Hello everyone! My name is Lukas Flohr, I am a UX Designer at Ergosign and a PhD Student
at Saarland University. I am presenting to you today our paper about
video-based simulation of Autonomous Vehicles which is a joint work with Dominik Janetzko,
Dieter Wallach, Sebastian Scholz and Antonio Krüger.
It is supported by the German Federal Ministry of Transport and Digital Infrastructure.
Over the past decades, much effort has been put into the development of automated driving
systems that shift more and more control from the human driver to the vehicle.
The SAE describes six levels to classify the degree of automation. In levels 0 to 3, a
driver is still required, whereas in levels 4 and 5 this would no longer be the case.
Therefore, levels 4 and 5 are often described as “autonomous” driving vehicles.
While autonomous – and especially shared autonomous – vehicles have great potential
for solving common challenges of today’s public transport systems, the technology faces
both technological and psychological challenges.
From an HCI-perspective, key hurdles for the acceptance and adoption of shared autonomous
vehicles are users’ expectations, concerns regarding the technology’s reliability,
performance, security and privacy and – most of all – trust issues.
So we asked ourselves the question: How can we counteract these challenges as early as
possible? Since autonomous ridesharing is still a more
or less theoretical subject, we figured that we need prototyping and simulation methods
enabling us to take this not yet available context into account.
For SAE levels 0 – 3, CGI-based driving simulators are already very popular in automotive
research. They enable the safe assessment of new systems from early development phases
on..
However, that leaves the questions: are those also suited for simulating autonomous vehicles?
Or are there other, potentially better options?
Since there are no driving-related controls necessary, a promising approach might be to
use real-world videos featuring a very realistic representation of the actual context. These
can easily be created and don’t require special equipment or programming skills.
The idea of using real-world videos to simulate specific situations has already been around
for a while.
Kray and colleagues, for example, created an immersive video experience in order to
put a person into a certain urban context. Krome and colleagues used a single video to
simulate an autonomous ride through a city. Gerber and colleagues combined a pre-existing
advanced driving simulator with immersive video to increase the existing simulator’s
immersion.
In contrast to previous approaches, we focused explicitly on simulating an autonomous vehicle
with a rather simple and cost-effective setup.
Therefore, we transformed one of our office rooms into a cave-like environment. It consists
of three front light video projections, stereo speakers, a seating group and a single MacBook
that controls the simulation.
For testing purposes, the setup could easily be extended by HMIs or other components to
resemble the interior of the respective vehicle. In our case, we wanted to simulate the experience
of riding in a „pod“-like people mover.
To record the footage for the simulation, we used low-budget action cameras, which were
mounted on the window of a common car while driving through traffic.
To investigate the simulator’s potential, we conducted two studies.
For the first study, we invited HCI professionals to share their unbiased opinions on the approach.
The purpose of this study was to evaluate the general quality of the simulation, to
eliminate potential issues and to derive optimizations.
All nine experts provided generally positive feedback in terms of overall presence perception.
Potential for optimization was in particular revealed regarding the sound simulation.
For example, participants suggested to add sounds of opening and closing doors, noises
from other passengers or signal sounds for announcing the next stop.
Furthermore, three participants emphasized the idea of increasing presence perception
by adding actors that simulate passengers getting on and off during shared rides.
Both suggestions have been directly used for our second study with 31 participants.
In this study we had two major objectives. First, evaluating the simulator with a larger,
more representative sample. Second, investigating the effects of actually
involving an actor in a simulator study that mimics another passenger.
When the participants arrived, they received a short briefing on the study’s purpose,
the test scenario and simulator sickness. Furthermore, they signed a participation consent
form.
All participants took two rides in the AV simulator. Each ride took about 14 minutes.
In both, the simulated vehicle stopped twice before they reached the target destination.
Another passenger joined the ride at the first stop and left at the second. This happened
either only virtually represented by the sounds being heard and the information on the HMI
or physically represented by an actor. At the third stop participants reached the
target destination.
After each ride, participants filled out a digital questionnaire to assess the depended
variables. At the end of the session, they received a debriefing and their compensation.
This video gives a glimpse on how the study looked like when the actor entered at the
first stop.
After each ride we assessed presence perception with the IPQ and a single
item „feeling of reality“, technology acceptance with the questionnaire
by Van der Laan and colleagues and wellbeing with the Motion Sickness Assessment
Questionnaire MSAQ and a single item „feeling of comfort“.
Descriptive statistics and plots of the IPQ scales revealed a rather positive evaluation
of presence perception within both conditions, especially in terms of spatial presence and
experienced realism.
Inferential statistics did not return significant differences between the conditions.
In contrast to the IPQ, results of the single item ‘feeling of reality’ showed a positive
tendency towards using an actor. A paired samples t-test exposed a significant difference
between the conditions.
Judging only from the single item, the actor actually seems to have a positive effect on
presence perception.
Both subscales of the acceptance questionnaire showed a slight positive trend favoring the
involvement of an actor, but paired-samples t-tests did not reveal significant differences.
However, the tests indicate a non-significant trend with a small effect regarding the usefulness
scale.
However the tests indicate a non-significant trend with a small effect regarding the usefulness
scale.
In terms of wellbeing, the MSAQ generally showed low values in all subscales, with slightly
higher values observable in the condition involving an actor. Four participants explicitly
mentioned symptoms related to simulator sickness.
Wilcoxon tests revealed significant differences in the subscale Gastrointestinal and in the
Overall rating, also both with a medium effect.
The results indicate significant adverse effects on motion sickness during simulator rides
that involve an actor.
The subjective feeling of comfort achieves high values in both conditions.
But without a relevant difference between the conditions.
To conclude, we introduced a simple immersive video-based method for low-budget autonomous
vehicle simulation that can be used as a prototyping framework. We evaluated the setup with two
studies and provide initial evidence for its suitability. Further studies might use the
setup for usability testing, user research or research-through-design activities.
Furthermore, we combined the AV simulator with a human actor imitating a passenger that
gets on and off a shared autonomous ride. Although we found some support for the idea,
it seems not to have a significant positive effect on participants’ presence perception
that would justify the extra effort.
Thank you very much for watching and goodbye.