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Context-Based Interface Prototyping: Understanding the Effect of Prototype Representation on User Feedback

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The rise of autonomous systems in cities, such as automated vehicles (AVs), requires new approaches for prototyping and evaluating how people interact with those systems through context-based user interfaces, such as external human-machine interfaces (eHMIs). In this paper, we present a comparative study of three prototype representations (real-world VR, computer-generated VR, real-world video) of an eHMI in a mixed-methods study with 42 participants. Quantitative results show that while the real-world VR representation results in higher sense of presence, no significant differences in user experience and trust towards the AV itself were found. However , interview data shows that participants focused on different experiential and perceptual aspects in each of the prototype representations. These differences are linked to spatial awareness and perceived realism of the AV behaviour and its context, affecting in turn how participants assess trust and the eHMI. The paper offers guidelines for prototyping and evaluating context-based interfaces through simulations.
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Context-Based Interface Prototyping: Understanding the Eect
of Prototype Representation on User Feedback
Marius Hoggenmueller
marius.hoggenmueller@sydney.edu.au
Design Lab, Sydney School of
Architecture, Design and Planning
The University of Sydney
Martin Tomitsch
martin.tomitsch@sydney.edu.au
Design Lab, Sydney School of
Architecture, Design and Planning
The University of Sydney
CAFA Beijing Visual Art Innovation
Institute, China
Luke Hespanhol
luke.hespanhol@sydney.edu.au
Design Lab, Sydney School of
Architecture, Design and Planning
The University of Sydney
Tram Thi Minh Tran
ttra6156@uni.sydney.edu.au
Design Lab, Sydney School of
Architecture, Design and Planning
The University of Sydney
Stewart Worrall
stewart.worrall@sydney.edu.au
Australian Centre for Field Robotics
The University of Sydney
Eduardo Nebot
eduardo.nebot@sydney.edu.au
Australian Centre for Field Robotics
The University of Sydney
ABSTRACT
The rise of autonomous systems in cities, such as automated vehi-
cles (AVs), requires new approaches for prototyping and evaluating
how people interact with those systems through context-based user
interfaces, such as external human-machine interfaces (eHMIs). In
this paper, we present a comparative study of three prototype rep-
resentations (real-world VR, computer-generated VR, real-world
video) of an eHMI in a mixed-methods study with 42 participants.
Quantitative results show that while the real-world VR representa-
tion results in higher sense of presence, no signicant dierences
in user experience and trust towards the AV itself were found. How-
ever, interview data shows that participants focused on dierent
experiential and perceptual aspects in each of the prototype repre-
sentations. These dierences are linked to spatial awareness and
perceived realism of the AV behaviour and its context, aecting in
turn how participants assess trust and the eHMI. The paper oers
guidelines for prototyping and evaluating context-based interfaces
through simulations.
CCS CONCEPTS
Human-centered computing HCI design and evaluation
methods.
KEYWORDS
prototyping, virtual reality, user studies, prototype representation,
automated vehicles, human-machine interfaces
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CHI ’21, May 8–13, 2021, Yokohama, Japan
©2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8096-6/21/05. . . $15.00
https://doi.org/10.1145/3411764.3445159
ACM Reference Format:
Marius Hoggenmueller, Martin Tomitsch, Luke Hespanhol, Tram Thi Minh
Tran, Stewart Worrall, and Eduardo Nebot. 2021. Context-Based Interface
Prototyping: Understanding the Eect of Prototype Representation on User
Feedback. In CHI Conference on Human Factors in Computing Systems (CHI
’21), May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 14 pages.
https://doi.org/10.1145/3411764.3445159
1 INTRODUCTION
With the rise of autonomous systems and their application in every-
day products, the human-computer interaction (HCI) community
has turned its attention towards developing ways for supporting the
design of such systems. Within the context of cities, autonomous
systems promise to transform urban mobility and to automate
services [
40
]. Recent trials of automated vehicles (AVs) as early pro-
tagonists of autonomous systems in cities, have primarily focused
on making the technology work. However, a key for the successful
uptake of AVs is the careful consideration of trust, usability and user
experience, as found in a study on automated driving [
19
]. Within
an urban environment, this extends to the design of the external
human-machine interface (eHMI) that AVs use to communicate
their internal state and their intent to pedestrians [14, 39, 46].
Prototyping and evaluating eHMIs with prospective users in
urban environments is extremely challenging, as it is associated
with high costs of real-world prototypes (e.g. a self-driving car)
and potential risks to participants. To address these challenges,
HCI researchers have turned to using various simulation platforms
and prototype representations that allow them to simulate eHMI
concepts in a lab environment [
10
,
13
]. This includes the use of video
recordings to study pedestrian interactions with an eHMI [
24
,
54
]
and computer-generated (CG) prototypes in a virtual environment
(VR) to evaluate how pedestrians would cross in front of an AV
equipped with an eHMI [9].
Previous simulation studies have primarily focused on prototyp-
ing and evaluating specic interface concepts (e.g. [
20
]), assessing
how participants experience the simulation (e.g. [
18
]) and compar-
ing the sense of presence across CG and real-world representations
(e.g. [
61
]). To our knowledge, no studies have been carried out to
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
date to investigate in what ways dierent prototype representations
aect how participants provide feedback on the prototype itself. To
address this gap, we implemented a mixed-methods study in which
we compared three prototype representations: real-world VR, CG
VR and real-world video. As a case study, we chose a ride-sharing
scenario (captured from the perspective of a pedestrian waiting for
their vehicle to arrive) in a shared urban environment, where pedes-
trians, cyclists and maintenance vehicles share the same road. We
chose this scenario as previous research has found that people con-
sider interactions with AVs more important in shared environments
[
52
]. Our research team involved interaction designers, urbanists
and engineers, which allowed us to take a holistic approach to
designing the AV prototype and the scenario used in our study.
To that end, we used a fully functional AV that was specically
designed for a shared environment and equipped with an eHMI in
the form of a low-resolution display.
The paper makes three contributions to the eld within HCI
that is concerned with the design of human-machine interfaces for
autonomous systems. (1) It presents the rst comparative study of
dierent simulation approaches for evaluating eHMIs from a pedes-
trian perspective. (2) It provides empirically based insights on what
participants focus on when assessing trust and user experience
across real-world VR, CG VR and real-world video prototype repre-
sentations. (3) It oers guidelines for how to create context-based
interface prototypes for lab-based evaluation studies.
2 RELATED WORK
Within the broader context of autonomous systems, this paper
specically draws on and contributes to (1) the design of eHMIs, (2)
prototyping approaches and the simulation of interactions between
people and AVs, and (3) studies of simulation platforms.
2.1 External Human-Machine Interfaces
Being designed to communicate the system’s awareness and in-
tent, eHMI concepts include projection-based solutions [
44
] and
displays attached to the vehicle [
17
], thereby supporting various
communication modalities, from abstract [
15
] to symbolic [
25
] to
textual [
3
]. The study reported in this paper contributes to this eld
through the systematic investigation of the various prototyping
representations that are currently available to evaluate eHMIs and
other context-based interfaces for autonomous systems (e.g. mobile
robots, drones) in complex urban environments.
2.2 Prototyping and Simulation
The creation of prototypes is an integral part of a human-centred
design process [
8
] and can full various purposes; for example,
prototypes are often used to evaluate certain aspects of a design
with users, before further development stages commence [
6
]. Lim
et al. [
37
] highlight the importance of understanding the funda-
mental characteristics of prototypes and the careful selection of
representational forms, prototyping materials and resolutions, as
these inuence the judgement of a target design concept.
Given the complexity, cost and potential risk to participants,
associated with designing and evaluating interfaces for and interac-
tions with autonomous systems, researchers have turned to a wide
range of methods and techniques, such as Wizard of Oz, video and
simulation prototyping [
48
]. In particular, CG VR has been found
to be a promising approach for simulating autonomous systems
and their interfaces in a safe environment [
43
]. CG VR allows for
assessing the user experience (UX) of an interaction in a contextual
environment [
50
] while increasing controllability and reproducibil-
ity [
12
]. Research on pedestrian safety has further demonstrated
that participant behaviour in CG VR matches real-world norms
and that participants found the VR environment to be realistic and
engaging [
13
,
41
]. Simulations also have the advantage of allowing
for rapid prototyping approaches, as various interface elements can
be quickly exchanged and evaluated [
1
,
20
]. Simulation studies are
not limited to CG environments with some studies employing video
[
24
,
54
] or 360-degree video based VR [
7
,
20
,
45
,
61
] as a way to
simulate the experience of interacting with real-world prototypes.
When it comes to the evaluation of context-based interfaces, it
is important that the simulated environment oers realistic experi-
ences and provokes similar user behaviours to those observed in
the real world. Here, results from similar HCI research domains are
promising; for example, Mækelæ et al. have reported that in the
area of public display research, they were able to observe similar
user behaviour in virtual compared to real-world settings [
42
]. They
therefore propose virtual eld studies as an alternative to real-world
studies, oering similar ecological validity but at a reduced eort.
A key purpose of prototypes is to collect feedback from prospec-
tive users. To that end, Pettersson et al. [
49
] found that the overall
user experience was similar when comparing in-vehicle systems
in VR and in the eld, but that participants provided less feedback
in VR. They further observed that users had diculties to separate
judgements about the evaluated prototype and the system through
which the prototype is presented. Similar ndings were reported by
Voit et al. for the evaluation of smart artefacts [
59
]; besides dier-
ences in reported feedback, they also found that evaluation methods
can inuence study results. This paper sheds further light on how
user feedback varies across dierent prototype representations.
2.3 Simulation Platforms
Regardless of the simulation platform being used, i.e. CAVE-like se-
tups [
18
], screen-based driving simulators or VR headsets, a major
consideration in the development of simulator platforms is to oer
users a high sense of presence [
35
]. This can be achieved through
various measures, such as increasing interaction delity [
51
], mo-
tion delity [
61
] and oering a high visual realism [
57
]. Previous
research has shown that higher visual realism enhances realistic
response in an immersive environment [53].
To that end, 360-degree video is a promising alternative to CG,
as it results in higher perceived delity and presence compared
to CG simulations [
61
]. In addition to the higher visual realism,
users’ sense of presence also benets from the familiarity with the
environment when using immersive real-world videos [
20
]. Impor-
tantly, real-world video is able to represent not only the prototype
but also the context at a high level of delity [
18
], which is com-
prised of various elements, such as audio-visual impressions, the
physical environment and the presence of other people and the
user’s relationship with them [
29
]. These elements might inuence
how participants experience an eHMI in a simulated situation [
30
].
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
The recent uptake in research on prototyping strategies for
eHMIs within the HCI community points to VR and 360-degree
video simulations as competing emerging trends. Yet, despite the
many promising concepts, the complexity of the context under in-
vestigation means that more work is required to further understand
the inherent qualities of those prototyping representations. Pre-
vious work has highlighted the importance of understanding the
fundamental characteristics of physical prototypes in the context
of interactive products [
37
]. To the best of our knowledge, charac-
teristics of emerging prototype representations, such as 360-degree
video simulations and VR, in relation to increasingly important
outcome variables, such as system trust, have not yet been sys-
tematically studied. In particular, a systematic evaluation of the
eect of dierent prototype representations on user feedback –
and therefore study results – is still lacking. This study represents
a rst attempt to address this gap, which we argue will not only
inform research on and the design of AVs, but also other categories
of urban technologies and autonomous systems, such as robotic
interfaces [56] and pulverised displays [22].
3 EVALUATION STUDY
Building on previous work and to address the gap identied in
the review of previous studies, we set out to investigate how user
feedback varies across dierent prototype representations. Rather
than evaluating a specic eHMI, our aim was to understand the
factors that inuence user feedback on eHMIs. This aim follows the
trajectory from early work in HCI that reported on dierences in
user feedback when evaluating paper versus interactive prototypes
[
38
,
60
]. As previous studies of VR simulations have found sense
of presence to be an important factor, we formulated our rst re-
search question (RQ1) to measure sense of presence for each of the
prototype representations: How does the prototype representation
aect user’s sense of presence?.
The subsequent two research questions that drove our study
design were formulated to measure specic user feedback sought
when evaluating human-machine interfaces, with previous studies
highlighting trust and UX as important aspects [
19
,
28
,
31
]. Thus,
the second research question (RQ2) was How does the prototype
representation aect perceived user’s trust in the eHMI? and the third
question (RQ3) was How does the prototype representation aect the
perceived UX of the eHMI?.
3.1 Study Design
We adopted a between-subject approach for the gathering of quan-
titative data to assess sense of presence, trust and user experience,
thus reducing learning eects and avoiding carryover eects from
repeated measures. To that end, we balanced the distribution of
participants across the three prototype representations. After expe-
riencing the assigned prototype representation, participants were
asked to complete a set of questionnaires and to partake in a semi-
structured interview. This was followed by participants experienc-
ing the same scenario in the remaining two prototype represen-
tations. At the conclusion of the study, participants took part in
a second semi-structured interview. This approach was chosen to
allow participants to compare their perceived sense of trust and
UX across all three representations.
Figure 1: Recording setup for the immersive 360-degree real-
world video prototype representation.
3.2 Prototype Representations
We opted to compare real-world VR (referred to as RW-VR), computer-
generated VR (CG-VR) and real-world video (RW-Video), hence
adopting two simulation platforms (VR and video). RW-VR is in-
creasingly used in simulation studies given that 360-degree cameras
are becoming more aordable and widely available [
27
], and due
to the higher level of delity of real-world video [
18
]. CG-VR is a
commonly used representation in pedestrian-AV safety research
(e.g. [
13
,
43
]). RW-Video was included as video prototypes can be
useful when evaluating context-based interfaces online [
15
]. Video
prototypes are further less complex and lower-cost in terms of the
evaluation setup.
The eHMI and the trajectories were the same for all three pro-
totype representations in terms of depicted eHMI hardware (i.e.
resolution; display technology), displayed content (i.e. light pat-
terns) and context (i.e. location and time when a specic vehicle
behaviour and light pattern was triggered). Dierences would only
occur due to the inherent nature of the prototype representation,
whose eects on study results were part of the investigation.
3.2.1 RW-VR. For creating the RW-VR prototype representation,
we worked in close collaboration with researchers from urbanism
and from the engineering department of our university. We used a
fully functional AV developed by the engineering department as a
cooperative autonomous electric vehicle (CAV) platform [
2
] with
hardware designed by AEV Robotics
1
. The vehicles - being small,
ecient and electrically powered - were designed for the purpose
to operate safely in low speed road environments (under 40kph)
and in shared environments where the vehicles would be operating
in close proximity to pedestrians [
47
,
55
]. The platforms have the
sensing and computation capacity to eventually operate at level
5 as dened by the Society for Automotive Engineers (SAE) for
autonomous driving. The system is based on the robot operating
system (ROS) which is a middleware for robotic platforms that
enables and promotes modular system design.
For the purpose of this study, we designed a low-resolution
(low-res) lighting display functioning as an eHMI to communicate
the shared AV’s intent and awareness, as well as enabling users to
identify their car, following recommendations from previous studies
1https://aevrobotics.com/, last accessed September 2020
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
[
4
,
46
]. The display consisted of LED strips installed on three sides
of the front window as shown in Figure 1. The LED strips featured
a pitch of 60 pixels per meter, resulting in a total of 145 LEDs. To
improve the viewing angle and to create the illusion of a light bar
(rather than a distinct set of point light sources), a diuser tube
of opal white acrylic was added. The LEDs were controlled via an
Arduino board, which was connected to the system of the vehicle.
A python ROS node was constructed that read the information
from the vehicle state by subscribing to the relevant information.
All light patterns were triggered in real-time based on the sensed
information (awareness) and the state of the AV platform (intent).
We developed a set of light patterns to demonstrate the usage of
an eHMI interface for shared AV services following a user-centred
design process supported through a purpose-built prototyping
toolkit (involving workshops with 14 experts) [
23
]. We do not dive
deeper into the design of the eHMI itself here, as this is not the
focus of the contributions reported in this paper. The nal sequence
of light patterns along with the scenes used in the prototype rep-
resentation are depicted in Figure 2. In total, we recorded three
scenes to demonstrate the eHMI interface in a shared AV scenario.
We staged and recorded the scenes in a shared environment (one
of our university’s main avenues) with an Insta360 Pro 2
2
camera,
which can record 360-degree panorama videos in 8K 3D. The scenes
(represented from the perspective of the study participant) included:
(1) The AV passing through the shared environment without any
staged interactions with pedestrians. (2) The AV pulling over and
picking up another pedestrian (Actor 2 in Figure 2). (3) The AV in-
dicating to pull over to the camera stand. In this trajectory, another
pedestrian (Actor 3 in Figure 2) forces the AV to slow down and
stop, demonstrating how a pedestrian safely crosses in front of the
AV. An additional person was placed directly behind the camera in
all three scenes (Actor 1 in Figure 2), giving the appearance of an-
other rider waiting for their own shared AVs. This was to constraint
participants’ movement in the simulation, as 360-degree video does
not allow for motion when imported into VR.
All three scenes were recorded with the same AV and therefore
recorded consecutively. The vehicle was operating based on pre-
computed trajectories that mimicked the desired vehicle behaviour
for the purpose of recording the 360-degree video. The vehicle
was operating a ‘virtual bumper’ which is a system that detects
obstacles in (or adjacent to) the proposed vehicle trajectory and
reduces the speed based on a time-to-collision calculation. Due
to safety regulations, a licensed operator had to sit in the AV –
in case of having to manually bring the AV to a halt. However,
for the purpose of the recordings, we were able to remove the
steering wheel, thus conveying clearly to participants that the car
was driving autonomously.
After recording the scenes with the 360-degree camera, we used
Adobe Premiere and Adobe After Eects for post-processing. As
we recorded the scenes in early evening hours for better visibility
of the low-res lighting display, we had to apply the Neat Video
3
lter to reduce image noise, while still preserving ne details, such
as people’s faces. We then combined the three scenes, added a short
blend transition between them, and exported them into a single 3D
2https://www.insta360.com/product/insta360-pro, last accessed September 2020
3https://www.neatvideo.com/, last accessed September 2020
entering
area
leaving
area
S3,
Actor 3
S2,
Actor 2
360-degree
camera S1-3,
Cameraman
(Actor 1)
AV
Scene 1 (S1): 45 sec.
Scene 2 (S2): 57 sec.
Scene 3 (S3): 37 sec.
Recording Sequence
Scenes
Low-resolution lighting eHMI
L2
L2
S1
S2
S3
L4
L1
L3
L2
L1
L2
L3
L1
L2 Light band sweeping to the left / right to indicate pulling over
Light band on the bottom constantly pulsing to indicate slow speed autonomous operation
L3 All LEDs uniformly pulsing to indicate pedestrian to get on the car
L4 All LEDs ashing to make pedestrian aware of stepping into the operational radius
Figure 2: Scenes (S1-3) and trajectories for the 360-degree
video recording and the light patterns (L1-L4) used in the
AV’s eHMI for the various simulated interactions. Colours
in the trajectories represent the colour encoding, which has
been used for riders to identify their vehicle (i.e. blue for Ac-
tor 2, purple for the user experiencing the prototype in VR).
over-under video le. To experience the stereoscopic 3D 360-degree
video with a VR headset (HTC Vive), we imported the video le
into Unity and applied it as a render texture on a skybox material.
To convey the immersive audio recording of the scene soundscape
and increase a sense of presence, we used stereo headphones.
3.2.2 CG-VR. To create the same three scenes from the RW-VR
in the CG-VR, we commissioned a 3D simulation designer with a
background in interaction design and a specialisation in 3D mod-
elling and creating immersive virtual products with more than 8
years of professional experience. We provided an overview of the
scene recordings (similar to Figure 2), the 360-degree video from the
RW-VR as a reference as well as a building information model (BIM)
of the university campus avenue. The designer further conducted
several visits to the physical site to better assess the dimensions and
proportions of the shared space environment and the surrounding
buildings. For the design of the AV, we provided the 3D designer
with technical drawings, photographs and videos of the actual AV.
The car model was created in Autodesk 3ds Max, using emissive
materials for the low-res lighting display in order to replicate the
lighting eects and aesthetics as realistically as possible.The car
model and the low-res lighting display were then animated in Unity.
We deliberately decided against using an existing autonomous driv-
ing simulator with a sensor suite (e.g. Carla [
16
]), as Unity has been
used for the majority of eHMI research and provides more exibility
for designing and prototyping customised context-based interfaces
and the surrounding environment. For creating the actors and sur-
rounding pedestrians from the 360-degree video, models from a
library providing 3D scanned people
4
, were used and customised
for our scenario. Throughout the design process, we arranged sev-
eral meetings with the 3D designer and also tested the prototype
4https://renderpeople.com/, last accessed September 2020
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
in VR. Through this iterative approach, changes were made to the
atmospheric lighting of the scene and interactions of pedestrians
with the AV were adjusted to match the details from the 360-degree
video. For the experiment we used the same VR headset as for the
RW-VR simulation. We imported the immersive audio recording to
reduce any eects of sound as a potentially confounding variable.
3.2.3 RW-Video. For the real-world video prototype representa-
tion, we used the previously recorded and post-processed 360-
degree video as a source. We used Adobe After Eects to map
the equirectangular into a 2D rectilinear video projection. In order
to highlight the rst-person nature of the experience (as opposed
to having the participant just passively watching the video as a
passer-by in the environment), we animated the viewing angle of
the video to ensure that the AV was always in the centre of the im-
age. Thus, if the AV was driving out of the scene, the camera would
follow its trajectory as if the participant were waiting for their own
car. We exported the nal video as a 1080p resolution, 16 to 9 video
le. For the experiment, we displayed the video in full-screen mode
on a 24-inch monitor. As in the VR prototype representations, we
also used the same stereo headphones to convey the immersive
audio soundscape.
3.3 Participants
We re cruited 42 participants (22 male, 20 female) between the ages of
21 and 57 (M=32.05,SD=9.13). Participants were recruited from our
university’s mailing lists, yers and social networks; all participants
voluntarily took part in the experiment and initial contact had to
be made by them, following the study protocol approved by our
university’s human research ethics committee. Participants were
randomly assigned to one of the three conditions to start with; the
two remaining conditions that participants experienced before the
post-study interview were counterbalanced. Further, we balanced
participants’ age, gender and previous experience in VR across the
three prototype representations with the help of an online screening
questionnaire that we sent to participants prior to the experiment.
Table 1 shows participant characteristics for each of the conditions
that they experienced rst.
3.4 Study Procedure
Upon arriving in our lab, participants received a short introduction
about the research topic on shared AVs and eHMIs. We informed
participants that the aim of our study was to evaluate trust and
Table 1: Number, gender, age and previous VR experience of
participants for each prototype representation.
RW-VR CG-VR RW-Video
n (m/f) 14 (7/7) 14 (7/7) 14 (8/6)
Age M=31.42 M=32.57 M=32.42
SD=8.6 SD=10.38 SD=8.48
Prev. VR-Exp.
never 3 3 3
less than 5 times 8 9 6
more than 5 times 3 2 5
UX for eHMIs in a shared AV scenario. We did not mention the
comparison of representations in order to avoid biases towards the
questionnaire and the interview that we conducted after the rst
experienced prototype representation. We then asked participants
to ll out the consent form to take part in the study, followed
by a short questionnaire to collect data on demographics. Before
experiencing the rst prototype representation, we shortly briefed
participants about the scenario they would experience, following
advice from previous work suggesting that providing users with a
meaningful narrative context increases their inner presence [
21
].
To further immerse them into the scenario of waiting for a shared
AV, we presented them with a mock-up interface on a mobile phone.
The interface followed the layout of existing ride-sharing services
and displayed: (a) a map of the location where the participants were
supposed to wait for their vehicle, (b) the vehicle’s current position
approximately 2 minutes away from the participant, (c) the colour
which was assigned by the system for the participant to recognise
their vehicle (in this case purple), and (d) the mock user prole of
the person whom they would share the vehicle with.
After experiencing the rst prototype representation, partici-
pants were asked to complete a set of standardised questionnaires,
which took between 9 to 13 minutes. In a next step, we conducted
a semi-structured interview (M=7min 43sec, SD=2min 54sec). After
consecutively experiencing the two remaining prototype repre-
sentations, we conducted a semi-structured post-study interview
(M=9min 34sec, SD=2min 53sec).
The duration of each experienced scenario was 2 minutes and
19 seconds (same duration for each condition). We chose this time
frame carefully based on initial tests within the team, ensuring that
there was sucient time for participants to get familiar with the
context and to adjust to the immersive experience, yet short enough
to avoid fatigue. The whole study took approximately 45 minutes
for participants to complete. We informed participants that they
could stop the experiment at any time, for example, should they
experience motion sickness, however, none of the participants had
to stop the experiment. The conditions and study procedure are
illustrated in Figure 3.
3.5 Data Collection
Throughout the experiment we collected both quantitative and
qualitative data, following a mixed-methods approach [
11
]. In the
following we provide an overview of our data collection. We present
the questionnaires in the same order as participants were asked to
complete them during the experiment.
3.5.1 estionnaires. In order to measure participants’ subjective
perception of trust towards the AV, we used a standardised trust
scale that was designed for the measurement of trust in autonomous
systems [
26
]. The questionnaire which has been widely used in the
context of research on autonomous vehicles [
19
,
49
] consists of two
subscales to calculate an overall trust score (7 items) and an overall
distrust score (5 items); all items correspond to 7-point Likert scales.
We instructed participants to assess trust by considering the AV as
a single system and based on how they experienced the AV in the
presented scenario.
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
Figure 3: Frames from the RW-VR and CG-VR simulations and the RW-Video setup used as conditions in the study (top). Study
procedure showing the way participants experienced each of the prototype representations and the data collected (bottom).
To assess participants’ UX of the eHMI, we used the UEQ ques-
tionnaire [
33
]. The questionnaire consists of 26 bipolar items (7-
stage scale from -3 to +3) to calculate 6 UEQ subscales: attractiveness
(overall impression of the product), perspicuity (how easy it is to get
familiar with the product), eciency (solving tasks without unnec-
essary eort), dependability (feeling in control), stimulation (how
exciting and motivating it is to use the product), and novelty (how
innovative and creative the product is). For the UX questionnaire,
we instructed participants to consider the low-res lighting interface
of the AV as experienced in the presented scenario.
To assess participants’ media experience and sense of presence
in the three prototype representations, we employed the ITC-Sense
of Presence Inventory (ITC-SOPI) [
35
]. The questionnaire is well
established to compare sense of presence across a wide range of
media systems, and has been previously used for comparing semi-
autonomous driving systems in VR and in the eld [
49
]. The ques-
tionnaire consists of 38-items (5-point Likert) to calculate 4 sub-
scales: spatial presence (assessing the sensation of being in a dis-
played environment), engagement (measuring the intensity of the
experience and feeling of being involved), ecological validity (natu-
ralism of the displayed environment and sensation that displayed
objects are solid), negative eects (assessing potential negative ef-
fects such as motion sickness).
3.5.2 Interviews. We collected qualitative data in the form of semi-
structured interviews. In the rst round of interviews, conducted
after participants experienced the rst prototype representation
and following the questionnaires, we asked questions about (1)
understanding of the light patterns, (2) trust towards the vehicle
and (3) comments on the experience. In the post-study interview,
conducted after participants experienced the remaining two pre-
sentations, we asked questions about (1) dierences between the
three prototype representations in terms of their experience, (2)
whether experiencing the remaining two presentations changed
participants’ perceived trust towards the AV and (3) perception and
understanding of the lighting display.
3.6 Data Analysis
3.6.1 estionnaires. We rst conducted a descriptive analysis of
our questionnaire data to obtain an overview about the relationship
between each predictor and the outcome domain variable. Thus, we
calculated means and standard deviations after an internal reliabil-
ity assessment of the scales calculating Cronbach’s alpha. Overall
internal reliability was excellent for both trust subscales (
𝛼
>= 0.9).
For the UEQ questionnaire, item reliability was acceptable for e-
ciency,stimulation and novelty (
𝛼
> 0.7), and good for attractiveness,
perspicuity and eciency (
𝛼
> 0.8). For the ICT-SOPI, overall inter-
nal reliability was excellent for spatial presence and engagement
(
𝛼
> 0.9), good for negative eects (
𝛼
= 0.83), and acceptable for
ecological validity (𝛼= 0.71).
We conducted a univariate analysis of variance (ANOVA) for
each outcome domain of the questionnaires. We used side-by-side
box plots to assess if the data was approximately normally dis-
tributed. In case of normal distribution, we calculated one-way
ANOVA, otherwise the Kruskal-Wallis rank sum test was utilised.
In case of signicant dierences, we performed post-hoc tests using
Benjamin-Hochberg (BH)-corrected p-values.
3.6.2 Interviews. All interviews were transcribed by a professional
transcription service. Two coders worked collaboratively to analyse
the data from both interviews. However, each coder started the anal-
ysis with a dierent set of interviews and independently developed
the codebook. We reviewed each other’s codebooks afterwards to
discuss the dierence and made adjustments where needed.
The data from the post-study interview was used to assess partic-
ipants’ preferences and to identify the reasons for those preferences
as well as for changes in terms of their perceived trust towards the
AV. These data were analysed following a deductive thematic anal-
ysis approach [
5
], using a digital whiteboard with sticky notes. The
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
Table 2: Mean and standard deviations for ITC-SOPI ratings
for the three prototype representations (max=5, min=1).
RW-VR CG-VR RW-Video p-value
(M / SD) (M / SD) (M / SD)
Spatial Pres. 3.15 / 0.56 3.21 / 0.43 2.32 / 0.96 <0.01
Engagement 3.93 / 0.60 3.91 / 0.52 2.84 / 0.64 <0.001
Ecol. Val. 4.4 / 0.50 3.84 / 0.60 3.74 / 0.59 <0.01
Neg. Eects 1.82 / 1.00 1.48 / 0.40 1.52 / 0.44 0.369
identied themes were used to structure the Discussion section. To
further illustrate specic observations around the identied themes,
relevant quotes were selected from the rst interview.
The data from the rst interview was used to assess perceived
trust towards AV and user experience of the lighting system. Using
the same analysis approach with the post-study interview, we iden-
tied key aspects of trust and user experience that changed with
prototype representations. Explanation of these changes, however,
were found mainly in the analysis of the post-study interview.
4 RESULTS
4.1 Sense of Presence (RQ1)
4.1.1 ITC-SOPI. Results of the ITC-SOPI (see Table 2) show above
middle rating sense of presence for RW-VR and CG-VR, and be-
low middle rating for RW-Video. Engagement ratings are high for
RW-VR and CG-VR, and slightly above middle rating for RW-Video.
Ecological validity scale is high for CG-VR and RW-Video and very
high for RW-VR. Negative eects are low for all three prototype
representations, with slightly higher ratings for RW-VR. Univariate
ANOVA found no signicant main eect of prototype represen-
tation on negative eects (F(2, 37) = 1.023, p = 0.369). However, a
signicant main eect of prototype representation was found for
spatial presence (F(2, 39) = 7.258, p < 0.01), engagement (F(2, 39) =
15.77, p < 0.001) and ecological validity (F(2, 39) = 5.424, p < 0.01).
For spatial presence, post-hoc tests revealed signicant dierences
between RW-VR and RW-Video, as well as CG-VR and RW-Video
(both with p < 0.01). For the engagement scale, post-hoc tests re-
vealed signicant dierences between RW-VR and RW-Video, as
well as CG-VR and RW-Video (both with p < 0.001). Finally, for
ecological validity, post-hoc tests revealed signicant dierences
between RW-VR and CG-VR, and also RW-VR and RW-Video (both
with p < 0.05).
4.1.2 alitative Feedback. In terms of the media experience (i.e.
how the prototype was presented), the post-study interviews showed
that RW-VR was favoured by the majority of participants (n=30),
followed by CG-VR (n=5) and RW-Video (n=1). There were 4 partic-
ipants who favoured both VR representations and 2 participants did
not have a preference. Being the least immersive, the RW-Video was
perceived by many participants as ‘boring’ (P10, P28, P37). Most
participants felt like they were ‘watching’ a video (n=9) and were
not really ‘being there’ in the scene (n=8). As P34 stated, ‘you are not
present at that particular place, so you are distancing yourself from
the actual situation’. Henceforward, we discuss two predominant
themes from our thematic analysis in more detail:
(1) Visual realism: Between the two VR simulations, participants
commented positively on RW-VR due to the higher realism of the
presented environment. The RW-VR prototype representation al-
lowed participants to ‘naturally [...] step in that environment’ (P8)
and ‘not [get] distracted by the novelty of being in a virtual world’
(P24). The real-world environment made it easy for participants to
quickly understand the scenario, which P18 related to a perceived
reduction of cognitive load: ‘[...] because it is so much more realistic,
your brain doesn’t have to do the work to try to create the picture
and make sense of it, which means you can actually focus on the
aspects of the car and what it does and the way it communicates’.
Participants who experienced the RW-VR also mentioned in the
rst interviews that they were impressed by the high realism (n=6).
Two participants stated in this regard that they ‘haven’t experienced
something that well put together in VR’ (P5) and were rather ex-
pecting a representation that is ‘cartoon’-like (P5) or ‘game’-like
(P21). Thirteen participants explicitly stated that the CG-VR felt
‘more like a game’ and that it ‘seemed weird and somehow detached
from reality’ (P27). The subjective experience of being present was
not as strong, as reported by P33: ‘I felt like I was injected into a
scene’. Some participants mentioned that they were ‘distracted by’
(P18, P24, P33) or ‘focused on’ (P15, P26) the imperfections in the
simulated environment: ‘I was thinking a lot about how this com-
puter world was created. I was just looking at the patterns on the trees
and looking at the movement of [people]’ (P15). P7 mentioned an
interesting aspect about feeling related to other people within an
immersive scene: ‘it’s easier to relate to an image of a real human
than to an avatar’. For the CG-VR she would have expected to ‘see
[her] hands like an avatar hand as well’, so she could see herself as
one of the people there and connect with them.
(2) Interaction delity: In the RW-VR prototype representation, par-
ticipants were able to look around the environment but could not
move around as naturally as in CG-VR. Motion sickness might be
experienced by those who tried to walk a few steps, as pointed
out by P18: ‘If you move, but the picture does not move accordingly,
your brain will make you sick’. Six participants noticed the nature
of 360-degree video and did not attempt to move: ‘I felt like I was on
a xed camera stand’ (P37). Five participants said that they did not
feel it was possible to interact, because things were ‘too realistic’
(P16) or had ‘already been happening’ (P29). Despite the dierence
in terms of interactivity, the number of participants who felt the
urge to respond physically to the AV was the same for both RW-VR
(n=5) and CG-VR (n=5). P31 asked the researcher if she could walk
to the vehicle and ‘actually sit there’. P35 raised a similar point:
‘when it arrived for me I would’ve liked to walk up to it’, express-
ing the desire to explore the experience holistically, including to
commute in a shared AV. In the CG-VR prototype representation,
participants (n=6) thought that interaction was possible since things
were ‘rendered’ and ‘it can take other inputs’ (P37). Two participants
suggested the potential impact of interactivity on feelings of immer-
sion. P34, for example, said: ‘if there was a possibility of interaction
[. . .] like going in front of the car and it stopped, then I would say that
the second prototype [CG-VR] will be much more immersive than the
third one [RW-VR]’.
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
Table 3: Mean and standard deviations (SD) for trust scale for
the three prototype representations (max=7, min=1).
RW-VR CG-VR RW-Video p-value
(M / SD) (M / SD) (M / SD)
Trust 4.98 / 0.75 4.64 / 1.25 4.37 / 1.29 0.55
Distrust 2.01 / 0.93 2.38 / 1.41 2.65 / 1.6 0.54
4.2 Trust (RQ2)
4.2.1 Trust Scale. Descriptive data analysis of the subjective trust
ratings [
26
] show that participants’ trust towards the AV was higher
for the VR representations than for the non-immersive RW-Video,
with highest trust in RW-VR (see Table 3). Conversely, participants’
distrust towards the AV was higher for RW-Video than for RW-VR
or CG-VR, with lowest distrust in the RW-VR. Yet, no statistically
signicant dierence could be found. That said, RW-VR had the
lowest standard variations for both trust and distrust, indicating a
greater consensus around the responses for RW-VR.
4.2.2 Motivators of Trust. To better understand why participants
generally trusted the vehicle (with low distrust across all three
conditions), we coded the sections of the rst interviews where
we asked about reasons to trust. This allowed us to investigate the
immediate responses given by the participants, considering which
aspects could have positively inuenced their trust. The codes
and frequencies show that they primarily assessed trustworthiness
based on the behaviour displayed by the vehicle itself, with similar
frequencies veried among the three prototype representations.
That is reasonable, given that the vehicle behaviour was identical
across all representations. For example, participants reported that
seeing the vehicle stopping for other pedestrians in the scenario
‘reinforced’ their trust (n=12, RW-VR=4, CG-VR=3, RW-Video=5).
P21 mentioned that ‘after seeing [the vehicle] stop for someone, it
wasn’t too scary anymore’ (RW-VR). Others stated that seeing the
vehicle ‘safely’ picking up another person in a previous scene (RW-
VR=3) and then stopping in a safe distance from the participant
(n=2, RW-VR=1, CG-VR=1) strengthened their sense of trust. The
low speed of the vehicle was another reason that reinforced trust
(n=6, RW-VR=3, CG-VR=1, RW-Video=2), among other contextual
factors. For example, ve participants stated that the light patterns
communicated from the vehicle were the main driver for trusting it
(RW-VR=1, CG-VR=3, RW-Video=1), while one participant (P3, RW-
VR) stated that ‘the passengers in the car looked quite relaxed’, which
made her feel ‘more trusting’. Finally, three participants related
their trust to a general preexisting condence in technology and
autonomous systems (RW-VR=1, CG-VR=1, RW-Video=1).
4.2.3 Comparisons Between Prototype Representations. After ex-
periencing all three prototype representations, roughly a quarter
of the participants reported in the post-study interview that their
subjective perception of trust towards the vehicle had not changed
(n=11). These participants mentioned that the vehicle’s driving
behaviour in the presented scenarios was similar (e.g. in terms
of speed, slowing down and communicating with pedestrians), so
‘it doesn’t matter what kind of platform to use [for representation]’
(P32). However, more than a third of the participants stated that
they trusted the AV more in the RW representations (n=15), with 11
of them explicitly reporting a higher perception of trust in RW-VR.
Additionally, 4 participants stated having had less trust towards the
AV in CG-VR, whereas 2 participants felt ‘more trustworthy’ (P11)
or expressed to ‘feel more safe’ (P28) in CG-VR. Two participants
stated that they experienced higher trust in both of the VR proto-
type representations, whereas one participant stated the opposite.
The rest of the participants expressed diculties to compare their
perception of trust, feeling they have learned the interface after
being exposed to the AV interactions multiple times (n=2). Others
explicitly stated that their trust towards the situation changed, but
that this did not inuence their trust towards the car (n=2).
To identify factors inuencing participants’ perception of trust,
we included a question to that end in the post-study interview, ag-
gregating responses into the high-level categories presented below:
(1) Spatial awareness: We found that participants’ spatial awareness
inuenced their trust towards the AV to some degree (n=2). For
example, P11 voiced the diculty in assessing trust in RW-Video:
‘it didn’t engage me enough to have any feelings about it, it was very
distant’. We further found that participants had better perception
of space, distance and speed in the VR representations (n=6). Be-
ing immersed in VR, they ‘felt more’ (P27), had ‘a closer view of
what could happen’ (P34) and noticed ‘more details’ (P31). Both
distance between objects, and between participants and the vehicle,
were better estimated in RW-VR and CG-VR as ‘your whole body
is within that environment, so you see the sizes of things, (P11) and
‘everything was at a certain scale’ (P26). These dierences in spatial
awareness prompted dierent emotional and behavioural reactions.
For example, P27 became more concerned about the inattentive
pedestrian: ‘When I watched the video, I thought that car can just
gently bump into the pedestrian, it’s not a problem. When I saw it in
VR, in 360-degree video, that would not have been a good idea’. P26
grew more aware of the vehicle’s trajectory: ‘I felt like where I was
standing I was in its way and I wanted to step back out of [...] the
path of the vehicle’.
(2) Realism of vehicle behaviour: Participants associated their higher
level of trust towards the RW representations with a higher realism
of the depicted driving behaviour (n=6). For example, P4 stated that
the vehicle in CG-VR ‘felt more like fast and manic, and made [her]
feel more manic too’, which P27 related to the ‘sideways gliding’ of
the vehicle. P10 referred to the ‘stability’ of the actual vehicle seen
in RW-VR ‘that seemed a lot heavier [...] and stuck on the ground’,
and therefore ‘something [she] would jump on and trust’. Other
participants also mentioned that the stopping behaviour in CG-VR
felt less realistic and thus less trustworthy (n=5). For example, P38
observed that ‘[the vehicle] stopped already at some distance [as]
if it was by design to stop, not because it had seen the person there
[through a sensor]’. P22 mentioned in retrospect that for CG-VR
she was not sure ‘if what was being depicted, was what the vehicle
would be supposed to do’, because ‘you can do all sort of things [in
CG-VR]’. Conversely, she also added that due to the lower realism
she ‘allowed [the system] to be a bit wrong and still taking in what
it said it would do’. Participants reported that having seen in the
RW representations that the vehicle can safely operate in the real
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
world increased their level of trust (n=6). P1 explained, for example,
that the authenticity of the RW representations ‘gives you a real
behaviour of the code of the car’, whereas P5 was impressed that
‘the vehicle is out there operating in the real world, [...] you know, it’s
not a science ction movie - this is really happening’. P23 added in
this regard: ‘When it was just a simulation, it just feels like “this is
an idea but it’s not reality”, so I don’t think I would’ve had as much
trust in it compared to the [RW-VR]’.
(3) Realism of people and the environment: We found that the dier-
ent levels of realism in the depiction of people and the environment
in the RW representations compared to CG-VR inuenced the trust
of participants towards the situation (n=7). Two female participants
who experienced CG-VR as the initial representation stated in the
rst interview that they felt not very trustworthy towards the male
characters in the scene. P14 mentioned that she was wondering
‘what [her] relationship to that [...] man was meant to be’ and that ‘it
took a lot of [her] attention at the beginning’. Similarly, P18 stated
that ‘[she] was constantly looking at the guy next to [her] and at
some stage asked herself ‘what if I punch him?’. She explained this
reaction as a ‘ght or ight response’, which was likely triggered by
a slight uncanny valley aect and aggravated by the fact that the
animated character did not respond in any way to the participant
looking at the character. In the post-study interview, she referred
back to this observation, commenting on a dierent emotional re-
sponse when experiencing the RW-VR prototype representation:
‘I didn’t feel like I wanted to hit the people because [in the RW-VR
environment] they were understandable and they made sense’. Sim-
ilarly, P3 referred to the people in the RW-VR as (‘look[ing] quite
relaxed’), which she linked to her perceived level of trust. The lower
trust towards people in CG-VR was also inuenced by the envi-
ronment. For example, P14 mentioned that she instantly thought
that ‘this is a place [she] would not stand as a woman to wait for a
car’, whereas in regards to the RW-VR prototype representation
she mentioned that ‘this would be a situation I would be catching an
Uber’. The dierence in the feelings reported towards people and
the environment, however, was not matched by the perceptions of
trust towards the vehicle. For example, P13 stated: ‘I trust more the
situation [in RW-VR], but it doesn’t aect my feeling to the car’. Those
participants also reported that trust towards the overall experience
is more important for them. As explained by P18: ‘I trust the car,
but I don’t trust the other person [in the car]’.
4.3 User Experience (RQ3)
4.3.1 UEQ estionnaire. Figure 4 shows the results of our de-
scriptive data analysis of the UEQ scales across the three prototype
representations. We were not able to nd any signicant dierences
between the dierent prototype representations. In other words,
participants rated the UX of the eHMI similarly across CG-VR,
RW-VR and RW-Video.
4.3.2 Dierences in UX Feedback. The analysis of the data from
across both interviews revealed several aspects in relation to how
participants assessed the UX of the eHMI.
(1) Comprehension: There were several lighting patterns intro-
duced throughout the ride-sharing scenario. The analysis of the
rst interviews revealed that participants did not notice or pay
Figure 4: UX assessment of the eHMI across the three repre-
sentations, based on the UEQ questionnaire [33].
attention to all of them. For example, the pattern when the vehicle
was about to pull over was not often recalled upon when prompted
in the post-study interview. We found the lack of attention towards
the individual light patterns occurred more frequently in the VR
representations (RW-VR=10, CG-VR=10) compared to the video
representation (n=6). Similarly, the number of participants who
correctly interpreted multiple light patterns was lower in CG-VR
(n=6) and RW-VR (n=9) compared to RW-Video (n=12). Further, we
only found explicit statements from participants in CG-VR that they
were not able to understand the eHMI (n=3). Participants reasoned
in this regard that in CG-VR they were distracted by the virtual
depiction of people and environment (n=10). P10 stated that ‘every-
thing looks kind of funny you tend to look around a lot’. For P33, ‘the
presence of that guy standing next to [her] waiting was really disturb-
ing’. Meanwhile, in RW-VR participants would get preoccupied by
‘other things’ (n=8). For example, P25 stated that ‘because it was too
real, [she] focus[ed] on the surroundings, [...] and was looking at the
sky as well for a moment’. Another reason for distraction reported
was the focus on nding the assigned car (n=3). As P19 expressed:
‘I was actually just trying to concentrate on which vehicle was mine. I
didn’t think to look for any other additional information’.
(2) Light colours: The participants were briefed before the exper-
iment to wait for a car with the low-res light display showing a
purple colour, while other cars had their light displays in blue. The
analysis of the rst interviews revealed that issues with distinguish-
ing between the two colours was brought up by more participants
in RW-VR (n=13) than in the other two representations (CG-VR=4,
RW-Video=6). For example, they expressed confusion when a ‘seem-
ingly purple’ car did not stop to pick them up. In this regard, they
commented on the limitations of using colours for identifying a
ride-sharing AV, which would be aggravated by scalability. For
example, P19 stated: ‘If you’re talking about the use of colour as a
vehicle distinguisher, it really depends on how concentrated the use of
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
this type of vehicle is going to be. [...] imagine a crowd coming out
of a sports stadium, there’s not going to be enough colours’. Thus,
participants experiencing the RW-VR prototype representation sug-
gested the use of more unique combinations of colours, bespoke
expressive light patterns, and high-resolution text displays. The
reason why this was particularly critical in the RW-VR was that
participants could not clearly distinguish between blue and purple
from a distance. User feedback indicated a dierence in display
contrast between the two VR prototype representations. Half of the
participants (n=21) mentioned that in CG-VR things looked clearer,
‘more sharp’ (P30), and ‘more vivid’ (P30), compared to the natural
ambient light in the RW-VR (n=12). One participant pointed out
that in regards to the CG-VR: ‘[...] in reality the lighting display will
not be as noticeable and outstanding as in a [CG] simulation’ (P6).
(3) Contextual factors: A large number of participants also reected
in the rst interviews on experiential aspects beyond the eHMI
and the AV system, and thereby considered various situations they
might face if they were to use the AV in real life. The majority
of those statements were made by participants who experienced
the VR representations (n=22, RW-VR=7, CG-VR=11, RW-Video=4).
Nine participants expected extra cues, for example, via public dis-
plays in the environment or information displayed on their smart-
phones. P7 for example referred to displays at bus stops (‘ve min-
utes until your next bus’) explaining that the information could
enable her to ‘relax, sit down a bit, [...] have a drink, go to [have] a
bathroom break’. Participants also related to their personal habits
and experiences (n=4). P15 thought that being able to identify the
AV from afar based on the colour was really helpful, given that she
might need time to ‘get o the phone and [...] grab [her] bags’. P7
brought up a scenario in which she travelled with much luggage: ‘Is
it actually going to start moving before I am able to get on safely? I’m
halfway in and it [laughs] starts driving o?’. P16 was anxious not
knowing how long the AV would wait for her arrival as in reality
she would ‘usually send the [driver] a message and ask them to wait’.
5 DISCUSSION
Our study results reveal a number of themes regarding the way
participants responded to the three prototype representations and
the feedback reported in the interviews. In this section, we discuss
those themes and how they relate to our research questions regard-
ing perceived sense of presence, trust and UX, followed by a series
of design guidelines for context-based interface prototyping, and a
reection on study limitations.
5.1 Eect of Prototype Representation on User
Feedback
5.1.1 Sense of Presence. In terms of how the dierent prototype
representations aected the sense of presence (RQ1), our results
show, as expected, that the two VR representations (RW-VR and
CG-VR) induced higher spatial presence and engagement compared
to the video representation, echoing results from other VR simu-
lator studies [
61
]. Interestingly, however, there was no signicant
dierence in spatial presence between the two VR representations,
despite various participants commenting on the unnatural impos-
sibility of moving around in RW-VR. Regarding the naturalism of
the scene (i.e. ecological validity), the quantitative and qualitative
results both show that the RW-VR prototype representation more
accurately depicted a real-world situation. Interviews conrmed
that the lower perceived ecological validity of the scene in the CG-
VR prototype representation was mainly induced by the diminished
level of naturalism of the virtual characters and animated objects in
the scene. Diminished immersion seemed also to have aected per-
ceived ecological validity, which was ranked lower for RW-Video
than for RW-VR, despite both displaying the same video material.
The results from the ITC-SOPI questionnaire, emphasised through
the semi-structured interviews, imply that RW-VR was best suited
to induce a sense of sharing the same spatial context as the AVs in
the scene. The high preference towards RW-VR (n=30) suggests that
both immersion and perceived naturalism are important design
factors to consider when evaluating eHMI concepts with users.
5.1.2 Trust. In regards to how the prototype representations af-
fected perceived user’s trust in the eHMI (RQ2), we can report that
there were in fact three levels of trust simultaneously at play: (a)
system trust, that is trust towards the vehicle and eHMI; (b) trust
in the environment itself, including other people within it; and
(c) trust in the real-world potential of the eHMI as a viable urban
technology solution.
In terms of system trust, the quantitative results of our study
indicated no signicant dierence in ratings between the three
representations. Yet, qualitative feedback suggests that participants
generally trusted the vehicle, and that they mainly derived their
trust from the way the vehicle interacted with other pedestrians.
Given that this behaviour (e.g. giving way to a pedestrian) was
identical in all three prototype representations, this explains why
there were no signicant dierences in the perception of trust
towards the AV. Interestingly, in the post-study interviews, more
than a third of the participants reported that they would trust the
vehicle more in the RW representations. However, the detailed
analysis revealed that participants’ assessment of trust is based
not just on the AV itself but also determined by trust towards the
overall experience.
The increased sense of presence provided by the VR represen-
tations led participants to express feedback directed at particular
elements of the environment (e.g. texture of trees) and, crucially, at
other human beings in the scene, who they more tangibly felt to
be sharing the experience with. That is relevant, as the VR repre-
sentations seemed to elicit a feedback loop between participants
and the environment, prompting varying feelings of relatedness
to strangers around them, and causing them to consider dierent
behaviour in response to the dierent levels of realism provided
by the prototype representation. Female participants feeling un-
safe in the presence of a seemingly unresponsive (CG simulated)
male character near to them, in a situation they felt having little
control over, are an insightful example of how lower realism can
aect the trust in the environment negatively. This is an important
observation as it highlights that when assessing trust towards an
AV prototype in a simulated environment, other factors might be
at play that inuence participants’ responses.
The realism of the experience oered by the RW representa-
tions, particularly the RW-VR, also seemed to boost participants’
trust in the real-world viability of the eHMI in urban spaces. The
direct experience of the technology contextualised to a real street
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
and surrounded by real people conveyed to participants a sense
of ’this already [being] reality, not ction’, prompting them to re-
ect upon practical aspects such as safety (their own and others’),
ambience (emotional cues given by other people in the scene) and
the autonomous nature of the technology (more clearly decoupled
from the surrounding environment, in comparison to the CG-VR
representation).
5.1.3 User Experience. Regarding RQ3, the quantitative results of
our study show that there is no signicant dierence in UEQ ratings
between the three prototype representations. However, there are
some tendencies in the ratings that are supported by the qualitative
data. For example, attractiveness and stimulation is rated slightly
higher in CG-VR, which also relates to the increased colour con-
trast participants reported on, and therefore oering a ‘cleaner’
depiction of the low-res lighting interface. Further, higher ratings
for perspicuity were matched by participant comments revealing
that they found it easier to comprehend the lighting patterns in the
RW representations. This can be linked to the fact that participants
reported being distracted by various other aspects in CG-VR (such
as the texture of trees). Interestingly, in RW-Video, participants
noticed and were able to reect on more of the eHMI light patterns
in the subsequent interviews. This contradicts previous research
which reported better memory assessment in immersive experi-
ences [
58
], and indicates that the VR experience itself - although
being more vividly - can distract from the assessment of singular
user interface elements. Participants conrmed this observation
in the post-study interviews, for example, stating that they were
more concerned about ‘nding their car’. On the other hand, the
VR prototype representations allowed them to ‘understand the user
experience more holistically’ (P14), which also led to more detailed
feedback on aspects beyond the eHMI.
5.2 Guidelines for Prototyping and Evaluating
Context-Based Interfaces
Based on our comparative study involving a shared AV scenario
and the evaluation of trust and UX towards a custom-designed
eHMI, we propose a series of preliminary guidelines for prototyping
and evaluating context-based interfaces for autonomous systems
through simulations and videos.
5.2.1 Choosing a Simulation Platform and Representation. The
choice of simulation platform (e.g. VR or video) and prototype
representation (e.g. CG or RW) depends on the specic questions
that the evaluation seeks to address.
GL1 - Use non-immersive prototypes for focused interface
evaluations:
We found the assessment of trust towards AVs in a
simulated scenario to be heavily based on how the vehicle interacted
with other pedestrians. These interactions between autonomous
systems and other people sharing the same urban environment can
easily be captured in video prototype representations, eliminating
the need for a costly VR setup and supporting online evaluation
studies [
15
]. Indeed, we found that participants were able to remem-
ber and comment on the light patterns used in our eHMI better in
RW-Video than the VR representations.
GL2 - Use immersive prototypes for holistic assessment and
evaluation of contextual aspects:
We learnt that the VR rep-
resentations allowed for a more holistic assessment of the user’s
relationship with the eHMI in the simulated urban environment,
due to increased spatial awareness and stronger sense of being
actively present in the scene. We therefore propose that VR rep-
resentations (RW and CG) are better suited when seeking user
feedback on not only the interface but how the interface inuences
the user’s experiential and perceptual aspects within a particular
context.
GL3 - Use real-world representations to increase familiarity
and assess overall trust:
Previous work on driving simulators
stressed that familiarity with the environment in real-world videos
increases feeling of safety and leads to richer feedback [
20
]. High
realism and inuence of environmental factors as well as social
interactions between multiple people sharing an urban space with
an eHMI were also deemed important by our participants. RW-
VR, thus, is especially well-suited for capturing the more nuanced
aspects of trust beyond the system itself (linked to the complex and
dynamic context within which the system operates).
GL4 - Use real-world representations to uncover interface
anomalies under more natural conditions:
We received more
responses on potential interface anomalies (i.e. use of colour to
encode information) and potential alternatives (i.e. text displays)
in the real-world representations due to the more natural ambient
lighting and lower contrast compared to the CG-VR. Therefore, we
conclude that real-world representations might be better suited to
evaluate the viability of visual-based interface design proposals.
5.2.2 Composition of Scenes. Prototyping and evaluating context-
based interfaces within a simulated or captured real-world urban
environment comes with a range of challenges and confounding
factors compared to decontextualised evaluation setups (as used e.g.
in [
15
]). Scenes should be carefully composed and their composition
has implications on perceived trust as well as keeping participant
engagement high.
GL5 - Stage interactions with context-based interfaces:
Our
ndings show that interactions between other pedestrians and
the eHMI mainly contributed to the assessment of trust towards
the AV. The high number of responses on those interactions fur-
ther indicates that staged interactions increase memory assessment
which can in turn lead to richer feedback in post-experience in-
terviews. Additionally, staged interactions might prevent survey
fatigue which is in particular important in RW-VR with the partici-
pant’s own interaction radius being limited.
GL6 - Consider eect of environment and people:
Our qual-
itative data showed that aspects beyond the system inuenced
participants’ perception of trust and user experience. Indeed some
participants deemed those aspects, such as with whom they would
be sharing an AV and waiting for the vehicle in a dark, empty loca-
tion, as more critical than the vehicle itself. We therefore conclude
that it’s crucial to consider the eect of surrounding entities, and in
turn stress for the importance of contextualised simulation setups
for a more holistic evaluation of interactions with autonomous
systems.
CHI ’21, May 8–13, 2021, Yokohama, Japan Hoggenmueller and Tomitsch, et al.
GL7 - Carefully consider camera position and constrain move-
ment in RW-VR:
Due to the lack of freedom to move in RW-VR,
the camera position for recording has to be carefully chosen. In
our specic context, it was important that the camera was not posi-
tioned in the vehicle’s potential trajectory, while still warranting a
good viewing angle to observe the interactions. Positioning people
next to the camera or recording in a physically constraint envi-
ronment can further help to create a visual bounding box to deter
participant’s urge to move within the RW-VR environment.
5.2.3 Designing CG-VR Prototypes. Finally, our ndings suggests
considerations to be made for the specic case of prototyping
context-based interfaces through CG-VR representations.
GL8 - Avoid virtual avatars in intimate or personal proxemic
zones of participants:
Our study results indicate that computer-
generated context-based interface evaluations, which require simu-
lated avatars to interact with autonomous systems, can be aected
by the uncanny valley phenomenon [
32
]. This not only leads to
decreased perception of realism, but also in decreased trust towards
the overall experience and feelings of distraction compromising the
assessment of the actual prototype. Based on that observation, we
recommend that virtual avatars, if possible, should not be placed in
the intimate or personal proxemic zones of participants.
GL9 - Avoid unnecessary details to prevent distraction by im-
perfections in CG representations:
Aiming for an accurate copy
of the RW-VR source in CG-VR, our 3D designer carefully crafted
ne details, such as tree textures and animation of leafs to simulate
wind. However, our participants reported those details as having
been distracting and emphasising imperfections in CG-VR. Due to
the still apparent lack of realism in CG representations and given
the often limited budget for research prototypes, we therefore rec-
ommend to limit unnecessary details (and in particular animations)
concerning the surrounding environment.
5.3 Limitations and Future Work
The presented study has some limitations that we would like to
acknowledge. To minimise the learning eect and transfer across
conditions, we opted for a between-subject design, which, however,
comes with the limitation that less data points per participant are
taken. Although the number of participants is in the range of other
similar studies (cf. [
49
]), we acknowledge that the sample size for
the quantitative data analysis is rather small. Yet, we would also
argue that the quantitative data is only part of the broader scope of
participant data collected, and feeds into the additional analysis of
qualitative data from 11.5 hours of interviews.
The novelty eect inherent to emerging technologies, such as
VR, and participants’ previous experience with VR, might also have
had an impact on the study results. We tried to address this as
much as possible by counterbalancing previous experiences in VR
in our study design. We further investigated the collected data for
dierences between participants linked to their previous experi-
ence. We found that participants with no previous VR experience
were impressed by the high realism and immersion of the CG-VR
when experiencing this representation as the rst condition. After
subsequently having experienced the RW-VR, they stated that they
would have assessed their sense of presence in CG-VR dierently.
The novelty eect in our study may be further aected by the fact
that none of the participants (including those with previous VR
experience) had experienced 360-degree VR before.
Previous experimental research studies with autonomous driving
simulators have acknowledged the limitations of measuring trust
based on post-experiment questionnaires and interviews [
19
,
25
].
They also refer to previous work in human-robot interaction, which
highlights that a widely accepted denition of trust is missing [
36
].
While acknowledging this as a limitation, we also want to emphasise
the exploratory ndings we gained through interviews, for example,
showing that participants assess trust towards various entities in
a VR simulation. Furthermore, when using RW representations,
participants seem to factor in potential real-life consequences in
their perception of trust, resulting in increased feelings of alertness
and awareness of the environment. We therefore posit that these
ndings oer new insights on the multifaceted and complex aspects
of measuring trust towards autonomous systems in VR.
Further, given that we only conducted a single user study, we see
our ndings and synthesised guidelines as preliminary and not set
in stone, indicating areas of future work and requiring more focused
investigations: for example, in regards to the use of avatars in CG-
VR, future research should investigate the eects of low-realism or
abstract representations of avatars on user feedback during context-
based interface evaluations. Plus, as suggested by a participant, it
might be helpful to allow users to visualise their own body parts in
the same visual style as the avatars in the scene [
34
,
50
], so that they
can better relate their own virtual self to the simulated characters.
Another open challenge for evaluating context-based interfaces is
to nd a sweet spot between preventing survey fatigue and oering
sucient time to experience the prototype. A potential solution for
keeping participants engaged also during longer scenarios in VR
could be to enable them to interact with a smartphone in meaningful
ways that support the scenario.
6 CONCLUSION
To sum up, the advent of AVs brings new challenges into the do-
main of interaction design, such as prototyping and evaluating
context-based interfaces (e.g. eHMIs). At the same time, technologi-
cal advances in immersive video capturing and VR hardware oers
designers and researchers a wider range of possible prototyping
representations and platforms to choose from. By systematically
studying the eect of prototyping representations on study results,
our paper adds to previous work on virtual eld studies [
42
] and
context-based interface prototyping [18].
ACKNOWLEDGMENTS
This research was supported partially by the Sydney Institute for
Robotics and Intelligent Systems (SIRIS) and ARC Discovery Project
DP200102604 Trust and Safety in Autonomous Mobility Systems: A
Human-centred Approach. The authors acknowledge the statistical
assistance of Kathrin Schemann of the Sydney Informatics Hub,
a Core Research Facility of the University of Sydney. We thank
all the participants for taking part in this research. We also thank
the anonymous CHI’21 reviewers and ACs for their constructive
feedback and suggestions how to make this contribution stronger.
Context-Based Interface Prototyping CHI ’21, May 8–13, 2021, Yokohama, Japan
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... AV picking up another pedestrian). After deploying the videos into the game development platform Unity 1 , we evaluated the HMI with users in immersive VR, thereby measuring trust and UX (Hoggenmueller et al., 2021b). ...
... how does the HMI support safety and trust), it is more important that user's perception and behaviour are comparable to a real-world situation (Feldstein and Dyszak, 2020). In this regard, we also found in a comparison study of 360-degree VR (B) and computer-generated VR simulations that differences in spatial awareness and perceived realism influence on which experiential and perceptual aspects user focus in the evaluation (Hoggenmueller et al., 2021b). We found that when evaluating trust and UX, aspects beyond the AV and the eHMI itself are assessed, for example overall trust towards the situation (e.g. ...
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... However, we acknowledge that VR simulations cannot represent the complexity of the real world. On the other hand, all of our participants reported a high perceived realism in the simulated urban environment [see also (Deb et al., 2017;Hoggenmüller et al., 2021)] and that they behaved as they would have in real traffic situations. A particular focus in our study was whether the concept is understandable for pedestrians in environments with multiple road users; in our case multiple vehicles and other pedestrians. ...
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Automated driving will require new approaches to the communication between vehicles and vulnerable road users (VRUs) such as pedestrians, e.g., through external human–machine interfaces (eHMIs). However, the majority of eHMI concepts are neither scalable (i.e., take into account complex traffic scenarios with multiple vehicles and VRUs), nor do they optimize traffic flow. Speculating on the upgrade of traffic infrastructure in the automated city, we propose Smart Curbs, a scalable communication concept integrated into the curbstone. Using a combination of immersive and non-immersive prototypes, we evaluated the suitability of our concept for complex urban environments in a user study (N = 18). Comparing the approach to a projection-based eHMI, our findings reveal that Smart Curbs are safer to use, as our participants spent less time on the road when crossing. Based on our findings, we discuss the potential of Smart Curbs to mitigate the scalability problem in AV-pedestrian communication and simultaneously enhance traffic flow.
... Simply rendering a few people and their background chatter within a scene [32] can bring liveliness to the simulated world and contribute to a more realistic experience [63]. These additions should be placed at a distance from the participant in a study's VE to avoid diverting their attention, especially when users might react strongly to humanoid agents in VR [64] because of the potential uncanny valley effect [65]. ...
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