1 Introduction
A self-driving vehicle is one of the most desirable visions in modern transport (Maurer, Gerdes,
Lenz & Winner, 2015). The new technology of automated driving aims at increasing road safety
and the reduction of road fatalities, while at the same time it is supposed to promote driving comfort and
convenience significantly. The potential benefits for drivers are countless: their travel time could be used
more productively by working or simply relaxing in the car during their daily commute. Moreover, this
time could be reduced because of designated lanes and improved traffic fluency. A fully automated vehicle
could even pick its owner up at home, or find a parking spot on its own. Already in the year 1956, an
advertisement for America’s Independent Electric Light and Power Companies by Miller demonstrated
the possible advantages of automated driving, illustrating a family enjoying a ride in their autonomous
vehicle. The advertising text said:
“Electricity may be the driver. One day your car may speed along an electric super-highway, its speed and
steering automatically controlled by electronic devices embedded in the road. Highways will be made
safe—by electricity! No traffic jams. . . no collisions. . . no driver fatigue.”
(America’s Independent Electric Light and Power Companies, 1956, p. 8).
Today, the automotive industry is working hard to make possible the final steps towards this technology,
which once seemed to us like the fascinating idea of a science fiction movie. The vision of a highly
automated driving vehicle is becoming increasingly realistic, with corporations and research institutes
around the world investing time and money to achieve this new, environmentally friendly, more efficient
and safer mobility.
Autonomous driving is characterized by the driver relinquishing manual vehicle control and instead
becoming a passenger of a self-manoeuvring vehicle. Because of this, AVs need advanced sensors and
intelligence to recognize relevant objects in driving situations and to safely perform driving tasks. It is
hoped that this will result in increased road safety and efficiency. However, in addition to the technical
challenges on the road to automated traffic, the psychological aspects also play an important role in the
acceptance and use of the technology. Due to the new distribution of roles with regard to driving control,
humans remain as on-board users without driving-related tasks. Therefore, the question of humanmachine interaction (HMI) is of utmost importance to create a common understanding of the situation
and upcoming driving maneuvers and to ensure user acceptance and trust.
1.1 Motivation
Due to the shifting role of the driver, who becomes an uninvolved observer, the amount and type of
information available to him about the driving situation and future actions is changing as well. For a driverfocused interaction strategy, it is critical to facilitate the exchange of information between the vehicle and
the on-board user. Novel interaction strategies need to focus on creating a shared understanding of the
current driving situation and a predictable future behavior of the AV for the user on board the vehicle to
avoid users being surprised by unexpected actions of the automation. Insufficient knowledge of the
situation and the AV's intent could lead to unnecessary or even dangerous driver intervention. LED strips
installed inside the vehicle represent an intuitive and well-accepted type of HMI (Dziennus, Kelsch &
Schieben, 2016), which use human peripheral vision to display important information about the
environment and automation status. However, these interaction concepts were developed for highways
and lower levels of automation than highly automated driving and need to be adapted for urban scenarios.
Therefore, this thesis will further investigate an LED light band design that can communicate to passengers
which external road users have been detected. It is of interest to what extent this communication strategy
can lead to a better user experience in terms of perceived safety and the understanding of the automation
and the driving behavior.
Previous studies found, that different HMI interaction designs (Wilbrink, Schieben & Oehl, 2020) lead to
higher user trust and usability. In a subsequent study, the so-called perception-based design was further
investigated regarding the optimal time of presentation and the distance at which the light band visualizes
the external road users. In a co-design online study, participants were able to set this time themselves.
The results of this study were reviewed and investigated in more detail in this thesis. In the two studies
included in this thesis, it was of interest which is a suitable time of visualization and whether the results
can be replicated and transferred for a different traffic environment and two (instead of one) road users.
This work focuses on high automated driving levels and does not include semi-automated or fully
automated driving (SAE, 2018).
1.3 Overview of the work
The present work is structured as follows: this first chapter serves as an introduction and will be followed
by an explanation of what automated driving is in chapter 2, where the theoretical background is
presented as well as the theoretical concept of the user experience. It concludes with a brief summary of
the current state of research that had been done so far regarding an LED-band as an internal HMI concept.
Subsequently, chapter 3 presents the research concept and gives an overview of the studies and the
methodological procedure. The results of these studies are reported in the fourth chapter of this thesis
and thereafter discussed in chapter 5.
The work at hand specifically aims at investigating how the user experience of an AV can be optimized
by enhancing transparency and communication of the automation towards the user. This in one of the
numerous challenges that need to be faced to bring automated driving on the road for everyone. In
return, automated driving has the potential of changing our mobility dramatically. Future users
should be guided into this new world of automated driving.