=Paper=
{{Paper
|id=Vol-3154/paper5
|storemode=property
|title=Challenges of Explainability, Cooperation, and External Communication of Automated Vehicles
|pdfUrl=https://ceur-ws.org/Vol-3154/paper5.pdf
|volume=Vol-3154
|authors=Mark Colley,Enrico Rukzio
|dblpUrl=https://dblp.org/rec/conf/chi/ColleyR22
}}
==Challenges of Explainability, Cooperation, and External Communication of Automated Vehicles==
https://ceur-ws.org/Vol-3154/paper5.pdf
Challenges of Explainability, Cooperation, and
External Communication of Automated Vehicles
Mark Colley1 , Enrico Rukzio1
1
Institute of Media Informatics, Ulm University, James-Franck-Ring 8, 89081 Ulm, Germany
Abstract
In this position paper, we describe current research questions in the area of interaction with automated
vehicles from the viewpoint of users within the vehicle (i.e., passengers) and from the viewpoint of parties
outside the vehicle (e.g., other manual drivers or pedestrians). First, we briefly introduce the topics of
Cooperation with Automated Vehicles, External Communication of Automated Vehicles, and Explainability
of Automated Vehicles before posing, in total, nine research questions guiding these three areas.
Keywords
Automation, vehicles, eHMI, explainability, cooperation, HMI
1. Introduction
Automated vehicles (AVs) are expected to change journeys and, in general, traffic fundamen-
tally [1]. Despite the numerous anticipated advantages (fewer accidents, more time for non-
driving-related activities [2], reduced fuel usage), currently, there are two major research areas
targeted towards the successful integration of AVs in the life of non-expert users (i.e., the
passengers) and also the bystanders of AVs, that is other (vulnerable) road users that have no
say in whether they want to interact with them such as pedestrians or bicyclists [3].
Regarding the passengers, current work can be (among other areas) broadly distinguished
into take-overs [4, 5], cooperation to overcome system boundaries [6, 7, 8, 9], explainability of
the AV’s actions [10, 11, 12, 13, 14], and simulators to enable valid experiments [15, 16, 17].
Regarding bystanders, especially the (potential) need to replace current driver-road user
communication via external Human-Machine Interfaces (eHMI) is investigated [18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32].
In this position paper and based on our work in these areas, we describe what we see as
current challenges of AVs as highly automated systems that permanently interact with other
road users and their primary intended users (i.e., the passengers).
AutomationXP22: Engaging with Automation, CHI'22, April 30, 2022, New Orleans, LA
$ mark.colley@uni-ulm.de (M. Colley); enrico.rukzio@uni-ulm.de (E. Rukzio)
https://m-colley.github.io/ (M. Colley)
0000-0001-5207-5029 (M. Colley); 0000-0002-4213-2226 (E. Rukzio)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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2. Cooperation with Automated Vehicles
Figure 1: The user interface of the ORIAS system for cooperation in AVs; taken from [8].
.
Besides the classical take-over process [33, 5], cooperation has been suggested as a way to
overcome both the shortcomings of today’s technology, for example, in object recognition [6]
or when integrated knowledge is necessary (e.g., legal requirements to parking somewhere).
Such an approach will leverage the capabilities of automation and the integrated understanding
of the user to enable safer and more pleasurable journeys. Here, the main question is:
1. When is cooperation between an AV and a user possible, and which level of engagement has
to be maintained during the monotonous part of the journey?
While we presented first supportive work on this [8, 6, 34, 35] (see Figure 1), this area of
research is still under-explored.
3. External Communication of Automated Vehicles
Recently, eHMIs have become a popular topic in the automotive domain [27]. External com-
munication of AVs is researched to enable communication between AVs and other road users.
This includes other manual drivers [36, 37] and vulnerable road users such as bicyclists or
pedestrians. Numerous aspects have been investigated, including anthropomorphism [38],
challenges of overtrust [39], various target groups such as children [40] or people with vision
impairments [23]. However, in our opinion, several key questions remain:
2. How can the aspect of scalability, that is, the communication of multiple AVs with multiple
vulnerable road users, be solved? [22]
3. How can we as researchers include and aid people with disabilities, a group which is even
more in danger in the heavy traffic of today’s cities? [20]
4. What are the long-term effects of eHMIs?
5. How can eHMIs be visually pleasing and effectively integrated into the general concepts of
automobile manufacturers?
6. Can eHMIs be useful for more than communication regarding the crossing decision? (e.g.,
see [24, 41, 31, 32])
7. Are eHMIs necessary or when are they necessary? [42]
This field is especially interesting as people that are not instructed nor did they actively
consent to using AVs in any way are involved in interacting with the AV. Therefore, this topic is
one of the first to fully incorporate true novice users of automation.
4. Explainability of Automated Vehicles
(a) Derived pedestrian intentions; taken from [10]. (b) Displayed semantic segmentation result; taken
from [11].
Figure 2: Examples from previous publications regarding the visualization of detected objects and
intentions.
Schoettle and Sivak [43] found that 75% of respondents were at least slightly concerned about
a possible system failure in unexpected situations. Additionally, the reliability of AVs is a worry
of users [44]. Therefore, numerous works have investigated potential visualization concepts to
communicate with the user of an AV [45, 12, 14].
One primary rationale regarding the explainability of AVs is to enhance and calibrate trust.
Hoff and Bashier defined trust as “a variable that often determines the willingness of human
operators to rely on automation” [46, p. 407]. They proposed a three-layered trust model,
including dispositional, situational, and learned trust. Dispositional trust refers to the trustor’s
personal background (e.g., culture, age, and personality traits). Situational trust is categorized
into internal and external variability. External variability refers to alterations occurring with
changed automation complexity. Internal variability describes the trustor’s mental capacity and
psychological state. Learned trust was modeled in two layers: initially learned trust, that is trust
based on known information about the automation) and dynamically learned trust (which is
altered via interaction with the automation).
Hereby, the approaches target either initially learned trust (see Körber et al. [47]) or dynami-
cally learned trust (e.g., [10, 11, 14, 12]; see Figure 2). Nonetheless, some significant questions
remain unanswered:
8. What are the long-term effects of using AVs, and how will interaction change?
9. How can include and aid people with disabilities, for example, people with vision impair-
ments?
With our approaches, we especially target to enhance calibrated trust by including uncertainty
information into the communication (e.g., see [11]).
5. Conclusion
In this work, we briefly outline the two major research areas on automotive automation in-
teraction: with drivers and users or with other road users such as pedestrians. For the three
areas Cooperation with Automated Vehicles, External Communication of Automated Vehicles, and
Explainability of Automated Vehicles, we briefly describe the previous work and define, in our
view, currently relevant research questions.
Acknowledgments
This work was conducted within the projects ’Interaction between automated vehicles and
vulnerable road users’ (Intuitiver) funded by the Ministry of Science, Research and Arts of
the State of Baden-Württemberg, as well as ’Semulin’ and ’SituWare’ funded by the Federal
Ministry for Economic Affairs and Energy (BMWi). Mark Colley was also supported by the
Startup Funding B of Ulm University.
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