###### Abstract

In recent years, researchers and manufacturers have started to investigate
ways to enable autonomous vehicles (AVs) to interact with nearby pedestrians
in compensation for the absence of human drivers. The majority of these
efforts focuses on external human-machine interfaces (eHMIs), using different
modalities such as light patterns or on-road projections to communicate the
AV’s intent and awareness. In this paper, we investigate the potential role of
affective interfaces to convey emotions via eHMIs. To date, little is known
about the role that affective interfaces can play in supporting AV-pedestrian
interaction. However, emotions have been employed in many smaller social
robots from domestic companions to outdoor aerial robots in the form of
drones. To develop a foundation for affective AV-pedestrian interfaces, we
reviewed the emotional expressions of non-humanoid robots in 25 articles
published between 2011 and 2021. Based on findings from the review, we present
a set of considerations for designing affective AV-pedestrian interfaces and
highlight avenues for investigating these opportunities in future studies.

###### keywords:

Emotional expression, non-humanoid robots, autonomous vehicles, pedestrians,
human-machine interfaces, human-robot interaction

1 1 0 https://doi.org/ How Can Autonomous Vehicles Convey Emotions to
Pedestrians? A Review of Emotionally Expressive Non-Humanoid Robots How Can
Autonomous Vehicles Convey Emotions to Pedestrians? A Review of Emotionally
Expressive Non-Humanoid Robots Yiyuan Wang *, Luke Hespanhol and Martin
Tomitsch Yiyuan Wang, Luke Hespanhol and Martin Tomitsch Wang, Y.; Hespanhol,
L.; Tomitsch, M. Correspondence<EMAIL_ADDRESS>
##  1 Introduction

The introduction of autonomy in vehicles promises to increase the level of
convenience and comfort for riders Litman (2017). However, the absence of
human drivers in fully autonomous systems induces an interaction void around
what used to be traditional communication strategies between drivers and other
road users, such as eye contact and body gestures Rasouli and Tsotsos (2019).
The responsibility for communicating internal states and intentions in
typically short, dynamic traffic scenarios is to a large extent, if not
entirely, delegated from drivers to the self-moving vehicles themselves.

Researchers and manufacturers have been dedicated to developing additional
channels to assist autonomous vehicles (AVs) in conveying their intent and
awareness to surrounding road users, especially to pedestrians, who are
considered as one of the most vulnerable yet frequent interaction subjects
Rasouli and Tsotsos (2019). Existing means that aim to support a safe and
intuitive AV-pedestrian interaction are various, ranging from display
technologies such as LED lighting patterns Lagström and Malmsten Lundgren
(2016); Mahadevan et al. (2018) and on-road projections Nguyen et al. (2019);
Pratticò et al. (2021), to borrowing anthropomorphic features such as moving
eyes that follow the pedestrian’s position Chang et al. (2017); Löcken et al.
(2019) or displaying a smiling expression on the front of AVs Löcken et al.
(2019); Pratticò et al. (2021).

While the existing solutions have been validated to compensate for the lack of
driver-pedestrian communication in different ways, emotions, a vital dimension
in human-human interaction Cauchard et al. (2016), have thus far been mostly
disregarded in this growing area of research. Different from pragmatic
channels, emotions have a unique role in affecting perception, empathy,
decision-making, and social interactions Picard (2003). In fact, imbuing
emotions into social robots is not rare in human-robot interaction (HRI).
Prior work describes the ability to convey emotions as one of the indicators
of socially interactive robots Fong et al. (2003). Previously designed robots
that are capable of expressing emotions vary in functionalities, appearances,
as well as how they articulate emotions in interactional contexts. We reviewed
a sub-domain of these robots — emotionally expressive robots that have a non-
humanoid form — aiming to translate the design paradigms for their emotional
expressions into cues that AVs may employ in the affective dimension, as AVs
are in essence self-moving, non-humanoid social robots.

The contribution of this paper is twofold. First, we systematically review 25
articles focusing on designing and evaluating emotional expressions for non-
humanoid robots in the past ten years. We summarise emotion models, output
modalities, evaluation measures, as well as how users perceived the emotional
expressions in these studies. Second, based on the findings from the review,
we propose a set of considerations for designing affective AV-pedestrian
interfaces by adding emotions as an additional communication dimension. Our
findings contribute to enhancing AV-pedestrian interaction and increasing
social acceptance for the deployment of AVs in urban environments.

##  2 Background

###  2.1 Emotion in Social Robotics

Social robots have been utilised in many domains including healthcare,
education, domestic environments, and public spaces Leite et al. (2013). To
bring the interactions with humans onto a more natural and engaging level
Bretan et al. (2015); Gácsi et al. (2016); Ritschel et al. (2019), many of
these robots have been integrated with the capability of displaying emotions.
Eyssel et al. Eyssel et al. (2010) showed that a robot expressing emotional
states could make people feel closer to the robot, perceive it as having
anthropomorphic traits and intentionality, and experience a more pleasant HRI
process. Besides facilitating empathic inferences about internal states and
intentions Löffler et al. (2018), emotions expressed by robots can
subsequently elicit affective responses from humans Boccanfuso et al. (2015);
Herdel et al. (2021); Whittaker et al. (2021) and impact the immediate
environment where the interaction is taking place Harris and Sharlin (2011);
Hoggenmueller et al. (2020). With the increased perceived sociability Herdel
et al. (2021), emotionally expressive robots can effectively establish closer
bonds with humans and, more importantly, increase public trust and acceptance
Cauchard et al. (2016); Hoggenmueller et al. (2020); Tennent et al. (2018);
Whittaker et al. (2021) in their deployment in our daily lives.

###  2.2 Humanoid Robots vs. Non-Humanoid Robots

Many humanoid robots, such as NAO Monceaux et al. (2009) and Pepper Pandey and
Gelin (2018), can employ readily available anthropomorphic features like body
movement, facial expression or speech to exhibit emotions. While such physical
embodiment may increase the recognition level of displayed emotions Harris and
Sharlin (2011); Löffler et al. (2018), a large class of social robots are
tuned to be less- or non-anthropomorphic in order to match the functional
requirements at their designated tasks Gácsi et al. (2016); Harris and Sharlin
(2011); Novikova and Watts (2014); Song and Yamada (2017). For example, rescue
robots are usually small and tank-like Song and Yamada (2018), and domestic
vacuum robots like Roomba are likely to be puck-shaped and able to fit under
couches Harris and Sharlin (2011). In addition to the utilitarian aspect,
implementing emotions using the anthropomorphic features in humanoid robots
not only can be expensive and technically complex Gácsi et al. (2016); Peng et
al. (2020); Song and Yamada (2017) but also needs to fulfill users’
expectations on their level of anthropomorphism Löffler et al. (2018); Park et
al. (2021) and at the same time to minimise the feeling of creepiness known as
the “uncanny valley” effect Herdel et al. (2021); Park et al. (2021).
Consequently, an increasing body of literature has focused on designing
emotional expressions for non-humanoid robots to show affection. On the one
hand, non-humanoid robots are anatomically unavailable to express emotions
like humans; on the other, this reduces stereotypes of how emotions should be
displayed and thus broadens the range of modalities across visual, auditory,
and haptic channels that these robots may employ. In the context of this
paper, we are interested in understanding what modalities have been used to
encode emotions in non-humanoid robots and how well the resulting emotional
expressions are identified and perceived by users.

###  2.3 Current AV-Pedestrian Interaction

With human drivers replaced by autonomous control systems, one important
challenge for the social acceptance of AVs is to communicate their intent and
awareness to nearby pedestrians and other vulnerable road users (VRUs)
Pratticò et al. (2021); Rasouli and Tsotsos (2019). A major direction in
exploring effective communication strategies is the development of external
human-machine interfaces (eHMIs) Dey et al. (2020); Pratticò et al. (2021);
Rasouli and Tsotsos (2019). Current solutions to AV-pedestrian eHMIs are
manifold. For example, vehicle-mounted LED lighting patterns have been
utilised to indicate vehicle modes, the awareness of a nearby pedestrian, or
the intention to yield or move Lagström and Malmsten Lundgren (2016);
Mahadevan et al. (2018); Rasouli and Tsotsos (2019). Studies also investigated
the use of eHMIs to convey messages by displaying pictograms Urmson et al.
(2015) and textsColley et al. (2021); Clamann et al. (2017). On-road
projections have been investigated as a way to leverage traffic metaphors like
crosswalks or stop signs Hesenius et al. (2018); Löcken et al. (2019); Nguyen
et al. (2019). In one implementation, the road infrastructure was updated to
collect data from AVs and to convey the information to pedestrians via a smart
road Mairs (2017). Some researchers also experimented with anthropomorphic
features to restore current driver-pedestrian interaction patterns. An example
for this is the implementation of moving eyes that follow the position of
pedestrians at crosswalks Chang et al. (2017); Löcken et al. (2019). Other
studies tried a printed hand that waved to indicate yielding Mahadevan et al.
(2018) and a sign indicating a smile to inform pedestrians that it is safe to
cross Löcken et al. (2019); Pratticò et al. (2021).

Those studies focused on the communication of intent and awareness through the
use of operational cues, akin to the way traditional street signage does,
which is designed to evoke immediate, and sometimes emotional, responses from
users and further coordinate actions among interaction subjects. However,
enabling AVs to express emotions as a communication strategy has not yet been
addressed as a primary focus of research. This gap was also identified in a
recently published design space for the external communication of AVs Colley
and Rukzio (2020). The authors pointed out the need for “affective messages”
(i.e. messages related to emotions) since such messages are highly important
in interpersonal communication even if they do not necessarily carry meaning
Colley and Rukzio (2020).

###  2.4 Why to Ascribe Emotions to AVs

The mass deployment of AVs in daily traffic environments could be hindered by
their social acceptance related to not only technical aspects but societal
factors as well Pratticò et al. (2021). Using eHMIs to communicate intent and
awareness reduces public scepticism by improving pedestrians’ understanding of
the machine’s decisions and maneuvers, and therefore fostering safe
interaction. Yet, overcoming people’s psychological barrier towards AVs’
deployment is not easy, and various forms of discrimination against AVs are
still continuously witnessed. For example, local residents harassed and
attacked (e.g. thrown rocks at) Waymo’s self-driving cars during a public
trial because of feeling uncomfortable or scared around them Newcomb (2018).
Volvo’s driverless cars were reported to be “easy prey” on the road and were
bullied by other drivers with slamming on brakes or aggressive driving to
force them into submission Connor (2016). Even being one of the most
vulnerable road users, pedestrians were also found to take advantage of AVs’
rule-abiding nature by crossing the road with impunity once they discovered
that the cars were self-driving Bazilinskyy et al. (2021); Jayaraman et al.
(2019). Though intelligent agents, AVs are often considered as mindless
machines following programmed rules or even, more generally, a piece of
“creepy” technology that breaks the status quo that people are comfortable
with Garber (2014).

Following findings from studies of social robots in other domains, a promising
avenue to address some of these issues is to equip AVs with social traits,
such as the ability to express emotions. This has the potential to shift
people’s perception of AVs as purely algorithmically driven agents towards
intelligent social actors. Indeed, concepts for increasing AVs’ sociability
have surfaced in recent years. In 2014, Google announced a driverless car
prototype that was intentionally designed like an adorable “Marshmallow Bumper
Bots” with headlights like wide eyes and a front camera like a button nose,
aiming to resemble a living being or a friend Garber (2014); D’Onfro (2014).
Taking an even greater leap, in August 2021, Honda released a lively AV bot
serving as both transportation and smart companion, conceptualised for the
year 2040 Sood (2021). It has a large frontal face with animated emotional
facial expressions and fenders with covered wheels like pet animal legs,
radiating “the cute character of a playful puppy” Sood (2021). These efforts
are striving to make AVs likable social agents and improve their acceptance by
evoking people’s empathy. In line with these concepts, increasing AVs’
emotional expressiveness is likely to enrich their social characteristics
Hoffman et al. (2015); Herdel et al. (2021); Fong et al. (2003) and improve
their acceptability Cauchard et al. (2016); Herdel et al. (2021).

Apart from influencing perception and empathic understanding, emotion in HRI
is known for its function to regulate and guide human decision-making and
behaviour, biasing the interaction process away from negative or harmful
results Picard (2003). For example, a majority of in-car affective human-
machine interfaces (HMIs) use expressive cues such as emotional music, ambient
light, or empathic speech to regulate drivers into an emotionally balanced
state and thus promote safe driving behaviour Braun et al. (2021). Similarly,
AVs expressing emotions through external affective cues may help regulate the
traffic climate Sadeghian et al. (2020), especially when other road users
disagree with AVs’ decisions (e.g. road rage towards the AV’s maneuver Connor
(2016)). Close to the strategy of using emotions or affects, some existing AV-
pedestrian eHMIs have attempted to convey courtesy through textual messages
like “Thank You” and “You’re Welcome” Colley et al. (2021), and “Please”
Lanzer et al. (2020), facilitating the cooperation between AVs and
pedestrians. Hence, along with adjusting people’s preconceptions, AVs’
expressiveness should help regulate and guide the interaction process and
eventually contribute to their functioning. This kind of approach follows
Picard’s definition of affective computing, suggesting that it is not about
making machines look “more emotional” but about making them more effective
Picard (2003).

The rich literature in affective robotics indicates that emotion encoding in
AV-pedestrian interaction is possible. Indeed, humans have the innate tendency
to attribute liveliness, emotions, intelligence, and other social
characteristics to moving objects Bretan et al. (2015); Harris and Sharlin
(2011); Hoggenmueller et al. (2020); Peng et al. (2020); Tan et al. (2016);
Tennent et al. (2018). A close example to AVs are drones, also referred to as
unmanned aerial vehicles (UAVs). These are small plane-like flying robots that
show rich kinematics and functionalities Cauchard et al. (2016). As personal
drones are becoming more popular and ubiquitous in our daily lives Cauchard et
al. (2016); Herdel et al. (2021), emotion encoding in human-drone interaction
(HDI) has also been studied in recent years Cauchard et al. (2016); Herdel et
al. (2021); Hieida et al. (2016), especially for the purpose of increasing
their acceptability Cauchard et al. (2016); Herdel et al. (2021). Through a
systematic review of how drones and other non-humanoid social robots have
displayed emotions to people surrounding them, we aim to investigate plausible
emotions or affects as well as possible output modalities that could be
considered for designing affective AV-pedestrian interfaces.

##  3 Method

To gain an understanding of current emotionally expressive non-humanoid
robots, we reviewed relevant articles from 2011 to 2021 using a systematic
search strategy.

###  3.1 Search Strategy

####  3.1.1 Database Selection

To identify the most relevant publishers, we first queried Google Scholar as
it covers a broad search across various sources of publications. We used the
Publish or Perish software (version 7) Harzing (2007) to request Google
Scholar in order to extract the search results into a CSV file, as Google
Scholar does not provide the functionality to download the results as a whole.
We searched for “emotion” AND “robot” from 2011 to 2021. We extracted the most
relevant 1000 results as it was the maximum number of results available for
retrieval. We then counted the number of results per publisher, resulting in
four top publishers (IEEE = 291, Springer = 176, Elsevier = 72, ACM = 69)
followed by Google Patents (31), MDPI (25), and SAGE (22) while others were
below 20.

We then searched within the databases corresponding to the top four
publishers, i.e. IEEE Xplore Digital Library, SpringerLink, ScienceDirect (for
Elsevier), and ACM Digital Library, using the same query string and time frame
as in the first step. We checked the number of results and then excluded
SpringerLink as it yielded over 10,000 results under the “Article” and
“Chapter and Conference Paper” content types, which would have made a
subsequent review impossible to achieve. This left us with three databases for
the detailed article search and analysis (ACM = 1,427, IEEE = 1,103,
ScienceDirect = 3,329).

####  3.1.2 Keyword Search Procedure

To identify potential article candidates for the review, we conducted a
keyword search within each of the selected databases. Three main keywords were
used: “emotion”, “robot”, and “non-humanoid”. We also included synonyms that
are commonly used to describe a non-humanoid appearance for robots: “non-
anthropomorphic”, “appearance-constrained”, and “appearance constrained”. We
combined these keywords using AND/OR operators and utilised the advanced
search feature in each database. We selected a time frame of the last ten
years because: (1) 87% of the total results fell within the last ten years,
and (2) we were interested in understanding recent trends in this growing
discipline. The time frame for the final search was from 01/01/2011 to
16/07/2021. The search yielded a total of 225 results including research
articles, posters, books, and other kinds of publications across the three
databases (ACM = 77, IEEE = 77, ScienceDirect = 71).

####  3.1.3 Article Selection

We chose articles published in conference proceedings and journals that: (1)
were written in the English language, (2) used a non-humanoid robot, (3)
designed emotional expressions for the robot, and (4) evaluated the emotional
expressions with empirical user studies and presented the evaluation results.
In a first step, the lead author screened the articles. This process involved
reading each article’s title, abstract, and full-text to see if it met the
selection criteria. In this process, we removed duplicated articles as some
articles were published jointly by different publishers. We further excluded
articles that proposed approaches for robots to sense or recognise users’
emotions rather than express emotions, as well as articles that included a
robot capable of displaying emotions but offered little information in how the
emotional expressions were designed. The screening process resulted in the
final collection of 25 articles that are included in our review. As a second
step, two of the authors then reviewed and discussed the results, mapping them
out on a digital whiteboard. No further changes were made to the article
selection in that step.

###  3.2 Research Questions

The review presented in this paper is based on the following research
questions.

  * •

What emotions are commonly expressed by non-humanoid robots?

  * •

How are the emotions displayed?

  * •

What measures are used to evaluate the emotional expressions?

  * •

What are the user perceptions of the emotional expressions?

##  4 Review of Emotionally Expressive Non-Humanoid Robots

This section presents the systematic review of the 25 articles in response to
the research questions in Section 3.2.

###  4.1 Overview

An overview of the reviewed articles is provided in Table 1. The non-humanoid
robots used in these articles varied in morphologies and functionalities
(Figure 1). Eleven (44%) articles adopted readily available robots developed
in previous work Bretan et al. (2015); Gácsi et al. (2016); Hoggenmueller et
al. (2020); Ritschel et al. (2019); Shi et al. (2018) or commercially
available Boccanfuso et al. (2015); Cauchard et al. (2016); Frederiksen and
Stoy (2020); Hieida et al. (2016); Song and Yamada (2018); Whittaker et al.
(2021), while other thirteen (52%) articles designed and prototyped robots for
the specific purpose of investigating emotional expressions Bucci et al.
(2018); Chase and Follmer (2019); Frederiksen and Stoy (2020); Harris and
Sharlin (2011); Hoffman et al. (2015); Kim and Follmer (2019); Löffler et al.
(2018); Peng et al. (2020); Sato et al. (2020); Song and Yamada (2017); Tan et
al. (2016); Tennent et al. (2018); Herdel et al. (2021). The remaining one
article implemented the design from another robot using Lego robot parts
Novikova and Watts (2014). Two articles developed animations instead of using
a physical embodiment to simulate robots (a car seat in Tennent et al. (2018)
and a drone in Herdel et al. (2021)). Regardless of whether the robots had a
designated functionality per se, four robots had a perceivable utilitarian
function during the evaluation of emotions, including two voice assistants Shi
et al. (2018); Whittaker et al. (2021), one drone Cauchard et al. (2016), and
one car seat Tennent et al. (2018). Another two robots served as companions
during the studies reported in the articles Boccanfuso et al. (2015); Hoffman
et al. (2015).

Figure 1: Examples of non-humanoid robots from the reviewed articles Bretan et
al. (2015); Herdel et al. (2021); Whittaker et al. (2021); Hoggenmueller et
al. (2020); Novikova and Watts (2014); Song and Yamada (2017); Peng et al.
(2020); Tan et al. (2016); Bucci et al. (2018); Chase and Follmer (2019); Kim
and Follmer (2019); Tennent et al. (2018)

Table 1: Articles in the Review of Emotionally Expressive Non-Humanoid Robots

Year | Authors |  Robot Prototype |  Emotion Models |  Output Modalities
---|---|---|---|---
2011 | Harris and SharlinHarris and Sharlin (2011) |  The Stem |  angry, happy, sad, etc. |  movement
2014 | Novikova and WattsNovikova and Watts (2014) |  a Lego robot based on a Phobot robot’s design |  Mehrabian’s model |  movement
2015 | Boccanfuso et al.Boccanfuso et al. (2015) |  Sphero |  angry, fearful, happy, sad |  colour, movement, sound
2015 | Bretan et al.Bretan et al. (2015) |  Shimi |  Ekman’s basic emotions,
Russell’s circumplex model |  movement
2015 | Hoffman et al.Hoffman et al. (2015) |  Kip1 |  calm, curious, scared |  movement
2016 | Cauchard et al.Cauchard et al. (2016) |  AR.Drone 2.0 by Parrot |  personalities from Walt Disney’s Seven Dwarfs and Peyo’s Smurfs |  movement
2016 | Gácsi et al.Gácsi et al. (2016) |  PeopleBot |  Ekman’s basic emotions |  movement, sound
2016 | Hieida et al.Hieida et al. (2016) |  “Rolling Spider” Drone by Parrot |  anger, joy, pleasure, and sadness from a Japanese idiom |  movement
2016 | Tan et al.Tan et al. (2016) |  a shape-changing interface |  Ekman’s basic emotions, Mehrabian’s model |  movement
2017 | Song and YamadaSong and Yamada (2017) |  Maru |  Russell’s circumplex model |  colour, movement, sound
2018 | Bucci et al.Bucci et al. (2018) |  FlexiBit with a fur cover |  emotional valence |  haptics, movement
2018 | Löffler et al.Löffler et al. (2018) |  a simple, wheeled robot probe |  Ekman’s basic emotions |  colour, movement, sound
2018 | Shi et al.Shi et al. (2018) |  a smartphone-based voice assistant |  Russell’s circumplex model |  facial expression, movement
2018 | Song and YamadaSong and Yamada (2018) |  Roomba |  Ekman’s basic emotions,
Russell’s circumplex model |  colour, movement
2018 | Tennent et al.Tennent et al. (2018) |  3D animation for a robot car seat |  aggressive, confident, cool,
excited, quirky |  movement
2019 | Chase and FollmerChase and Follmer (2019) |  a device with a visible and graspable handle |  Mehrabian’s model |  haptics, movement
2019 | Kim and FollmerKim and Follmer (2019) |  SwarmHaptics |  Ekman’s basic emotions |  haptics, movement
2019 | Ritschel et al.Ritschel et al. (2019) |  BärBot |  happiness, sadness, etc. |  sound
2020 | Frederiksen and StoyFrederiksen and Stoy (2020) |  three Thymio II Robots |  fear |  movement, sound
2020 | Frederiksen and StoyFrederiksen and Stoy (2020) |  Affecta |  remorse |  colour, facial expression, movement, sound
2020 | Hoggenmueller et al.Hoggenmueller et al. (2020) |  Woodie |  Ekman’s basic emotions |  colour, movement
2020 | Peng et al.Peng et al. (2020) |  three small robot characters for a robot theatre |  Ekman’s basic emotions |  facial expression, movement
2020 | Sato et al.Sato et al. (2020) |  tabletop, wheeled haptic robots |  Russell’s circumplex model |  haptics
2021 | Herdel et al.Herdel et al. (2021) |  animated drone with DJI Phantom 3 body |  Ekman’s basic emotions |  facial expression
2021 | Whittaker et al.Whittaker et al. (2021) |  Olly |  “Big Five” personality theory |  colour, movement, sound

2

The robots were ascribed with a set of distinct expressive behaviours
corresponding to specific emotions. These expressive behaviours were encoded
through a variety of output modalities, supporting inferences of the robots’
internal states and broadening the range of feasible cues applicable to
designing affective interfaces. Twenty-three (92%) articles investigated the
encoding of multiple emotions and evaluated the effectiveness of one or more
modalities in displaying the emotions, while the remaining two articles simply
tested the perception and impact of a single emotion Frederiksen and Stoy
(2020, 2020). To support a more interactive evaluation process, robots in
eight (32%) articles were able to express emotions based on the behaviours of
participants Boccanfuso et al. (2015); Bretan et al. (2015); Frederiksen and
Stoy (2020); Harris and Sharlin (2011); Hoffman et al. (2015); Ritschel et al.
(2019); Shi et al. (2018); Whittaker et al. (2021).

The purpose of robotic emotion design in fifteen (60%) articles was to explore
the use of a modality or multiple modalities. Eight of these articles aimed to
address the feasibility or effectiveness of the chosen modality/modalities in
developing the emotional expressions Harris and Sharlin (2011); Bucci et al.
(2018); Herdel et al. (2021); Hieida et al. (2016); Löffler et al. (2018);
Song and Yamada (2018, 2017); Chase and Follmer (2019), while the other seven
of the fifteen articles focused on providing design strategies for using the
modality/modalities to encode emotions Novikova and Watts (2014); Cauchard et
al. (2016); Sato et al. (2020); Tennent et al. (2018); Tan et al. (2016);
Gácsi et al. (2016); Kim and Follmer (2019). The remaining ten (40%) articles
had a main purpose other than evaluating modalities. Six of them examined the
influence of emotional expressions on user cognition Frederiksen and Stoy
(2020); Shi et al. (2018) or behaviour Hoffman et al. (2015); Boccanfuso et
al. (2015); Frederiksen and Stoy (2020); Peng et al. (2020). Two articles
evaluated the efficacy of their proposed approach for the robot expressing
emotions dynamically Bretan et al. (2015); Ritschel et al. (2019). As for the
remaining two articles, one Whittaker et al. (2021) aimed to understand user
preference for the robot’s personality traits, and the other Hoggenmueller et
al. (2020) explored various contextual factors influencing user perception of
the emotional expressions.

###  4.2 Emotion Models

To understand what emotions are commonly expressed by non-humanoid robots,
this section presents the emotion models that guided the selection of emotions
in the reviewed articles. We identified three emotion models, which are used
to structure this section. Two streams of models are based on previous
literature, namely categorical models and dimensional models Bretan et al.
(2015); Hoggenmueller et al. (2020); Novikova and Watts (2014); Tan et al.
(2016). We further added emotional personas as a third model since we found
that some articles developed emotional personas for the robots and selected
emotions in accordance with the personas to articulate corresponding
personalities.

####  4.2.1 Categorical Models

Nineteen (76%) articles encoded categorical emotions (e.g. happiness and
sadness). The most employed categorical emotion model is Ekman’s six basic
emotions Ekman and Friesen (1971) including anger, disgust, fear, happiness,
sadness and surprise. These emotions are regarded as essential for human-human
communication, easy to understand, and widely recognisable across cultural
backgrounds Bretan et al. (2015); Herdel et al. (2021); Hoggenmueller et al.
(2020); Löffler et al. (2018). Besides using a validated psychological model,
two studies referred to cultural conventions. Hieida et al. derived anger,
joy, pleasure, and sadness from a popular Japanese idiom, ki-do-ai-raku Hieida
et al. (2016), while Cauchard et al. Cauchard et al. (2016) chose emotional
states (brave, dopey, grumpy, happy, sad, scared, shy, and sleepy) from
personalities found in Walt Disney’s Seven Dwarfs and Peyo’s Smurfs.

####  4.2.2 Dimensional Models

Some studies argued that discrete emotions were unable to cover a
comprehensive space of emotions since human emotions comprise not only basic
emotions but also subtle variations within each category Novikova and Watts
(2014); Sato et al. (2020). For instance, joyous, content, and jubilant
describe different levels of happiness Bretan et al. (2015). Consequently,
dimensional approaches were utilised in nine (36%) articles. This includes
three articles Bretan et al. (2015); Tan et al. (2016); Song and Yamada (2018)
that conducted multiple studies using both categorical emotion models and
dimensional emotion models. Two mostly adopted dimensional models are
Russell’s circumplex model Russell (1980) and Mehrabian’s model Mehrabian
(1996). Russell’s circumplex model positions emotions on a two-dimensional
space: valence and arousal, where valence refers to the positive or negative
connotation of the emotion Hoggenmueller et al. (2020), and arousal means the
intensity of the emotion. Mehrabian’s model distributes emotions in a three-
dimensional space: pleasure-arousal-dominance (PAD), also known as valence-
arousal-dominance, in which the dominance dimension measures the controlling
or submissive nature of the emotion Herdel et al. (2021). For example,
although both anger and fear are negative emotions, the former is perceived as
dominant while the latter is submissive.

####  4.2.3 Emotional Personas

Two articles (8%) used emotions to portray personalities for different
personas Cauchard et al. (2016); Whittaker et al. (2021). Instead of being
presented with emotions directly, participants in these two articles were
faced with stereotype emotional personas Whittaker et al. (2021) in which
typical emotions were expressed and discerned. Cauchard et al. Cauchard et al.
(2016) integrated selected emotional traits into three representative personas
(adventurer, anti-social, and exhausted) for drones. For example, the
behaviours of an adventurer drone showed a combination of happiness and
bravery. In the work of Whittaker et al. Whittaker et al. (2021), a voice-
assisted home robot was assigned to three distinct personas (buddy, butler,
and sidekick) which were derived based on the well-known “Big Five”
personality traits Goldberg (1992) and differed in perceived emotions via
speech, intonation, colour, and movement when responding to people’s commands.

###  4.3 Output Modalities

In order to answer how the emotions are displayed, this section demonstrates a
variety of output modalities across visual, auditory, and haptic channels in
the articles for creating affective interfaces for non-humanoid robots to
manifest emotions. We classified the modalities into the sensory categories
with reference to how these modalities were sensed by users during evaluation.

####  4.3.1 Visual Modalities

Twenty-three (92%) articles utilised visual modalities including movement,
colour, and facial expression. Many of the robots presented in the articles
used a combination of visual modalities.

The most employed modality was movement, which was found in twenty-two
articles. Six articles used movement to encode emotions based on suggestions
in prior work Chase and Follmer (2019); Boccanfuso et al. (2015);
Hoggenmueller et al. (2020); Song and Yamada (2017); Tan et al. (2016);
Whittaker et al. (2021). For instance, for a robot placed in a natural play
scenario with young children, Boccanfuso et al. Boccanfuso et al. (2015) used
movements that were previously reported to be indicative of emotions in
children. Tan et al. Tan et al. (2016) designed shape-changing movements by
reviewing biological motion studies that had demonstrated relations between
emotions and shape-changing parameters such as velocity and orientation. In
addition to referencing previous findings directly, four articles created
emotional movements based on validated knowledge systems including the Laban’s
movement framework Sharma et al. (2013) which was adopted in three articles
Hieida et al. (2016); Novikova and Watts (2014); Tennent et al. (2018) and the
Interaction Vocabulary Lenz et al. (2013) used in one article Cauchard et al.
(2016). Furthermore, metaphorical mappings were utilised in four articles
Gácsi et al. (2016); Löffler et al. (2018); Shi et al. (2018); Song and Yamada
(2018). For example, Löffler et al. Löffler et al. (2018) used conceptual
metaphors such as “joy is up and active” and “anger is hot fluid in a
container” to develop movement patterns. Shi et al. Shi et al. (2018) designed
emotional movements for text boxes on a smart-phone based voice assistant
using affective human body expressions. Besides prescribing movement–emotion
mappings in advance, four articles explored how users specified the
relationships between emotions and movements Bucci et al. (2018); Harris and
Sharlin (2011); Kim and Follmer (2019); Song and Yamada (2018). Despite the
non-humanoid form of the robots, six articles designed emotional body
expressions Bretan et al. (2015); Frederiksen and Stoy (2020); Gácsi et al.
(2016); Hieida et al. (2016); Hoffman et al. (2015); Novikova and Watts
(2014). Except for the body expressions for drones in Hieida et al. (2016)
which still remained in the realm of mechanical movements, the other five
articles followed anthropomorphic or zoomorphic behaviours such as stretching
the “neck” for a lamp-like robot to show curiosity Hoffman et al. (2015) or
borrowing emotional behavioural patterns from dogs Gácsi et al. (2016).

Coloured lights were used in seven articles. Three articles Löffler et al.
(2018); Song and Yamada (2017, 2018) corresponded one emotion to one colour,
e.g. anger–red, sadness–blue, calm–white, etc. Among them, the two articles by
Song and Yamada Song and Yamada (2017, 2018) generated the encoding based on
prior research while the work of Löffler et al. Löffler et al. (2018) referred
to conceptual metaphors like “joy is light and warm” and “fear is darkness”.
Additionally, four articles Boccanfuso et al. (2015); Frederiksen and Stoy
(2020); Hoggenmueller et al. (2020); Whittaker et al. (2021) combined multiple
visual properties of coloured lights to reflect single emotions. For example,
Hoggenmueller et al. Hoggenmueller et al. (2020) developed colour patterns
with animation effects, e.g. blur and fade green and yellow colours for
showing disgust, and Boccanfuso et al. Boccanfuso et al. (2015) used bright
and colourful lights with high intensity to convey happiness. For generating
the mappings between multiple colour properties and emotions, Whittaker et al.
conducted an online user study to decide colour patterns for different
emotional personas while the other three articles referenced prior work.

Four articles reported the design of facial expressions for conveying emotions
Frederiksen and Stoy (2020); Herdel et al. (2021); Peng et al. (2020); Shi et
al. (2018). Herdel et al. Herdel et al. (2021) used the Facial Action Coding
System (FACS) to establish associations between basic emotions and animated
facial expressions on drones. Shi et al. Shi et al. (2018) developed facial
expressions for their smart-phone based voice assistant by first seeking well-
received cartoon designs and then selecting the final expressions through an
online survey. Instead of using multiple facial features, Peng et al. Peng et
al. (2020) and Frederiksen and Stoy Frederiksen and Stoy (2020) designed only
eye patterns for displaying emotions. Peng et al. Peng et al. (2020) created
eyes with various shapes and colours for their small theatre robots and
identified the mappings with emotions using online questionnaires, while the
animated eyes on a phone mounted on the robot in Frederiksen and Stoy (2020)
simply showed paying attention and conveyed an apologetic state in a scolding
scenario.

####  4.3.2 Auditory Modalities

Eight (32%) articles employed auditory modalities including non-linguistic
utterances (NLUs), music, and vocalisations.

NLUs are usually defined as “robot-like” mechanical sounds (e.g. chirps,
beeps, whirrs, etc.) Read and Belpaeme (2016) and are often used as affective
cues in HCI Read and Belpaeme (2016); Song and Yamada (2017). Five articles
included NLUs for cueing emotions Frederiksen and Stoy (2020, 2020); Gácsi et
al. (2016); Löffler et al. (2018); Song and Yamada (2017). For instance,
mechanical sounds such as chirps and beeps varied in tones and rhythms were
associated with different emotions in Gácsi et al. (2016); Löffler et al.
(2018); Song and Yamada (2017). In both articles by Frederiksen and Stoy
Frederiksen and Stoy (2020, 2020), NLUs were used to show single emotions
(e.g. alerting audio signals to express fear Frederiksen and Stoy (2020) and
augmented naturally occurring sounds of the robot to convey remorse
Frederiksen and Stoy (2020)).

Two articles utilised music that was validated to evoke emotions before
Boccanfuso et al. (2015); Ritschel et al. (2019). Ritschel et al. Ritschel et
al. (2019) used previously proposed melodies to show emotions and intentions
and personalised the timbre dynamically according to user preferences.
Boccanfuso et al. Boccanfuso et al. (2015) produced a set of synthesised music
to enhance the robot’s emotional expressions in the play environment with
young children (e.g. a happy state was conveyed with a piece of music with
frequent and smooth changes in a moderate to high pitch).

Vocalisations were found in two articles Boccanfuso et al. (2015); Whittaker
et al. (2021). Whittaker et al. Whittaker et al. (2021) implemented humanoid
speech and intonation to articulate three distinct personas in the voice-
assisted home robot, while Boccanfuso et al. Boccanfuso et al. (2015) added
non-linguistic child vocalisations, such as giggle and crying, simply to
augment the main sound cue (i.e. music).

####  4.3.3 Haptic Modalities

Haptic modalities, including haptic movements and textures, were used in four
(16%) articles. In one of those articles, the robot was covered in a
naturalistic fur to mimic furry animals and invite user touch only to assist
in the evaluation of the primary modality (i.e. breathing behaviours) Bucci et
al. (2018). The other three articles used haptics as main cues Chase and
Follmer (2019); Kim and Follmer (2019); Sato et al. (2020). In the work of
Sato et al. Sato et al. (2020), they investigated how users mapped
combinations of haptic movements (e.g. tap rapidly/slowly) and textures (e.g.
aluminium, clay, etc.) to a list of discrete emotions. Chase and Follmer Chase
and Follmer (2019) combined haptic movements with visual movements to test the
perceived pleasure-arousal-dominance (PAD) for properties like stiffness and
jitter. Kim and Follmer Kim and Follmer (2019) assessed perceived PAD in a
swarm of small haptic devices by changing parameters including the number of
robots, force types, frequency, and amplitude.

###  4.4 Evaluation Measures

All of the reviewed articles included user evaluations to assess the quality
and impact of the emotional expressions. This section discusses the evaluation
measures in terms of use scenarios, experimental tasks, and evaluated aspects.

####  4.4.1 Use Scenarios

Eleven (44%) articles created use scenarios for robots displaying emotions in
the evaluation. Three of the articles embedded the emotional expressions into
the robots’ tasks Cauchard et al. (2016); Shi et al. (2018); Whittaker et al.
(2021). For instance, the drone in Cauchard et al. (2016) displayed emotional
profiles during different flying tasks. The two voice assistants in Shi et al.
(2018); Whittaker et al. (2021) conveyed emotional states during tasks
activated by users’ spoken commands, e.g. setting a reminder Shi et al. (2018)
and playing a music Whittaker et al. (2021). Six articles created scenarios
specifically for the emotional expressions Frederiksen and Stoy (2020, 2020);
Peng et al. (2020); Tennent et al. (2018); Novikova and Watts (2014); Hoffman
et al. (2015). The car seat in Tennent et al. (2018) showed expressive
movements when greeting its driver. Peng et al. designed plots for the robot
actors in the robot theatre to show contextual emotions Peng et al. (2020).
The conversation companion robot in Hoffman et al. (2015) responded to humans’
vocalics during a conflict conversation between couples. The robot in
Frederiksen and Stoy (2020) was placed in a scolding scenario in order to
convey remorse. Two articles intentionally designed triggers for the emotions
in order to help with users’ understanding, such as a scenario where the robot
showed positive emotions after finishing its task successfully Novikova and
Watts (2014) and a high volume explosion sound to evoke the robot’s fearful
reactions Frederiksen and Stoy (2020). Using a more natural context,
Boccanfuso et al. placed the robot into an unstructured play environment with
young children for eliciting their affective responses Boccanfuso et al.
(2015). Though the robot in Bretan et al. (2015) expressed emotions without
contexts in the first two studies, it responded to users’ speech in the third
one. Apart from these eleven articles, the robots’ emotional expressions in
other articles were devoid of any use case, presumably with the purpose of
evaluating the design without bias Harris and Sharlin (2011); Hoggenmueller et
al. (2020).

####  4.4.2 Experimental Tasks

In seven articles (28%), participants viewed images or watched pre-recorded or
simulated videos where the robot showcased emotional expressions Bretan et al.
(2015); Gácsi et al. (2016); Herdel et al. (2021); Hieida et al. (2016);
Novikova and Watts (2014); Peng et al. (2020); Tennent et al. (2018).
Specifically, the videos in Tennent et al. (2018) and Herdel et al. (2021)
were simulated animations. Nineteen (76%) articles presented participants with
physically embodied robots, including the article by Bretan et al. Bretan et
al. (2015) in which participants were faced with either the physical robot or
images/videos. Six of those articles asked participants to only watch the
robots displaying emotions Cauchard et al. (2016); Frederiksen and Stoy
(2020); Hoggenmueller et al. (2020); Löffler et al. (2018); Song and Yamada
(2017, 2018), whereas thirteen of the articles let participants interact with
the robots Boccanfuso et al. (2015); Bretan et al. (2015); Bucci et al.
(2018); Chase and Follmer (2019); Frederiksen and Stoy (2020); Harris and
Sharlin (2011); Hoffman et al. (2015); Kim and Follmer (2019); Ritschel et al.
(2019); Sato et al. (2020); Shi et al. (2018); Tan et al. (2016); Whittaker et
al. (2021). The contents of the interactions varied across articles. For
example, participants were instructed to touch or play with the robots in
order to experience the emotional expressions Boccanfuso et al. (2015); Bucci
et al. (2018); Chase and Follmer (2019); Harris and Sharlin (2011); Kim and
Follmer (2019); Sato et al. (2020); Tan et al. (2016). In the two articles
with voice-assisted robots Shi et al. (2018); Whittaker et al. (2021),
participants received emotions from the robot while completing specified tasks
using the robot. Furthermore, some robots were able to adjust their emotional
responses according to participants’ behaviours through either pre-programmed
mechanisms Boccanfuso et al. (2015); Bretan et al. (2015); Frederiksen and
Stoy (2020); Hoffman et al. (2015); Ritschel et al. (2019) or a Wizard of Oz
procedure Harris and Sharlin (2011).

####  4.4.3 Evaluated Aspects

Twelve articles (48%) gauged the recognition level of the emotional
expressions, that is, the rate that an emotion was correctly recognised
through its expression. In nine articles, participants matched presented
expressions with a list of emotion names Cauchard et al. (2016); Gácsi et al.
(2016); Herdel et al. (2021); Löffler et al. (2018); Novikova and Watts
(2014); Sato et al. (2020); Song and Yamada (2017); Tan et al. (2016); Tennent
et al. (2018), whereas the other three articles asked participants to rate how
much they thought the presented expressions matched the prescribed emotions on
scales Bretan et al. (2015); Hoggenmueller et al. (2020); Ritschel et al.
(2019).

Other characteristics of the emotional expressions were also measured. Five
articles used the Self-Assessment Mannequin (SAM) scale Bradley and Lang
(1994) to measure the perceived valence, arousal, and dominance of the
expressions Chase and Follmer (2019); Herdel et al. (2021); Kim and Follmer
(2019); Song and Yamada (2018); Tan et al. (2016). Several articles evaluated
valence Bucci et al. (2018); Frederiksen and Stoy (2020); Hieida et al.
(2016), arousal (or intensity) Cauchard et al. (2016); Frederiksen and Stoy
(2020); Herdel et al. (2021); Hieida et al. (2016), and dominance Frederiksen
and Stoy (2020) independently. Two articles Harris and Sharlin (2011); Hieida
et al. (2016) asked participants to rate each emotional expression on a scale
of pairs of opposite adjectives like “tired vs. energetic” and “hasty vs.
leisurely”. Other traits such as emotional and cognitive engagement Shi et al.
(2018), perceived urgency Kim and Follmer (2019), and the impact of emotions
on surroundings Frederiksen and Stoy (2020); Herdel et al. (2021) were also
measured. Additionally, some articles assessed the level of anthropomorphism
of their robot. For instance, two articles employed HRI metrics for gauging
the perceived anthropomorphism, likeability and safety of the robots Chase and
Follmer (2019); Kim and Follmer (2019). Similarly, social human character
traits like friendliness, cooperativeness, sociability, etc., as well as
participants’ comfort level with the robot were assessed in one of the
articles Hoffman et al. (2015).

Three articles analysed participants’ behaviours during their interaction with
the robots using video coding Boccanfuso et al. (2015); Hoffman et al. (2015)
or direct observation Harris and Sharlin (2011). Through the analysis of video
coding data, Boccanfuso et al. Boccanfuso et al. (2015) characterised
different play patterns and affective responses of young children, and Hoffman
et al. Hoffman et al. (2015) collected verbal references to the robot during
simulated couple conflicts. In the direct observation procedure in Harris and
Sharlin (2011), think-aloud was used for participants to express feelings and
thoughts during their interaction with the moving robot. Moreover, nine
articles presented and discussed participants’ comments on their experience
with the emotionally expressive robot, either from interviews or open-ended
questions in questionnaires Cauchard et al. (2016); Bucci et al. (2018);
Harris and Sharlin (2011); Herdel et al. (2021); Hoffman et al. (2015);
Hoggenmueller et al. (2020); Peng et al. (2020); Ritschel et al. (2019);
Whittaker et al. (2021).

###  4.5 User Perceptions

To discuss user perceptions of the emotional expressions in the reviewed
articles, we summarise common findings reported including the recognition
level of emotions and other important aspects associated with user
perceptions.

####  4.5.1 Recognition of Emotional Expressions

Basic emotions that are relatively obvious and universal Cauchard et al.
(2016); Tan et al. (2016) were best recognised by participants, including
happiness Cauchard et al. (2016); Gácsi et al. (2016); Herdel et al. (2021);
Hoggenmueller et al. (2020); Novikova and Watts (2014); Peng et al. (2020),
sadness Bretan et al. (2015); Herdel et al. (2021); Hoggenmueller et al.
(2020); Ritschel et al. (2019), and anger Hoggenmueller et al. (2020); Peng et
al. (2020). However, sadness was at the same time found to be the least
recognisable emotion in three articles Gácsi et al. (2016); Novikova and Watts
(2014); Peng et al. (2020). Two other negatively valenced emotions, fear
Bretan et al. (2015); Herdel et al. (2021) and disgust Herdel et al. (2021);
Tan et al. (2016), were also the most difficult to identify. This is in line
with research in psychology showing that some emotions are more easily
recognised than others Herdel et al. (2021) and that negative emotions tend to
be recognised slower Herdel et al. (2021) and are less consistently
interpreted correctly Tennent et al. (2018) compared to positive emotions.
Furthermore, emotions like surprise, coolness, and affirmation that are
believed to be more abstract and need more contexts to interpret Hoggenmueller
et al. (2020); Kim and Follmer (2019) were also rated low in terms of
recognition Hoggenmueller et al. (2020); Ritschel et al. (2019); Tennent et
al. (2018).

When combining emotions with modalities, we found that the interpretation of
emotional expressions was mostly the intuitive comprehension of the
emotion–expression relationship. For example, in most studies where movement
was used, participants were likely to associate high speed or high frequency
with emotions with high arousal such as excitement or anger Cauchard et al.
(2016); Harris and Sharlin (2011); Hoggenmueller et al. (2020); Kim and
Follmer (2019); Novikova and Watts (2014); Song and Yamada (2017); Tan et al.
(2016), while a low speed or an avoidance behaviour were usually interpreted
as less intense emotions like sadness or fear Hoggenmueller et al. (2020);
Novikova and Watts (2014); Song and Yamada (2018). Similarly, participants
were able to understand intuitive, conventional mappings for other modalities.
For instance, a falling sound or slow tempo were usually interpreted as
conveying sadness Löffler et al. (2018); Ritschel et al. (2019); Song and
Yamada (2017), and bright and fast-changing colours were commonly associated
with joy Hoggenmueller et al. (2020); Löffler et al. (2018); Whittaker et al.
(2021).

####  4.5.2 Sociability

Many articles reported that participants attributed liveliness, internal
states, and sociability to the emotionally expressive robots. For example,
participants in one article Bucci et al. (2018) referred to the emotional
furry robot as having “a rich inner life” and reminding them of pets. In the
storytelling section in another article Peng et al. (2020), children
interpreted motivations, intentions and emotions from the performance of the
robots in the robot theatre. In Hoffman et al. (2015), the companion robot was
perceived as friendly, warm, and capable of forming social bonds and
attachments. The drone with facial expressions in Herdel et al. (2021) was
described as an agent with autonomy, consciousness, and cognitive and
behavioural abilities.

Nevertheless, disengagement was found when robots displayed certain emotional
expressions. Harris and Sharlin Harris and Sharlin (2011) reported that nearly
half of their participants showed boredom when presented with slow and
repetitive movements. For the voice-assisted home robot in Whittaker et al.
(2021), the “sidekick” persona which had a low amplitude voice and used slow
movements also failed to engage some participants emotionally. Besides,
Boccanfuso et al. Boccanfuso et al. (2015) suggested that negative emotions
that elicited frustration and annoyance might cause disengagement in children.
In the investigation of emotional and cognitive engagement of the emotionally
expressive voice assistant, Shi et al. Shi et al. (2018) found that emotions
with positive valence and high arousal might help robots to establish
emotional connections with humans.

####  4.5.3 Contexts

Context was regarded as an important factor influencing users’ interpretation
of emotional expressions. In Novikova and Watts (2014), the recognition rate
of emotions improved dramatically when emotions were displayed within an
appropriate context compared to displayed alone. Tan et al. Tan et al. (2016)
also suggested that adding a use scenario could help users identify and
disambiguate emotions. In order to interpret relatively more abstract emotions
such as fear and surprise, participants in Kim and Follmer (2019) tended to
combine the haptic expressions with other factors such as motion paths and the
contact locations of the haptic stimuli to obtain more contextual information.
Hoggenmueller et al. Hoggenmueller et al. (2020) discussed a range of
contextual aspects that impacted users’ comprehension, including
spatiotemporal context, interactional context, as well as contexts related to
users’ background.

Additionally, participants were found to create narratives for the emotional
expressions in several articles. For instance, participants in Herdel et al.
(2021) tended to develop stories to make sense of the emotions, such as
speculating about the cause of fear and surprise. Children who watched the
theatre play performed by multiple robots in Peng et al. (2020) generated
conjectures about robots’ social relationships according to the display of
emotions. After analysing participants’ verbal descriptions of the expressive
behaviours of the robot, Bucci et al. Bucci et al. (2018) concluded that
narratives made about the motivation and situation of the robot could heavily
influence the perception of emotional expressions.

##  5 Considerations

The reviewed studies show rich evidence for designing affective interfaces for
non-humanoid robots to communicate emotions. This serves as a foundation and
offers guidance for adding an emotional dimension to AV-pedestrian interfaces.
In this regard, we aim to provide preliminary considerations around core
elements for designing affective AV-pedestrian interfaces. First, we draw on
findings from the review to provide a set of considerations for designing
emotional expressions for AVs as social robots. Then, based on both the review
and current AV-pedestrian communication strategies, we present a set of
considerations that take a broader range of factors into account for building
affective AV-pedestrian interfaces.

###  5.1 Considerations for Designing Emotional Expressions

Based on findings from the review of emotional expressions of non-humanoid
robots, we propose five considerations for imbuing AVs with emotions with
regard to what emotions to communicate, and how to communicate emotions.

_Include Basic Emotions:_ More than half of the reviewed articles employed
basic emotions, which were mostly derived from Ekman’s six emotions Ekman and
Friesen (1971). Particularly, the user evaluation in these articles showed
that happiness, sadness, and anger were most easily recognised Cauchard et al.
(2016); Bretan et al. (2015); Gácsi et al. (2016); Herdel et al. (2021);
Hoggenmueller et al. (2020); Novikova and Watts (2014); Peng et al. (2020);
Ritschel et al. (2019). In general, such emotions are regarded as easy to
understand, recognisable across different cultural backgrounds, and important
for intuitive human-robot interaction Bretan et al. (2015); Herdel et al.
(2021); Hoggenmueller et al. (2020); Löffler et al. (2018). Hence, we suggest
that basic emotions or basic emotion models should be considered when deciding
what emotions to attribute to AVs.

_Use Negative Emotions for a Reason:_ In some articles where negative emotions
were triggered for a reason, these emotions demonstrated important
contributions in affecting user behaviours, e.g. defused conflict situations
when showing fear Hoffman et al. (2015) or remorse Frederiksen and Stoy
(2020), and evoked sympathetic behaviours when displaying sadness Boccanfuso
et al. (2015). Indeed, robot emotions can cause humans to mirror the emotional
state of the robot Frederiksen and Stoy (2020); Herdel et al. (2021);
Hoggenmueller et al. (2020) or to reflect on their own behaviours Frederiksen
and Stoy (2020); Herdel et al. (2021); Hoffman et al. (2015). However, when
negative emotions were displayed without reasons, users speculated about the
cause of the emotions Herdel et al. (2021), involuntarily interpreted them as
positive valenced Hoggenmueller et al. (2020), or even sometimes concerned
about their own safety under aggressive emotions Chase and Follmer (2019);
Harris and Sharlin (2011); Tennent et al. (2018). Therefore, providing reasons
for the display of negative emotions can be essential for the intended user
interpretation and the subsequent influence on user behaviours.

_Provide Contexts for Abstract Emotions:_ Some reviewed articles showed that
emotions such as surprise, disgust, coolness, and affirmation, though some
included in Ekman’s six basic emotions, are more abstract in connotation than
those universally recognisable emotions (e.g. happiness and sadness) and thus
need more contexts for users to interpret them as intended Hoggenmueller et
al. (2020); Kim and Follmer (2019); Ritschel et al. (2019); Tennent et al.
(2018). When displayed without context, these emotions can seem to be
ambiguous Tan et al. (2016), and the interpretation can be more greatly biased
by the user’s cultural background and previous experiences Hoggenmueller et
al. (2020). Hence, the expected user perception of abstract emotions in AVs
can hardly be isolated from an appropriate context, a context relating but not
limited to the task that the AV is performing Novikova and Watts (2014), the
immediate surroundings Hoggenmueller et al. (2020), and cultural norms Park et
al. (2021).

_Combine Multiple Modalities:_ Several studies compared the recognition rate
of emotions between using single and multiple modalities Löffler et al.
(2018); Song and Yamada (2017, 2018). Results showed that people recognised
the multi-modal expressions more easily and were more confident in their
judgement. Indeed, when multiple modalities are combined together to convey a
certain emotion, they tend to serve as an “amplifier” to each other Song and
Yamada (2018) and reassure people of their interpretation Löffler et al.
(2018); Song and Yamada (2017). Even in some studies where only a single
modality was tested, some participants still referred to partial cues other
than the primary modality to support their inferences Herdel et al. (2021);
Kim and Follmer (2019). Hence, using multiple modalities can be beneficial to
clarifying the emotional state of the AV and help increase users’ confidence
in making fast and safe decisions accordingly.

_Employ Intuitive Encoding:_ The review showed that emotions were best
interpreted when the encoding followed a conventional and intuitive assignment
of expression–emotion relationships, such as using colourful lights or
uplifting music for happiness Boccanfuso et al. (2015); Hoggenmueller et al.
(2020); Löffler et al. (2018); Ritschel et al. (2019); Whittaker et al. (2021)
and slow movements or dark colours for sadness Hoggenmueller et al. (2020);
Löffler et al. (2018); Song and Yamada (2017, 2018). Nonetheless, such
“intuitive” mappings can be culture-specific Peng et al. (2020); Frederiksen
and Stoy (2020); Löffler et al. (2018); Song and Yamada (2018). For instance,
some studies argue cultural differences in mapping colour to emotion Peng et
al. (2020). Besides, there are also conceptual models or universal
associations that are very little dependent on culture or can be found in many
languages Löffler et al. (2018); Peng et al. (2020). Overall, employing
encoding that is intuitive to users is important in AV-pedestrian interaction,
as such interaction requires immediate decisions in dynamic traffic
situations. Similar concerns can be found in existing AV-pedestrian eHMIs. For
example, a participant in Mahadevan et al. (2018) commented that during the
crossing scenario, they had to frequently look at a sheet which specified the
mapping between multiple LED colours and vehicle states, and they further
pointed out that it was unrealistic to bring a sheet in real life. Thus, it
should be carefully considered to use encoding rules that might require a
learning process.

###  5.2 Considerations for Building Affective AV-Pedestrian Interfaces

Drawing on the review and taking into account current AV-pedestrian
communication strategies, this section presents a set of considerations around
a broader range of factors that may contribute to designing affective AV-
pedestrian interfaces.

_Align with AV’s Primary Functionality:_ AVs’ primary function (i.e.
transportation) is likely to influence people’s interpretation of their social
traits during the interaction. In the review, only four robots had a
perceivable primary function during user evaluation Shi et al. (2018);
Whittaker et al. (2021); Cauchard et al. (2016); Tennent et al. (2018).
However, as shown in Harris and Sharlin (2011) and Hoggenmueller et al.
(2020), when interacting with a robot, people are likely to speculate about
the functionality or “purpose” of the robot and interpret its emotions
accordingly. This suggests that people would expect the emotional expressions
of a robot to be coherent with its functionality. In the example of a voice-
assisted home robot Whittaker et al. (2021), people preferred the smart helper
to show conscientiousness and agreeableness through its expressive cues.
Therefore, the affective interface should account for its interplay with AVs’
major utilitarian purpose, i.e. serving as a secondary function Hoggenmueller
et al. (2020) and facilitating the operation of the primary one.

_Understand Pedestrian’s Expectations:_ Pedestrians’ expectations of AVs’
emotions can differ from passengers sitting inside the vehicle or other road
users. Various interaction contexts have been found in AV-pedestrian
interaction, such as interaction contents, road types, other vehicles or road
users, etc Tran et al. (2021). If AVs are to use emotions to support the
interaction with pedestrians, it is important to consider the legitimacy of
the emotions in those interaction contexts from the perspective of
pedestrians, that is, to understand what emotions pedestrians expect to see.
Similar considerations have been reported in human-drone interaction (HDI).
For instance, in the design of emotional drones, Cauchard et al. Cauchard et
al. (2016) left aside emotions that did not seem to be applicable to HDI, such
as disgust. Herdel et al. Herdel et al. (2021) also stated the concern about
whether users would envision certain emotions (e.g. fear) to appear in drones.
Besides, emotional patterns in existing driver-pedestrian interaction may
provide a vital reference for conjecturing pedestrians’ expectations of AVs’
emotions, as it is essentially the drivers’ role that the autonomous systems
take over when interacting with pedestrians socially.

_Refer to Existing eHMIs:_ Although there is no empirical evidence yet for
which modality is effective for an affective AV-pedestrian interface, efforts
made in current eHMIs for communicating AVs’ intent and awareness offer
various solutions, such as vehicle-mounted displays Chang et al. (2017);
Clamann et al. (2017); Lagström and Malmsten Lundgren (2016); Mahadevan et al.
(2018); Urmson et al. (2015), on-road projections Hesenius et al. (2018);
Löcken et al. (2019); Nguyen et al. (2019), smart-road interfaces Mairs
(2017), and wearable devices Dey et al. (2020). These interfaces provide a
range of feasible modalities across visual, auditory, and haptic channels to
support the potential communication of emotions. Nevertheless, many previous
studies show that regardless of the presence of eHMIs, pedestrians still
greatly rely on changes in the movement of AVs (e.g. speed) Clamann et al.
(2017); Epke et al. (2021); Lee et al. (2021); Pillai (2017); Rasouli and
Tsotsos (2019). Thus, the affective interface may prefer movement cues to
encode emotions (e.g. movement-related “gestures” Dey et al. (2020)), or use
emotions to amplify the intention of movements (e.g. display a happy face to
show friendliness when the vehicle is yielding). Overall, designers should
refer to existing AV-pedestrian eHMIs and their corresponding findings to
understand the usability of different types of interfaces when designing for
the affective dimension.

_Make It a Reciprocal Process:_ Current eHMIs for conveying intent and
awareness to pedestrians are mostly designed in a proactive manner. For
example, in a crossing scenario, most visual displays and on-road projections
provide information about the deceleration progress of the vehicle Clamann et
al. (2017); Hesenius et al. (2018); Pratticò et al. (2021) or inform
pedestrians when it is safe to cross Nguyen et al. (2019); Urmson et al.
(2015). However, it is also important for AVs to be responsive to pedestrians’
intentions Epke et al. (2021). A recent study by Epke et al. Epke et al.
(2021) used human gestures and eHMIs to form a bi-directional communication
between pedestrians and AVs. The study found that participants preferred the
case where an approaching AV acted (i.e. displaying “I SEE YOU” or yielding)
in accordance with pedestrians’ hand gestures. More importantly, the
communication of emotions is itself a reciprocal process in which emotional
responses can be evoked between the two subjects Herdel et al. (2021). Hence,
AVs should have the capability to express emotions not only proactively but
also in response to the behaviours of pedestrians or contingencies in their
surroundings.

_Account for Contextual Factors:_ As suggested in previous sections, the
display of emotion should be appropriate and plausible within the specific
context. However, a number of contextual factors can condition pedestrians’
interpretation of the emotional expression, such as pedestrians’ own
background, the environment where the AV is situated in, and the way the AV
interacts with pedestrians Fischer et al. (2019); Hoggenmueller et al. (2020);
Novikova and Watts (2014); Pillai (2017). For instance, cultural norms can
influence how users perceive the emotional expression Fischer et al. (2019)
and also the appropriateness of the situation in which the emotion is
expressed Park et al. (2021). In the deployment of an emotional self-moving
robot in a university campus Hoggenmueller et al. (2020), Hoggenmueller et al.
found that participants generally perceived the robot as in a happy state
regardless of what emotion the robot was displaying, due to the peaceful
environment and the robot’s high luminosity. Moreover, weather and road
conditions like rainy and poor lighting can impact the effectiveness of the
AV’s interface Pillai (2017); Rasouli and Tsotsos (2019). Hence, the design of
affective AV-pedestrian interfaces should account for various contextual
factors, including cultural, environmental, and interactional aspects.

_Consider Evaluation Environments:_ Most AV-pedestrian interaction solutions
are evaluated in lab-based simulated environments since building fully
functional prototypes on actual AVs can be expensive, complicated, and
sometimes dependent on traffic regulations Tran et al. (2021). On the one
hand, simulation-based prototypes such as virtual reality (VR) simulations are
more feasible and flexible than physical ones deployed in the wild; on the
other, they are still weaker in terms of ecological validity Albastaki et al.
(2020). Furthermore, VR-based evaluations can be divided into immersive VR
(i.e. use wearable equipment and immerse users in the virtual world) and non-
immersive VR (i.e. users experience the virtual world via a computer screen)
Albastaki et al. (2020), in which the former provides a better presence of the
AV and the latter is less confined by hardware and allows for remote user
testing. Nevertheless, studies in affective robotics show that the proximity
and presence of the robot can impact people’s perceptions of its social
intentions Bretan et al. (2015); Tennent et al. (2018). Therefore, evaluation
environments should be considered and compared, particularly in the context of
both AV-pedestrian interaction and affective robotic design.

##  6 Conclusion

In this paper, we investigated the potential role of affective interfaces for
AVs to communicate emotions to pedestrians. To understand possible solutions
to this problem, we systematically reviewed 25 articles about the emotional
expressions of non-humanoid robots in the past ten years. The review
summarised core aspects including emotion models, output modalities,
evaluation measures, as well as user perceptions. Building on findings from
the review and taking into account current AV-pedestrian communication
strategies, we proposed five considerations for designing emotional
expressions for AVs and six considerations regarding a broader range of
factors contributing to building affective AV-pedestrian interfaces. Our
findings provide a foundation for incorporating an affective dimension into
AV-pedestrian eHMIs to increase the social acceptance of AVs and highlight
avenues for investigating these opportunities in future research.

Conceptualization, Y.W., L.H. and M.T.; methodology, Y.W., L.H. and M.T.;
formal analysis, Y.W.; writing—original draft preparation, Y.W.;
writing—review and editing, Y.W., L.H. and M.T.; visualization, Y.W.;
supervision, L.H. and M.T. All authors have read and agreed to the published
version of the manuscript.

This research was funded by the Australian Research Council through grant
number DP200102604 Trust and Safety in Autonomous Mobility Systems: A Human-
centred Approach.

Not applicable.

Not applicable.

The authors declare no conflict of interest.

References

yes

## References

  * Litman (2017) Litman, T.  Autonomous vehicle implementation predictions; Victoria Transport Policy Institute Victoria, Canada, 2017.
  * Rasouli and Tsotsos (2019) Rasouli, A.; Tsotsos, J.K.  Autonomous vehicles that interact with pedestrians: A survey of theory and practice.  IEEE transactions on intelligent transportation systems 2019, 21, 900–918.
  * Lagström and Malmsten Lundgren (2016) Lagström, T.; Malmsten Lundgren, V.  AVIP-Autonomous vehicles’ interaction with pedestrians-An investigation of pedestrian-driver communication and development of a vehicle external interface.  Master’s thesis, 2016.
  * Mahadevan et al. (2018) Mahadevan, K.; Somanath, S.; Sharlin, E., Communicating Awareness and Intent in Autonomous Vehicle-Pedestrian Interaction.  In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2018; p. 1–12.
  * Nguyen et al. (2019) Nguyen, T.T.; Holländer, K.; Hoggenmueller, M.; Parker, C.; Tomitsch, M.  Designing for Projection-Based Communication between Autonomous Vehicles and Pedestrians.  Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2019; AutomotiveUI ’19, p. 284–294.  doi:black10.1145/3342197.3344543.
  * Pratticò et al. (2021) Pratticò, F.G.; Lamberti, F.; Cannavò, A.; Morra, L.; Montuschi, P.  Comparing State-of-the-Art and Emerging Augmented Reality Interfaces for Autonomous Vehicle-to-Pedestrian Communication.  IEEE Transactions on Vehicular Technology 2021, 70, 1157–1168.
  * Chang et al. (2017) Chang, C.M.; Toda, K.; Sakamoto, D.; Igarashi, T.  Eyes on a Car: An Interface Design for Communication between an Autonomous Car and a Pedestrian.  Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2017; AutomotiveUI ’17, p. 65–73.  doi:black10.1145/3122986.3122989.
  * Löcken et al. (2019) Löcken, A.; Golling, C.; Riener, A.  How Should Automated Vehicles Interact with Pedestrians? A Comparative Analysis of Interaction Concepts in Virtual Reality.  Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2019; AutomotiveUI ’19, p. 262–274.  doi:black10.1145/3342197.3344544.
  * Cauchard et al. (2016) Cauchard, J.R.; Zhai, K.Y.; Spadafora, M.; Landay, J.A.  Emotion encoding in human-drone interaction.  2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2016, pp. 263–270.
  * Picard (2003) Picard, R.W.  Affective computing: challenges.  International Journal of Human-Computer Studies 2003, 59, 55–64.
  * Fong et al. (2003) Fong, T.; Nourbakhsh, I.; Dautenhahn, K.  A survey of socially interactive robots.  Robotics and autonomous systems 2003, 42, 143–166.
  * Leite et al. (2013) Leite, I.; Martinho, C.; Paiva, A.  Social robots for long-term interaction: a survey.  International Journal of Social Robotics 2013, 5, 291–308.
  * Bretan et al. (2015) Bretan, M.; Hoffman, G.; Weinberg, G.  Emotionally expressive dynamic physical behaviors in robots.  International Journal of Human-Computer Studies 2015, 78, 1–16.
  * Gácsi et al. (2016) Gácsi, M.; Kis, A.; Faragó, T.; Janiak, M.; Muszyński, R.; Miklósi, Á.  Humans attribute emotions to a robot that shows simple behavioural patterns borrowed from dog behaviour.  Computers in Human Behavior 2016, 59, 411–419.
  * Ritschel et al. (2019) Ritschel, H.; Aslan, I.; Mertes, S.; Seiderer, A.; André, E.  Personalized synthesis of intentional and emotional non-verbal sounds for social robots.  2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2019, pp. 1–7.
  * Eyssel et al. (2010) Eyssel, F.; Hegel, F.; Horstmann, G.; Wagner, C.  Anthropomorphic inferences from emotional nonverbal cues: A case study.  19th international symposium in robot and human interactive communication. IEEE, 2010, pp. 646–651.
  * Löffler et al. (2018) Löffler, D.; Schmidt, N.; Tscharn, R.  Multimodal Expression of Artificial Emotion in Social Robots Using Color, Motion and Sound.  Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction; Association for Computing Machinery: New York, NY, USA, 2018; HRI ’18, p. 334–343.  doi:black10.1145/3171221.3171261.
  * Boccanfuso et al. (2015) Boccanfuso, L.; Kim, E.S.; Snider, J.C.; Wang, Q.; Wall, C.A.; DiNicola, L.; Greco, G.; Flink, L.; Lansiquot, S.; Ventola, P.; others.  Autonomously detecting interaction with an affective robot to explore connection to developmental ability.  2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2015, pp. 1–7.
  * Herdel et al. (2021) Herdel, V.; Kuzminykh, A.; Hildebrandt, A.; Cauchard, J.R., Drone in Love: Emotional Perception of Facial Expressions on Flying Robots.  In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2021.
  * Whittaker et al. (2021) Whittaker, S.; Rogers, Y.; Petrovskaya, E.; Zhuang, H.  Designing Personas for Expressive Robots: Personality in the New Breed of Moving, Speaking, and Colorful Social Home Robots.  J. Hum.-Robot Interact. 2021, 10.  doi:black10.1145/3424153.
  * Harris and Sharlin (2011) Harris, J.; Sharlin, E.  Exploring the affect of abstract motion in social human-robot interaction.  2011 Ro-Man. IEEE, 2011, pp. 441–448.
  * Hoggenmueller et al. (2020) Hoggenmueller, M.; Chen, J.; Hespanhol, L.  Emotional Expressions of Non-Humanoid Urban Robots: The Role of Contextual Aspects on Interpretations; Association for Computing Machinery: New York, NY, USA, 2020; PerDis ’20, p. 87–95.  doi:black10.1145/3393712.3395341.
  * Tennent et al. (2018) Tennent, H.; Moore, D.; Ju, W.  Character Actor: Design and Evaluation of Expressive Robot Car Seat Motion.  Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2018, 1.  doi:black10.1145/3161407.
  * Monceaux et al. (2009) Monceaux, J.; Becker, J.; Boudier, C.; Mazel, A.  Demonstration: First Steps in Emotional Expression of the Humanoid Robot Nao.  Proceedings of the 2009 International Conference on Multimodal Interfaces; Association for Computing Machinery: New York, NY, USA, 2009; ICMI-MLMI ’09, p. 235–236.  doi:black10.1145/1647314.1647362.
  * Pandey and Gelin (2018) Pandey, A.K.; Gelin, R.  A mass-produced sociable humanoid robot: Pepper: The first machine of its kind.  IEEE Robotics & Automation Magazine 2018, 25, 40–48.
  * Novikova and Watts (2014) Novikova, J.; Watts, L.  A Design Model of Emotional Body Expressions in Non-Humanoid Robots.  Proceedings of the Second International Conference on Human-Agent Interaction; Association for Computing Machinery: New York, NY, USA, 2014; HAI ’14, p. 353–360.  doi:black10.1145/2658861.2658892.
  * Song and Yamada (2017) Song, S.; Yamada, S.  Expressing Emotions through Color, Sound, and Vibration with an Appearance-Constrained Social Robot.  Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction; Association for Computing Machinery: New York, NY, USA, 2017; HRI ’17, p. 2–11.  doi:black10.1145/2909824.3020239.
  * Song and Yamada (2018) Song, S.; Yamada, S.  Designing Expressive Lights and In-Situ Motions for Robots to Express Emotions.  Proceedings of the 6th International Conference on Human-Agent Interaction; Association for Computing Machinery: New York, NY, USA, 2018; HAI ’18, p. 222–228.  doi:black10.1145/3284432.3284458.
  * Peng et al. (2020) Peng, Y.; Feng, Y.L.; Wang, N.; Mi, H.  How children interpret robots’ contextual behaviors in live theatre: Gaining insights for multi-robot theatre design.  2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020, pp. 327–334.
  * Park et al. (2021) Park, S.; Healey, P.G.T.; Kaniadakis, A., Should Robots Blush?  In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2021.
  * Dey et al. (2020) Dey, D.; Habibovic, A.; Löcken, A.; Wintersberger, P.; Pfleging, B.; Riener, A.; Martens, M.; Terken, J.  Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles’ external human-machine interfaces.  Transportation Research Interdisciplinary Perspectives 2020, 7, 100174.
  * Urmson et al. (2015) Urmson, C.P.; Mahon, I.J.; Dolgov, D.A.; Zhu, J.  Pedestrian notifications.  US Patent Office 2015.  Patent No. 9 196 164 B1.
  * Colley et al. (2021) Colley, M.; Belz, J.H.; Rukzio, E.  Investigating the Effects of Feedback Communication of Autonomous Vehicles.  13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2021; AutomotiveUI ’21, p. 263–273.  doi:black10.1145/3409118.3475133.
  * Clamann et al. (2017) Clamann, M.; Aubert, M.; Cummings, M.L.  Evaluation of vehicle-to-pedestrian communication displays for autonomous vehicles.  96th Annual Transportation Research Board; , 2017.
  * Hesenius et al. (2018) Hesenius, M.; Börsting, I.; Meyer, O.; Gruhn, V.  Don’t Panic! Guiding Pedestrians in Autonomous Traffic with Augmented Reality.  Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct; Association for Computing Machinery: New York, NY, USA, 2018; MobileHCI ’18, p. 261–268.  doi:black10.1145/3236112.3236148.
  * Mairs (2017) Mairs, J.  Umbrellium develops interactive road crossing that only appears when needed 2017.
  * Colley and Rukzio (2020) Colley, M.; Rukzio, E.  A Design Space for External Communication of Autonomous Vehicles.  12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2020; AutomotiveUI ’20, p. 212–222.  doi:black10.1145/3409120.3410646.
  * Newcomb (2018) Newcomb, A.  Humans Harass and Attack Self-Driving Waymo Cars. Available online: https://www.nbcnews.com/tech/innovation/humans-harass-attack-self-driving-waymo-cars-n950971 (accessed on 11th Oct 2021).  NBC News 2018.
  * Connor (2016) Connor, S.  First Self-Driving Cars Will Be Unmarked So That Other Drivers Don’t Try to Bully Them. Available online: https://www.theguardian.com/technology/2016/oct/30/volvo-self-driving-car-autonomous (accessed on 11th Oct 2021).  The Guardian 2016.
  * Bazilinskyy et al. (2021) Bazilinskyy, P.; Sakuma, T.; de Winter, J.  What driving style makes pedestrians think a passing vehicle is driving automatically?  Applied ergonomics 2021, 95, 103428.
  * Jayaraman et al. (2019) Jayaraman, S.K.; Creech, C.; Tilbury, D.M.; Yang, X.J.; Pradhan, A.K.; Tsui, K.M.; Robert Jr, L.P.  Pedestrian trust in automated vehicles: Role of traffic signal and av driving behavior.  Frontiers in Robotics and AI 2019, 6, 117.
  * Garber (2014) Garber, M.  The Revolution Will Be Adorable: Why Google’s Cars Are So Cute. Available online: https://www.theatlantic.com/technology/archive/2014/05/the-revolution-will-be-adorable-why-googles-driverless-cars-are-so-cute/371699/ (accessed on 11th Oct 2021).  The Atlantic 2014.
  * D’Onfro (2014) D’Onfro, J.  Why Google Made Its Self-Driving Car Look So Cute. Available online: https://www.businessinsider.com.au/google-self-driving-car-why-its-so-cute-2014-12?r=US&IR=T (accessed on 11th Oct 2021).  Business Insider Australia 2014.
  * Sood (2021) Sood, G.  Honda 2040 NIKO Comes with A Tiny AI Assistant, Taking the Car from A Vehicle to Your Friend! https://www.yankodesign.com/2021/08/21/honda-2040-niko-comes-with-a-tiny-ai-assistant-taking-the-car-from-a-vehicle-to-your-friend/ (accessed on 11th Oct 2021).  Yanko Design 2021.
  * Hoffman et al. (2015) Hoffman, G.; Zuckerman, O.; Hirschberger, G.; Luria, M.; Shani Sherman, T.  Design and Evaluation of a Peripheral Robotic Conversation Companion.  Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction; Association for Computing Machinery: New York, NY, USA, 2015; HRI ’15, p. 3–10.  doi:black10.1145/2696454.2696495.
  * Braun et al. (2021) Braun, M.; Weber, F.; Alt, F.  Affective Automotive User Interfaces–Reviewing the State of Driver Affect Research and Emotion Regulation in the Car.  ACM Comput. Surv. 2021, 54.  doi:black10.1145/3460938.
  * Sadeghian et al. (2020) Sadeghian, S.; Hassenzahl, M.; Eckoldt, K.  An Exploration of Prosocial Aspects of Communication Cues between Automated Vehicles and Pedestrians.  12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2020; AutomotiveUI ’20, p. 205–211.  doi:black10.1145/3409120.3410657.
  * Lanzer et al. (2020) Lanzer, M.; Babel, F.; Yan, F.; Zhang, B.; You, F.; Wang, J.; Baumann, M.  Designing Communication Strategies of Autonomous Vehicles with Pedestrians: An Intercultural Study.  12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Association for Computing Machinery: New York, NY, USA, 2020; AutomotiveUI ’20, p. 122–131.  doi:black10.1145/3409120.3410653.
  * Tan et al. (2016) Tan, H.; Tiab, J.; Šabanović, S.; Hornbæk, K.  Happy Moves, Sad Grooves: Using Theories of Biological Motion and Affect to Design Shape-Changing Interfaces.  Proceedings of the 2016 ACM Conference on Designing Interactive Systems; Association for Computing Machinery: New York, NY, USA, 2016; DIS ’16, p. 1282–1293.  doi:black10.1145/2901790.2901845.
  * Hieida et al. (2016) Hieida, C.; Matsuda, H.; Kudoh, S.; Suehiro, T.  Action elements of emotional body expressions for flying robots.  2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2016, pp. 439–440.
  * Harzing (2007) Harzing, A.  Publish or Perish. Available from https://harzing.com/resources/publish-or-perish 2007.
  * Shi et al. (2018) Shi, Y.; Yan, X.; Ma, X.; Lou, Y.; Cao, N.  Designing Emotional Expressions of Conversational States for Voice Assistants: Modality and Engagement.  Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2018; CHI EA ’18, p. 1–6.  doi:black10.1145/3170427.3188560.
  * Frederiksen and Stoy (2020) Frederiksen, M.R.; Stoy, K.  On the causality between affective impact and coordinated human-robot reactions.  2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020, pp. 488–494.
  * Bucci et al. (2018) Bucci, P.; Zhang, L.; Cang, X.L.; MacLean, K.E., Is It Happy? Behavioural and Narrative Frame Complexity Impact Perceptions of a Simple Furry Robot’s Emotions.  In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2018; p. 1–11.
  * Chase and Follmer (2019) Chase, E.D.Z.; Follmer, S.  Differences in Haptic and Visual Perception of Expressive 1DoF Motion.  ACM Symposium on Applied Perception 2019; Association for Computing Machinery: New York, NY, USA, 2019; SAP ’19.  doi:black10.1145/3343036.3343136.
  * Frederiksen and Stoy (2020) Frederiksen, M.R.; Stoy, K.  Robots can defuse high-intensity conflict situations.  2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 11376–11382.
  * Kim and Follmer (2019) Kim, L.H.; Follmer, S., SwarmHaptics: Haptic Display with Swarm Robots.  In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2019; p. 1–13.
  * Sato et al. (2020) Sato, D.; Sasagawa, M.; Niijima, A.  Affective Touch Robots with Changing Textures and Movements.  2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020, pp. 1–6.
  * Ekman and Friesen (1971) Ekman, P.; Friesen, W.V.  Constants across cultures in the face and emotion.  Journal of personality and social psychology 1971, 17, 124.
  * Russell (1980) Russell, J.A.  A circumplex model of affect.  Journal of personality and social psychology 1980, 39, 1161.
  * Mehrabian (1996) Mehrabian, A.  Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament.  Current Psychology 1996, 14, 261–292.
  * Goldberg (1992) Goldberg, L.R.  The development of markers for the Big-Five factor structure.  Psychological assessment 1992, 4, 26.
  * Sharma et al. (2013) Sharma, M.; Hildebrandt, D.; Newman, G.; Young, J.E.; Eskicioglu, R.  Communicating affect via flight path exploring use of the laban effort system for designing affective locomotion paths.  2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2013, pp. 293–300.
  * Lenz et al. (2013) Lenz, E.; Diefenbach, S.; Hassenzahl, M.  Exploring Relationships between Interaction Attributes and Experience.  Proceedings of the 6th International Conference on Designing Pleasurable Products and Interfaces; Association for Computing Machinery: New York, NY, USA, 2013; DPPI ’13, p. 126–135.  doi:black10.1145/2513506.2513520.
  * Read and Belpaeme (2016) Read, R.; Belpaeme, T.  People interpret robotic non-linguistic utterances categorically.  International Journal of Social Robotics 2016, 8, 31–50.
  * Bradley and Lang (1994) Bradley, M.M.; Lang, P.J.  Measuring emotion: the self-assessment manikin and the semantic differential.  Journal of behavior therapy and experimental psychiatry 1994, 25, 49–59.
  * Tran et al. (2021) Tran, T.T.M.; Parker, C.; Tomitsch, M.  A Review of Virtual Reality Studies on Autonomous Vehicle–Pedestrian Interaction.  IEEE Transactions on Human-Machine Systems 2021.
  * Epke et al. (2021) Epke, M.R.; Kooijman, L.; De Winter, J.C.  I see your gesture: a vr-based study of bidirectional communication between pedestrians and automated vehicles.  Journal of advanced transportation 2021, 2021.
  * Lee et al. (2021) Lee, Y.M.; Madigan, R.; Giles, O.; Garach-Morcillo, L.; Markkula, G.; Fox, C.; Camara, F.; Rothmueller, M.; Vendelbo-Larsen, S.A.; Rasmussen, P.H.; others.  Road users rarely use explicit communication when interacting in today’s traffic: implications for automated vehicles.  Cognition, Technology & Work 2021, 23, 367–380.
  * Pillai (2017) Pillai, A.  Virtual reality based study to analyse pedestrian attitude towards autonomous vehicles.  Master’s thesis, KTH Roy. Inst. Technol., Stockholm, Sweden, 2017.
  * Fischer et al. (2019) Fischer, K.; Jung, M.; Jensen, L.C.; aus der Wieschen, M.V.  Emotion expression in HRI–when and why.  2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019, pp. 29–38.
  * Albastaki et al. (2020) Albastaki, A.; Hoggenmüller, M.; Robinson, F.A.; Hespanhol, L.  Augmenting Remote Interviews through Virtual Experience Prototypes; Association for Computing Machinery: New York, NY, USA, 2020; OzCHI ’20, p. 78–86.  doi:black10.1145/3441000.3441057.