detection
algorithm can be developed.
The results of this experiment clearly showed that physiological data differs
significantly between workload levels and provide a good basis for workload detection
algorithm:
A within-subjects ANOVA of physiological data of 73 participants showed highly
significant increases in beat-to-beat HR, high significant decreases in HRV and significant
increases in skin conduction level over all workload levels. Respiration was excluded for
further analysis. The offline analysis of physiological data showed that especially HR and
HRV because of their very high significant effect, showed potential for implementation in an
online workload algorithm for detecting different workload periods.
These results also indicate that physiological data are not only feasible for a binary
(just one change to a higher distance at high workload levels) but also for a gradual (continual
changes of distance according to finer increments in workload) change of distance. That
means that from the perspective of physiological data, a WACC could be differing between
different stages of workload (low, medium and high) which enables a broad range of adaption
strategies.
32 This section was published as part of the book chapter Hajek (2014) in a revised version (cf. Hajek, 2014, p.202-211). Reproduced by permission of the Institution of Engineering & Technology.
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The second main goal was to indicate the influence of different cognitive workload
levels on brake reaction time. The WACC shall adjust distance under high workload levels to
provide the participant with more time for an appropriate time for reaction. This is a critical
question for establishing the safety advantages of WACC.
Our results indicate that concerning an adaption strategy in the case of brake reaction
time, a binary WACC would be consistent with the influence of workload on human
performance: An ANOVA for 36 participants showed no significant effects over all workload
levels. A conducted contrast analysis found a significant increase between the cue task and
the 2-back task level in brake reaction time.
Based on these results and regarding future experiments, a binary system is
considered most appropriate for the development of WACC. Nevertheless gradual workload
detection could be used for other compensation measures that could prevent drivers from
entering this high workload state.
8.2.2 Experiment 2 – Wizard of Oz simulation of the WACC in the simulator
In a step-by-step approach and after establishing the general detection of workload
because of physiological data, we validated the results under the condition that the driver is
driving with ACC. According to this change in the experimental setting, the first goal of this
experiment was to find out if the physiological changes concerning workload were as
sensitive as in the first experiment without ACC.
Physiological data validated the results of Experiment 1. High workload levels can be
easily distinguished using HR, respiration and GSR. Because it is uncertain whether
respiration data are influenced due to verbal answering behaviour, HR and GSR data were
considered more reliable data sources for future algorithm development. As HR showed
consistence increases in Experiment 1, it is preferred over GSR data and is used as single
detection parameter for further experiments.
Another research goal was to confirm that brake reaction time should increase in high
workload situations. As WACC adjusts distance to a leading car to maintain a greater
headway, this effect should be compensated for. This assumption must be validated in a
critical situation under high workload conditions, and compared with normal ACC to ensure
that no human adaption effects will occur, which minimize this security advantage (risk
homeostasis).
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Concerning this security aspect of the developed WACC, simulator data has been
logged and analyzed. Brake reaction time was measured from (i) the point of the lane change
of the leading car, thereby providing an unobstructed view to the blocking vehicle (ii) to the
point of first brake pedal pressure. We found no significant results for brake reaction time,
which argues against risk homeostasis theory and argues against adaptive human behaviour to
a less dangerous situation as in this case. In other words, participant reactions in WACC and
ACC occurred at the same point in time. In addition highly significant smoother deceleration
was found in WACC condition compared with the ACC condition, which supports a possible
lower risk for rear-end collision in real-life scenarios in the case of a critical braking
manoeuvre.
One other main research goal was the evaluation of the acceptance of WACC
compared to ACC. In particular, prior information concerning system mode were researched
as influential variables concerning changes in acceptance.
Different approaches were used to gather knowledge of acceptance:
(1) Providing participants with no prior information about differences in system modes,
revealed no significant differences in acceptance for both systems. This is likely because only
7 out of 47 participants noticed any change in distance. After participants were given an
explanation about differences between both systems, highly significant increases in
subjective variables reasonable, helpful, comfortable and distance sensation were found.
(2) No significant differences between ACC and WACC systems were found in
subjective stress level, which is reasonable as the 2-back task as main stress factor had to be
completed in both conditions.
(3) Another question asked how much money people would spend to buy one of the two
systems. Even though these measures in general do not correspond with real spending
behaviour an overall assumption of acceptance can be made. We found that highly
significantly more money would be spent for the WACC system.
Altogether the main focus in Experiment 2 was establishing acceptance and security
of a WACC system and validating the effects of workload on physiological data.
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Concerning physiological data, HR and GSR displayed changes in workload level
very well, even when participants used an ACC system. Both variables can thus be used for
future algorithm development.
Regarding safety, there was no increase in brake reaction time, which suggests the
same reaction speed in a critical situation and argues against risk homeostasis theory under
WACC conditions. If participants adapted according to this theory, they would show an
increase in brake reaction time for the WACC condition. Furthermore we found a smoother
deceleration for the WACC condition, which shows that participants really used the higher
distance and therefore minimized the risk of a rear-end collision with their own vehicle.
Subjective acceptance and most of the subscales suggest a clear preference for WACC
if participants are provided with prior information. Aside from this, the findings suggest that
most drivers do not realize changes in the mode of a WACC system compared with an ACC
system under high workload conditions. This could have occurred because participants
experiencing high workload were unaware of minor changes. The distance to a leading car is
seen as such a minor change because it is not a critical factor, as long as no critical situation
arises (e.g. car in front is having an accident).
As mentioned, results have to be validated in reality, especially those questions
concerning system mode awareness. As this experiment was performed in a static simulator,
only sound provided information concerning changes in speed. Moreover, distance perception
cannot be compared to a real-life setting.
Encouraged by the promising results of the WACC system, we next focused on the
implementation of such a system in a real car to research system mode awareness and
acceptance on the street. Even though it would be reasonable to validate critical situations in
an on-road setting, we refrained from doing this for safety reasons.
8.2.3 Experiment 3 – Wizard of Oz simulation of WACC on the road.
Concerning the detection algorithm, prior experiments in the simulator showed that
simple detection of different workload periods is possible. In particular, HR showed stable
results in both of the last two experiments. Therefore one of the main goals was to provide
the basis for the development of a real world detection algorithm. To keep the possibility of
confounding effects low, the WACC was coupled with the secondary task instead of already
introducing a working system. This gave us the possibility to gather first information
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concerning physiological data and to develop workload detection algorithm in real-life
settings.
Detection rates were quite high (True negative: 66.23 %, True Positive 83.37) giving
a good basis for continual improvement and implementation of the WACC based on such a
detection algorithm in the next experiment.
Promising results of the simulator experiments suggest that participants accept
WACC more than ACC and that they do not notice differences in system mode between the
two systems. As simulator settings are varying greatly from real-life conditions, the second
main research goal of this experiment was to validate if the participants are showing the same
acceptance in a real-life setting.
Concerning acceptance, values for the ACC and WACC were >7 out of 10 and there
was a significant preference for WACC.
As we have found out in experiment 2 system mode awareness is an influencing
factor (that is, realizing changes in distance in the WACC setting). Therefore we split the
participants in groups concerning their system mode awareness: 17 (56.67%) did not notice
any change in distance, whereas 13 (43.33%) did realize a change in distance. A significant
increase in acceptance scores for participants who did not notice the system was found, after
they had driven with the WACC system; there were no significant results in acceptance
concerning the influence of information (with or without information). For the group that
noticed the correct or incorrect behaviour of the system we did not obtain significant results.
Experiment 3 therefore shows that more people do notice a change in distance, in
reality. Our assumption lies in the observation that this is connected to the speed at which the
WACC system changes its distance to the leading car. In discussing this with technicians it
became clear that it would be possible to program smoother distance change behaviour.
Although this is not in the focus of this dissertation it shows future directions for adapting
WACC. Significant increases in acceptance for participants who did not notice the change in
distance suggest that participants would prefer an unnoticeable, automated system.
8.2.4 Experiment 4 – On-road study with WACC.
In the previous 3 experiments we showed the foundation of a WACC in simulator and
real-life driving situations. To consider the WACC in future cars, a driving algorithm has to
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be developed with an overall detection rate of >70%. As mentioned in the discussion of
Experiment 3, workload (defined in our case as increase in HR within a certain time range)
could not only be connected to the start of the 2-back task but also to other changes in the
environment and to behaviour of the participants. The in-depth analysis concerning
correlations with other occurrences and reactions of the algorithm are topics of future
research.
Altogether only 10 participants took part in the experiment, and thus, results were
interpreted very carefully. Concerning the detection algorithm, after implementing logical
extensions to the algorithm, the overall detection rate increased to 81.37%. Furthermore if the
extra trigger is included as a possible occurrence of workload (and the according non-
workload periods are treated as workload periods) detection rates increase to 85.82% of the
whole time of the experimental setting.
Therefore experiment 4 shows that the application of WACC with HR can be
implemented in a car and is therefore technically possible. However, our definition (% of
total workload-induced time) of correctness will never reach 100% because of (i) time lags
and (ii) problematic issue of influences on HR, which may occur for various reasons.
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8.3 Discussion and conclusion
This thesis investigates the use of physiological data (as workload indicators) in ACC
and examines the new system WACC. WACC could represent a new set of future assistance
systems based on physiology. Four experiments were conducted in the course of this thesis to
provide first empirical evidence of technical possibility, usefulness and acceptance of such
and ADAS.
As stated in the introduction, a major challenge lies in defining points in time when a
driver should resume vehicular control as well as defining optimal security and time
thresholds that would allow drivers sufficient time to both resume control and take
appropriate action to prevent an accident.
This thesis supports the point that workload should be considered as an input
parameter for future driving assistance systems. Our researches validate that the driver
himself is not stable in his reaction times, but is strongly dependent on mental workload.
These dependencies are resulting in a delay in brake reaction times in critical situations.
Therefore the general concept of workload-adaptive cruise control is in line with the natural
compensation behaviour of the human being – especially if we consider that those adaptive
driving assistance systems could be designed in such a way that the driver will not realize
changes to higher security levels, but nevertheless drives with safer security thresholds.
The main goal of this dissertation, to evaluate the possibility of employing physiology
as an input parameter to monitor automatic threshold changes with active cruise control in
response to different workload levels, is reached in real-life circumstances. A WACC was
developed, which changes its distance to the driver’s actual workload in real-life
circumstances.
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The flower model-based on the adapted Yerkes–Dodson law of the MIT-Lab built the
foundation for the adaption mode. According to this model workload will only be adapted in
high workload situations, as this is the point which lies behind the workload redline, where
the performance decreases. There is a wide area between low and high workload where no
performance decrease takes place but where the human body invests higher resources to
prevent performance decrease. Physiological data shows a high sensitivity, so that these
compensatory stages could be detected too. A whole set of compensatory measures comes to
mind which could help to prevent that a driver will eventually ever reach the performance
decrease stage (starting from minimizing information systems to lowering speed or indicating
time for a break). Which of these measures are feasible for future driving assistance systems
is up to further research and cannot be answered within this research. The findings in general
supporting the idea to think about new car concepts as for example introduced in the
AwareCar concept developed by Coughling, Reimer & Mehler (2009). The AwareCar is a
concept where the car continually measures drivers (physiological) parameters and takes
preventive measures to keep him in the ideal state fo performance, Even if the detailed idea
may not match the exact direction of research in this thesis, the idea provide research with
concepts in which kind of future concepts are possible.
The measurement of physiological data as an indicator of arousal (Brookhuis & de
Waard, 2010; de Waard, 1996; Mayser et al., 2003; Veltman & Gaillard, 1998) offers
information about actual workload of a driver. Workload in turn, is connected with
performance, and reveals the performance that a driver exhibits at a moment in time or during
a critical situation. Our first simulator study shows that cognitive workload directly
influences performance and leads to decreases in performance. This in line with several other
studies and suggests that our setting is feasible enough for further researches and is founded
on a stable basis of past physiological researches.
Concerning the feasibility of the combination of physiological data, quite surprising
results were found within this thesis. At the beginning the main possibility to generate a
stable and reliable workload algorithm seemed to be a multimodal algorithm consisting of a
neural network of several signals, which were already found feasible in other experiments
(heart rate, respiratory signals, heart rate variability and skin conductance). On the contrary
our experiments showed that a workload algorithm based simply on heart rate is good enough
for an overall detection rate of over 85 % of the time. Considering time lags because of
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human response behaviour and experimental settings, this value is quite high and seems more
than feasible for the usage of a system which shall adapt over a higher time period and is not
bound to trigger time sensitive systems (like emergency braking systems). Heart rate is
furthermore one of the most researched and common measures of the past. Therefore people
are already used to get it measured at the doctor or to establish their personal performance in
fitness related areas of their life (e.g. running, cycling). For example the iPhone is able to
detect some kind of heart rate based on the color differences of the thumb in combination
with the integrated camera. Fitness trackers are all equipped with a kind of pulse or heart rate
measurement to inform people when they are training in their optimal performance range.
Keeping these developments in mind, the next step of informing the driver when he is in the
right performance sector for optimal driving performance does not seem far away and opens
the possibility for further research of the combination of driving assistance or information
systems combined with heart rate measurements. Furthermore individual differences in
coping with workload are considered in this workload detection approach. In one of our
experiments a participant was test driver for BMW. His daily business was to test and drive
new prototypes of cars. As one can imagine this individual is used to highly critical situation
as he drives cars in high speed ranges and has to cope with possible failures of the system at
each given point in time. His personal heart rate didn’t show any changes in the high
workload situation and we can assume that this is in direct relation to his individual workload
level. Even if he was in the same workload situation as other drivers in the experiments, his
personal ability to cope with workload was quite higher than those of other individuals.
Therefore the here presented WACC system also takes into account inter-individual
differences of drivers.
However, physiological data, as an indicator of arousal, reacts not only to certain
tasks but also to a wide set of events (e.g. changes in environment) as seen in the on-road
workload validation study. It is doubtful that all of these events influence performance in the
same way. Future studies should investigate how workload can be classified according to
“safety risk workload” and “non-risk workload” (if such workload exists) or if any type of
workload leads to a safety risk.
If no “safety risk-free” workload exists, then adaption of WACC in every workload
situation may be a feasible solution, and appears to enhance safety according to our results. If
a safety risk-free workload indeed exists, which cannot be distinguished from other kinds of
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workload according to physiological data, further studies must examine how acceptance
decreases due to WACC behaviour. If a decrease in acceptance leads drivers to switch-off the
system, then it loses much of its value.
One way to identify safety risk-free workload is to identify the source, which induce
the workload. To determine which vehicular source contributes to creating workload (and
therefore eliminating the above described problem), other car parameters may provide further
information. For example, if physiological data detects high workload and the use of a
Bluetooth connection and phone speaker are confirmed in the vehicle, then workload most
likely is the result of a demanding phone call. Such connectivity and related conclusions have
to be researched in future.
Provided that workload is identified correctly, the action of the WACC system will
lead to higher safety as shown in the simulator experiment. That is, WACC simulates natural
compensatory behaviour of human drivers under high workload: decreasing speed and
enhancing the headway to a leading vehicle.
As participants did not adapt to WACC as anticipated due to risk homeostasis theory
in a risky situation (Wilde, 1982), the system could enhance safety. Brake reaction time is the
same with ACC and WACC under high workload conditions, and only negative velocity is
lower, which shows smoother deceleration.
Nevertheless one has to keep in mind that these results are derived from one critical
event. Over a longer period of time, drivers could feel safer and eventually adapt to the new
system. Whether or not this effect indeed occurs, should be investigated in further
experiments. Moreover, this effect was found in a simulator study and must be confirmed in
reality.
Acceptance is very important; a system that is not accepted can be shut off by the
driver. In all of our experiments, we found that acceptance in general for WACC was high,
which is connected with generally high acceptance of ACC. Evidence of its acceptance also
was confirmed by the questionnaire. Nevertheless, acceptance of WACC is higher than ACC
(after participants got information concerning system mode), likely because of system
awareness. In the simulator, results showed that people rate WACC more positively after
obtaining information about the system’s functionality, thus contributing to higher system
awareness. Before receiving the explanation, most participants (40 out of 47) did not notice
that the system adjusted the vehicle’s distance in high workload situation and therefore
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differed from normal ACC. In the on-road experiment, fewer participants (approx. 50%) did
not notice the change in distance thus validating high acceptance, whereas those who realized
the change in distance rated the system as highly as ACC. The question that arises at this
point is: do drivers generally prefer a system that they do not notice, and which enhances
safety? These questions are beyond the scope of this thesis, yet, could prove interesting in the
future. Especially if we consider other disciplines like computer sciences. After the first
euphoria of the new possibilities what computer possible can do a change in the mindset of
developing systems took place. With raising complexity of systems in the last years User
Experience (UX) as design approach is covering more and more ground in the development
of new systems. UX mainly sees technology as a possibility to provide the user with
experience not with a new function. The underlying assumption is, that technology and its
functionality is not enough to use a system in the personal life of a person. Instead of that it is
necessary to take further research with customer in which way he would like to use the
product in his daily life. With this information it is tried to integrate technology in a way in
the customers life, that it is making his life more joyfull or easier and therefore may not even
realize that he is supported by a system. Nevertheless what he is realizing is that in his life a
positive effect took place.
If we follow this approach it is necessary to think about the reason why participants
realized the adaptive behaviour of the system. One hypothesis as to why participants did
realize the adaptive behaviour of the system could be that the sensation of acceleration and
deceleration (e.g. motor noise, feeling of braking) raised awareness. This parameter could be
adapted in the future to make the WACC system’s behaviour less detectable under real
circumstances – that is acceleration and deceleration would take place in smoother way.
Results of workload detection rate in response to physiological data are very
promising. Different levels of workload are easy to distinguish with simple statistical
methods such as t-tests and ANOVAs. A developed workload algorithm was able to identify
85.82% of the total workload time correctly. This is particularly impressive considering the
several second delay of on- and offset until physiological data reacts to changing workload
conditions, and the constructed lags in the design of the experiment (as workload induction
started only at the presentation of the third presented number). WACC is designed as an
anticipatory system. Therefore it does not react immediately after the possible stop of
workload but gives the driver a greater headway several seconds after the workload is over.
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This does not lead to problems as long as this behaviour does not interfere with general
acceptance. Concerning the delay of detection at the beginning of the workload period, faster
detection could be possible because of in-car measurements (e.g. can-bus information to
determine if a driver is using the telephone).
The quality of the detection algorithm is closely connected with the quality of the
underlying data derived from the physiological sensors. The sensors used in the experiments
described in this thesis are medically certified and therefore feature very high signal quality.
Nevertheless they are sensitive to movements and therefore contribute to artifacts in the data,
if, for example, the participant is gripping the steering wheel too tightly or is moving in
his/her seat. Furthermore, electrodes have to be applied on the skin and are connected by
wires with the measurement device, within the car’s interior. This measurement equipment is
not feasible for use in real-life settings at present, but as sensors become smaller and cheaper
(and perhaps wireless in future), this problem will likely be solved in time. First
developments are already showing the possibility of such future system. Fitness trackers and
first sport shirts which have interwoven the physiological measurement devices in the fabric
of these shirt which are already available for the public market show the direction how future
physiological sensors could look like and are giving hope for fast developments in this sector.
From a theoretical perspective, the presented results suggest that the flower model, as
further development of the adapted Yerkes–Dodson Law, is valuable for the WACC system
developed in this research. Nevertheless it should be noted that the flower model has only
been evaluated for WACC and not for Advanced Driver Assistance Systems in general. Other
driver assistance systems such as, for example lane keeping systems may be difficult to adapt
or it may not be feasible at all. The usefulness of the flower model for other systems depends
strongly on the one hand, on whether different kinds of workload can be identified and
detected which have the same effect on performance, and on the other hand, on what safety
parameter of a certain assistance system shall be adapted. Therefore applications for future
systems and for different kind of workloads depend strongly on further research in this area.
In general promising results of the research performed in this thesis represent a first
step toward physiologically based driver assistance systems. To this end, the many questions
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which have been raised here, need to be evaluated and validated in future experiments before
such systems can be introduced in the driving context.
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Eidesstattliche Erklärung
Hiermit erkläre ich, Wilfried Hajek, geboren am 30. Dezember 1984 in Graz, dass ich die vorliegende
Arbeit selbstständig verfasst und keine anderen als die angegebenen Hilfsmittel verwendet habe.
Wilfried Hajek
Wien, den
W o r k l o a d A d a p t i v e C r u i s e C o n t r o l P a g e | 107
Curriculum Vitae
Wilfried Hajek
Ohmanngasse 16/4/8
1190 Wien
Mobile: +49 171 3050149
Email: [email protected]
Date of birth: 30.12.1984
Place of birth: Graz
Nationality: Austria
Work Experience
Work period
Occupation
October 2014 – till now
Agile Coach and Consultant
Employer Boris Gloger Consulting GmbH
Responsibilities Developing and implementing human oriented work environments
Work period
Occupation
April 2010 – May 2013
Researcher
Employer BMW Group Research and Technology
Responsibilities Developing a new workload-based driver assistance system
W o r k l o a d A d a p t i v e C r u i s e C o n t r o l P a g e | 108
Work period
Occupation
October 2009 – April 2010
Interviewer and Transcriptor
Employer Karmasin Opinion Research Institute, Vienna
Responsibilities Interviewing employees of the top management;
Transcription and translation from German to English
Work period August 2007 – January 2008
Occupation Trainee
Employer Austrian National School-psychological Information Centre, Wiener
Neustadt
Responsibilities Accomplishing and interpreting psychological tests;
Writing certificates and informational brochures
Work period August 2004 – August 2006
Occupation Advisor of psychologically at-risk children and teenagers
Employer School Josefinum, Klagenfurt
Responsibilities Supervising homework; Test preparation;
Personal talks due to work history
Education and training
W o r k l o a d A d a p t i v e C r u i s e C o n t r o l P a g e | 109
Period
Institute
October 2003 – March 2010
University Klagenfurt
Occupation Psychology Diploma, Grade 1.8
Publications
Hajek, W. (2014). Evaluating the potential for workload based driving assistance
systems from a psychological, technological and physiological perspective. In
A. Stevens, C. Brusque, & J. Krems (Eds.), Driver Adaptation to Information and
Assistance Systems (pp. 197-214). London, United Kingdom: The Institution of
Engineering and Technology.
Hajek, W., Gaponova, I., Fleischer, K. H., & Krems, J. (2013). Workload-adaptive
cruise control – A new generation of advanced driver assistance systems.
Transportation Research Part F: Traffic Psychology and Behaviour, 20, 108-120.
doi:10.1016/j.trf.2013.06.001