Web view5 Workload-adaptive ... The world is full of ongoing ... The most interesting result was that people were not aware of the adaptation and did not notice that the

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

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 | 2

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.


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


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


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.


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


BMW Group Research and Technology, he identified important department- and project-

partners and combined the knowledge to a result which benefits science and economy.


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.


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


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.


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


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.


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?


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.


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.


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.


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).


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.


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


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).


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)


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.


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


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)


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.


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


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


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


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!




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




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


ed b

y 4

wor

kloa

d le

vels

(0-b

ack)





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





Start of the 0-back task 00:32:40 2 min 0-back Doing 0-back task








One

Ses

sion

Cou

nter

bala

nced

by

4

wor

kloa

d le

vels







vehicle - acceleration



(1-b

ack)







One

Ses

sion

Cou

nter

bala

nced

by

4 w

orkl

oad

leve

ls(2

-bac

k)




Instructions 1-back task 00:46:4020 s

2-back Hearing Instructions








vehicle – braking event




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





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.


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


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.


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.


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.


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)

mailto:Karl-Heinz.Fleischer%[email protected]


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


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


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


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).


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.


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


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.


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


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).


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


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


Questionnaires

Driving (single


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


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


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


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


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.


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.


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


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


(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.


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.


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,


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,


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).


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.


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


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.


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.


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


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


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


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


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


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


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


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


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


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.


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-


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.


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.


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


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


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


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


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).


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%.


Figure 14 Left: visualized percentage of classification rates in absolute time with logic.

Right: visualized proportion of extra trigger and not explainable percentage


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


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.


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.


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.


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.


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.


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).


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.


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


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


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.



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.


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


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


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


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.


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


which have been raised here, need to be evaluated and validated in future experiments before

such systems can be introduced in the driving context.


9 References

Beauducel, A., Strobel, A., & Brocke, B. (2003). Psychometrische Eigenschaften und

Normen einer deutschsprachigen Fassung der Sensation Seeking-Skalen, Form V. /

Psychometric properties and norms of a German version of the Sensation Seeking

Scales, Form V. Diagnostica, 49(2), 61-72. doi:10.1026//0012-1924.49.2.61

Bellem H. (2013). An on-road study of system awareness, acceptance, and trust of a

simulated Workload-Adaptive Cruise Control (Unpublished master’s thesis). Fakultät

für Human- und Sozialwissenschaften der technischen Universität Chemnitz,

Chemnitz, Germany.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York,

USA: Springer.

Brookhuis, K. A., & de Waard, D. (2010). Monitoring drivers’ mental workload in

driving simulators using physiological measures. Accident Analysis & Prevention,

42(3), 898-903. doi:10.1016/j.aap.2009.06.001

Brookhuis, K. A., de Vries, G., & de Waard, D. (1991). The effects of mobile

telephoning on driving performance. Accident Analysis and Prevention, 23(4), 309-

316. doi:10.1016/0001-4575(91)90008-S

Brookhuis, K. A., & de Waard, D., (2011). Measuring physiology in

simulators. In D.L. Fisher, M. Rizzo, J.K. Caird, & J.D. Lee (Eds.), Handbook of

Driving Simulation for Engineering, Medicine and Psychology (pp. 233 – 241). Boca

Raton, USA: CRC Press.

Brookhuis, K. A., Driel, C. J. G., Hof, T., Arem, B. & Hoedemaeker, M. (2009). Driving with

a congestion assistant; mental workload and acceptance. Applied ergonomics, 40,

1019-1025. doi:10.1016/j.apergo.2008.06.010


Clarion, A., Ramon, C., Petit, C., Dittmar, A., Bourgeay, J. P., Guillot, A., Gehin, C.,

McAdams, E., & Collet, C. (2009). An integrated device to evaluate a driver's func-

tional state. Behaviour Research Methods, 41(3), 882-888. doi:10.3758/

BRM.41.3.882

Coughlin, J. F., Reimer, B., & Mehler, B. (2009). Driver Wellness, Safety & the

Development of an AwareCar (White Paper). Retrieved from MIT AgeLab website:

http://agelab.mit.edu/system/files/file/Driver_Wellness.pdf

Coughlin, J. F., Reimer, B., & Mehler, B. (2011). Monitoring, Managing and

Motivating Driver Safety and Well-Being. IEEE Pervasive Computing, 10(3), 14-21.

doi:10.1109/MPRV.2011.54

Crowther, P. S., & Cox, R. J. (2005). A Method for Optimal Division of Data Sets for

Use in Neural Networks. In R. Khosla (Ed.), Knowledge-Based Intelligent Informa-

tion and Engineering Systems (pp.1-7). New York, USA: Springer.

D'Angelo, L. T., Parlow, J., Spiessl, W., Hoch, S., & Lueth, T. C. (2011). Unobtrusive

In-Car Vital Parameter Acquisition and Processing. In R. Wichert & E. Birgit (Eds.),

Ambient Assisted Living (pp. 257-270). New York, USA: Springer.

De Waard, D. (1996). The measurement of drivers' mental workload (Doctoral

Dissertation, University of Groningen). Retrieved from

http://irs.ub.rug.nl/ppn/297329391

De Waard, D., & Brookhuis, K. A. (1991). Assessing driver status: a demonstration

experiment on the road. Accident Analysis & Prevention, 23(4), 297-307.

doi:10.1016/0001-4575(91)90007-R

Duffy, E. (1957). The psychological significance of the concept of "arousal" or

"activation." Psychological Review, 64(5), 265-275. doi:10.1037/h0048837

https://springerlink3.metapress.com/content/?Author=Robert+J.+Cox

https://springerlink3.metapress.com/content/?Author=Patricia+S.+Crowther


Dusek, J. A., Coughlin, J. F., Reimer, B., & Mehler, B. (2009). Impact of Incremental

Increases in Cognitive Workload on Physiological Arousal and Performance in Young

Adult Drivers. Transportation Research Record: Journal of the Transportation

Research Board, 2138, 6-12. doi:10.3141/2138-02

Endsley, M. R., & Kiris, E. O. (1995). The Out-of-the-Loop Performance Problem and

Level of Control in Automation. Human Factors, 37(2), 381-394.

doi:10.1518/001872095779064555

Engström, J., Aust, M. L., & Viström, J. (2010). Effects of working memory load and

repeated scenario exposure on emergency braking performance. Human Factors,

52(5), 551 – 559. doi:10.1177/0018720810381072

Engström, J., Johansson, E., & Östlund, J. (2005). Effects of visual and cognitive

load in real and simulated motorway driving. Transportation Research Part F: Traffic

Psychology and Behaviour, 8(2), 97–120. doi:10.1016/j.trf.2005.04.012

Freeman, G.L. (1940). The relationship between performance level and bodily

activity level. Journal of Experimental Psychology, 26(6), 602-608.

doi:10.1037/h0056767

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


Hassenzahl, M., Burmester, M., & Koller, F. (2003). AttrakDiff: Ein Fragebogen zur

Messung wahrgenommener hedonischer und pragmatischer Qualität. In J. Ziegler &

G. Szwillus (Eds.), Mensch & Computer 2003: Interaktion in Bewegung (pp. 187-

196). Stuttgart, Germany: Teubner Verlag.

Healey, J., & Picard, R. W. (2000). SmartCar: Detecting Driver Stress. Proceedings of 15th

International Conference on Pattern Recognition, 4, 218-221.

doi:10.1109/ICPR.2000.902898

Horrey, W. J., & Simons, D. J. (2007). Examining cognitive interference and adaptive safety

behaviours in tactical vehicle control. Ergonomics, 50(8), 1340–1350.

doi:10.1080/00140130701318889

Horrey, W. J., Simons, D. J., Buschmann, E. G., & Zinter, K. M. (2006). Assessing

Interference from Mental Workload Using a Naturalistic Simulated Driving Task: a

Pilot Study. Proceedings of the 50th Annual Human Factors and Ergonomics Society

Meeting, USA, 50(17), 2003-2007. doi:10.1177/154193120605001762

Jahn, G., Krems, J. F., & Gelau, C. (2003). The 0.1 Hz component of heart

rate variability (HRV) and the peripheral detection task (PDT) to measure driver

workload. Journal of Psychophysiology, 17(3), 156.

Jamson, A. H., & Merat, N. (2005). Surrogate in-vehicle information systems and

driver behaviour: Effects of visual and cognitive load in simulated rural driving.

Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 79–96.

doi:10.1016/j.trf.2005.04.002

Jian, J. Y., Bisantz, A. M., Drury, C. G., & Llinas, J. (2000). Foundations for an

empirically determined scale of trust in an automated System. International Journal

of Cognitive Ergonomics, 4(1), 53-71. doi:10.1207/S15327566IJCE0401_04


Kaber, D. B., & Endsley, M. R. (1997). Out-of-the-loop performance problems

and the use of intermediate levels of automation for improved control system

functioning and safety. Process Safety Progress, 16(2), 126–131.

doi:10.1002/prs.680160304

Kahneman, D. (1973). Attention and effort. New Jersey, USA: Prentice Hall.

Karrer, K., Glaser, C., Clemens., & Bruder, C. (2009). Technikaffinität erfassen: der

Fragebogen TA-EG. Der Mensch im Mittelpunkt technischer Systeme, 8, 196-201.

Katsis, C. D., Ganiatsas, G., Fotiadis, D. I. (2006). An integrated telemedicine

platform for the assessment of affective physiological states. Diagnostic pathology,

16(1), 1-9. doi:10.1186/1746-1596-1-16

Kirchner, W. K. & Wayne, K. (1958). Age differences in short-term retention of rapidly

changing information. Journal of Experimental Psychology, 55(4), 352-358.

doi:10.1037/h0043688

Lamble, D., Kauranen, T., Laakso, M., & Summala, H. (1999). Cognitive load and detection

thresholds in-car following situations: safety implications for using mobile (cellular)

telephones while driving. Accident Analysis and Prevention, 31(6), 617–623.

doi:10.1016/S0001-4575(99)00018-4

Lenneman, J. K., & Backs, R. W. (2009). Cardiac autonomic control during simulated

driving with a concurrent verbal working memory task. Human Factors: The Journal

of the Human Factors and Ergonomics Society, 51(3), 404-418. doi:

10.1177/0018720809337716


Liu, B., & Lee, Y. (2006). In-vehicle workload assessment: Effects of traffic situations and

cellular telephone use. Journal of Safety Research, 37(1), 99–105. doi:10.1016/

j.jsr.2005.10.021

Mandryk, R., & Atkins, M. (2007). A fuzzy physiological approach for continuously

modeling emotion during interaction with play technologies. International Journal of

Human-Computer Studies, 65(4), 329-347. doi:10.1016/j.jsr.2005.10.021

MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms.

Cambridge, UK: Cambridge University Press.

Mao, Y. Y., Huang, S. Q. (2009). Pollen resistance to water in 80 angiosperm species:

Flower structures protect rain-susceptible pollen. New Phytologist, 183(3), 892-899.

doi:10.1111/j.1469-8137.2009.02925.x

Mayser, C., Piechulla, W., Weiss, K.E., & König, W. (2003). Driver workload

monitoring. Proceedings of the 50th-Anniversary Conference of the GfA, ISOES and

FEES, München, 41-44.

Mehler, B., Reimer, B., & Coughlin, J. F. (2010). Physiological reactivity to graded

levels of cognitive workload across three age groups: An on-road evaluation.

Proceedings of the 54th Annual Meeting of the Human Factors and Ergonomics

Society, 54(24), 2062-2066. doi:10.1177/154193121005402409

Mehler, B., Reimer, B., & Coughlin, J. F. (2012). Sensitivity of physiological measures

For detecting systematic variations in cognitive demand from a working memory task:

an on-road study across three age groups. Human Factors, 54(3), 396-412.

doi:10.1177/0018720812442086

Mehler, B., Reimer, B., Coughlin, J. F., & Dusek, J. A. (2009). The impact of

Incremental increases in cognitive workload on physiological arousal and

performance in young adult drivers. Transportation Research Record: Journal of the

Transportation Research Board, 2138(1), 6-12. doi:10.3141/2138-02


Mehler, B., Reimer, B., D’Ambrosio, L. A., Pina, A., & Coughlin, J. F. (2010). An

Evaluation of Time of Day Influences on Simulated Driving Performance and

Physiological Arousal. Proceedings of Transportation Research Board 89th Annual

Meeting, Washington DC.

Mehler, B., Reimer, B., & Dusek, J.A. (2011). MIT AgeLab delayed digit recall task

(nback). (White Paper Number 2011-3A). Retrieved from MIT AgeLAB Website:

http://www.researchgate.net/publication/230729111_MIT_AgeLab_Delayed_Digit_R

ecall_Task_(n-back)(Paper_2011-3B)(2011-06-28)/file/d912f503951996399e.pdf

Mehler, B., Reimer, B., & Wang, Y. (2011). A comparison of heart rate and heart rate variabil-

ity indices in distinguishing single task driving and driving under secondary cognitive

workload. Proceedings of the Sixth International Driving Symposium on Human Fac-

tors in Driver Assessment, Training, and Vehicle Design, Lake Tahoe, CA, 590-597.

Mulder, L. J. M., de Waard, D., & Brookhuis, K. A. (2005). Estimating mental effort using

heart rate and heart rate variability. In A. K. Brookhuis, N. A. Stanton, A. Hedge, E.

Salas & H. W. Hendrick (Eds.), Handbook of Human Factors and Ergonomics

Methods (pp. 20-1-20-8). Boca Raton: CRC Press.

Mulder, L. J. M., Dijksterhuis, C., Stuiver, A., & de Waard, D. (2009). Cardiovascular state

changes during performance of a simulated ambulance dispatchers' task: potential use

for adaptive support. Applied ergonomics, 40(6), 965-977.

doi:10.1016/j.apergo.2009.01.009

Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward Machine Emotional

Intelligence: Analysis of Affective Physiological State. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 23(10), 1175-1191. doi:10.1109/34.954607

Rakauskas, M. E., Ward, N. J., Bernat, E., Cadwallader, M., Patrick, C., & de Waard,


D. (2005). Psychophysiological measures of driver distraction and workload while in-

toxicated. Poster presented at 3rd International Driving Symposium on Human

Factors in Driver Assessment, Training and Vehicle Design. Retrieved from http://

www.its.umn.edu/Research/Papers/Cellphones/DA2005-RakauskasRevised.pdf

Reimer, B. (2009). Cognitive task complexity and the impact on drivers‘ visual

Tunneling. Transportation Research Record: Journal of the Transportation Research

Board, 2138(1), 13-19. doi:10.3141/2138-03

Reimer, B., & Mehler, B. (2011). The impact of cognitive workload on physiological arousal

in young adult drivers: a field study and simulation validation. Ergonomics, 54(10),

932-942. doi:10.1080/00140139.2011.604431

Reimer, B., Mehler, B., Coughlin, J. F., Godfrey, K. M., & Tan, C. (2009). An on-road assess-

ment of the impact of cognitive workload on physiological arousal in young adult

drivers. Proceedings of the First International Conference on Automotive User Inter-

faces and Interactive Vehicular Applications, New York, USA, 115-118. doi: 10.1145/1620509.1620531

Reimer, B., Mehler, B., Wang, Y., & Coughlin, J. F. (2012). A field study on the impact

of variations in short-term memory demands on drivers‘ visual attention and driving

performance across three age groups. Human Factors: The Journal of the Human

Factors and Ergonomics Society, 54(3), 454-468. doi:10.1177/0018720812437274

Rouse, W. B., Edwards, S. L., & Hammer, J. M. (1993). Modeling the dynamics of

mental workload and human performance in complex systems. IEEE Transactions on

Systems, Man, and Cybernetics, 23(6), 1662-1671. doi:10.1109/21.257761

Trzuskowsky A. (2012). Adaption eines Fahrerassistenzsystems basierend auf einer

Online Workload-Detektion (Unpublished diploma’s thesis). RWTH Aachen

University, Aachen, Germany.


Van der Laan, J. D., Heino, A., & de Waard, D. (1997). A simple procedure for the

assessment of acceptance of advanced transport telematics. Transportation Research –

Part C: Emerging Technologies, 5(1), 1-10. doi:10.1016/S0968-090X(96)00025-3

Veltman, J. A., & Gaillard, A. W. K. (1998). Physiological workload reactions to increasing

levels of task difficulty. Ergonomics, 41(5), 656-669. doi:10.1080/001401398186829

Wang, F., Sagawa, K., & Inooka, H. (1998). Time domain heart rate variability index

for assessment of dynamic stress. Computers in Cardiology, 25, 97-100.

doi:10.1109/CIC.1998.731734

Wartzek T., Eilebrecht B., Lem J., Lindner H.-J., Leonhardt S. & Walter M. (2011).

ECG on the road: Robust and Unobtrusive Estimation of Heart Rate. IEEE

Transactions on Biomedial Engineering, 58(11), 3112 – 3120.

doi:10.1109/TBME.2011.2163715

Watson, J. M. & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multi-tasking

ability. Psychonomic Bulletin & Review, 17(4), 479-485. Doi: 10.3758/PBR.17.4.479

Wilde, G. J. S. (1982). The Theory of Risk Homeostasis: Implications for Safety and

Health. Risk Analysis, 2(4), 209-225. doi:10.1111/j.1539-6924.1982.tb01384.x

Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of

habit-formation. Journal of Comparative Neurology and Psychology, 18(5), 459-482.

Zeitlin, L. R. (1993). Subsidiary task measures of driver mental workload: A long-

term field study. Transportation Research Record, 1403, 23-27.

Zuckermann, M., Eysenck, S. B. G., & Eysenck, H. J. (1978). Sensation Seeking in

England and America: Cross-cultural age and sex comparisons. Journal of Consulting

and Clinical Psychology, 46(1), 139-149. doi:10.1037/0022-006X.46.1.139


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


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

mailto:[email protected]


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


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

Documents

Web view5 Workload-adaptive ... The world is full of ongoing ... The most interesting result was that people were not aware of the adaptation and did not notice that the