in-Car Entertainment via Group-wise Temporary Mobile Social
Networking
Mario G. C. A. Cimino
1a
, Antonio Di Tecco
1,2 b
, Pierfrancesco Foglia
1c
, Raffaele Giannessi
1
,
Jacopo Malvatani
1
, Cosimo A. Prete
1d
and Giulio Rossolini
1
1
Department of Information Engineering, University of Pisa, Pisa, Italy
2
University of Florence, Florence, Italy
{g.rossolini, r.giannessi4, j.malvatani}@studenti.unipi.it
Keywords: Autonomous Car, in-Car Entertainment, Temporary Mobile Social Networking, Music Streaming Service.
Abstract: Next generation cars will increase the passengers’ time for fun and relax, as well as the number of unknown
passengers traveling together. A key functionality to improve the users experience is that of Temporary
Mobile Social Networking (TMSN): where passengers form, for a limited-time, a mobile social group with
common interests and activities, using their already available social network accounts. The goal of TMSN is
to automatically redesign the users’ profiles and interfaces into a group-wise passengers’ profile and a
common interface, by reducing isolation and enabling socialization. In this paper, a TMSN-inspired music
selection is proposed and developed via the Spotify music streaming service. Early results are promising and
encourage further developments towards the concept of in-car entertainment.
1 INTRODUCTION
Nowadays mobile technology guarantees seamless
network connectivity and application functionality to
travelers. As a consequence, car passengers can easily
enrich their traveling experience via mobile social
networks, providing various entertainment such as
music and video streaming, feed, stories, and so on
(Coppola, 2016; Bilius, 2020).
In the current social network market, some major
products of interest for in-car entertainment are
represented in Figure 1. Facebook is characterized by
focus on user’s feed and status; it allows to share
thoughts and emotional feedback. YouTube is the
most popular social video platform, on which users
can find and share video about their interests. Spotify
is leading the market of social music, where favorite
artists, genres, and playlists are managed. Finally,
Instagram is the repository of stories, with
photographs and short videos of friends and
influencers.
a
https://orcid.org/0000-0002-1031-1959
b
https://orcid.org/0000-0003-0126-8079
c
https://orcid.org/0000-0001-6432-4504
d
https://orcid.org/0000-0002-8467-8198
Given the role that Mobile Social Networking
(MSN) can play in the smart city paradigm and in the
next generation cars, a great development of MSN
industry is expected towards in-car infotainment,
enabled by the ongoing, high usable car’s interior
redesign (Coppola, 2016; Foglia, 2014) and the
increasing AI-based interaction (vocal interaction,
emotion recognition, and so on) (Rong, 2021).
Indeed, MSNs have already been introduced in cars
via Android Auto or Apple CarPlay, the major
platforms for smart phone interoperability with car's
dashboard information and entertainment unit.
Specifically, Figure 2 illustrates the autonomous
driving levels classified by the Society of Automotive
Engineers (SAE) (Coppola, 2016):
level 0 – manual driving, i.e., a completely manual
vehicle, without electronic stability program, paring
assistance, or any kind of assistance system;
level 1 – driver assisted but steering the vehicle
independently, i.e., cruise control, lane departure
warning, emergency brake assistance;
432
Cimino, M., Di Tecco, A., Foglia, P., Giannessi, R., Malvatani, J., Prete, C. and Rossolini, G.
in-Car Entertainment via Group-wise Temporary Mobile Social Networking.
DOI: 10.5220/0011096000003191
In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2022), pages 432-437
ISBN: 978-989-758-573-9; ISSN: 2184-495X
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
level 2 – partial automation, i.e., the vehicle can
independently perform individual driving, such as
parking, navigating stop-and-go traffic, lane
departure warning, distance warning;
level 3 – conditional automation, i.e., autonomous
driving under certain conditions;
level 4 – high automation, i.e., vehicle controls
complete journeys on the highway, or in city traffic
predominantly independently;
level 5 – full automation, i.e., neither driving ability
nor a driving license are required to use the vehicle.
Figure 1: Some major social network platforms of interest
for in-car entertainment.
Figure 2: Autonomous driving levels and MSN access.
At level 3, in which the car controls a significant
number of driving operations, the number of MSN
products can sensibly increase. In this trend, car
sharing is also expected to become popular. As a
consequence, next generation cars will increase the
passengers’ time for fun and relax, as well as the
number of unknown passengers traveling together.
A deeply discussed issues of MSN is the negative
effect of the prolonged use of mobile apps: many
studies show the orthopedic injuries and the
psychological diseases that can affect a social user,
resulting in isolation, anxiety, depression, and so on.
In this regard, ambient intelligence and next
generation car (Bilius, 2020) are expected to solve the
inherent limits of smart phones and of current MSN.
To further enrich the interaction of an MSN user
with the physical world, by reducing his isolation, an
important paradigm made possible by next generation
car is the group-to-many interaction, illustrated in
Figure 3. In the traditional one-to-many interaction,
the user is alone in his physical world and is
connected to the others via MSN. With the group-to-
many interaction, a group of users is temporarily
living in a common physical space, interact in person
and share their experience with the MSN as a whole
group.
(a)
(b)
Figure 3: One-to-many interaction paradigm vs. group-to-
many in the context of in-car MSN.
In the literature, the concept of Temporary MSN
(TMSN) has attracted attention as a conceptual
framework to be used at hotels, concerts, theme parks,
sports arenas, where people form a mobile social
group for a limited time, by having a common
physical interaction (Yin, 2019). People confined in a
specific place are allowed to join the TMSN using
their personal account, and acting via a group-wise
interaction with the others, improving the mobile
users’ experiences with such temporal friends. A
surrogate of this concept is Spotify Group Session, a
beta testing feature that allows up to six people to
share control over the music playing in the
background of a physical or virtual get-together
(Spotify GS, 2022). There is an expansion of Spotify
in-Car Entertainment via Group-wise Temporary Mobile Social Networking
433
Group Sessions available in Android Automotive OS,
an Android-based infotainment system that is built
into vehicles, such as the Polestar 2 (2022).
In the context of hotels, LobbyFriend was the first
TMSN, enabling hotels to stay connected to guests
throughout their stay, within the one hotel or across
multiple hotels within the vicinity. It was inspired by
loneliness while traveling often for business. When a
guest checks out of the hotel, all interactions in the
TMSN are erased (Yin, 2019).
TMSN is beyond sharing a common space and a
collaborative playlist (Spotify CL, 2022; Spotify FM,
2022), it concerns algorithms for the exploitation of
the user’s profile and the current environmental
context, enabling an augmented interaction via
proactive services such as recommendation (Cimino,
2011). Social network platforms are today strongly
managed by powerful analytics and algorithms (Lu,
2015; Zhang, 2019), also based on key techniques of
modern sociology. As a such, in this study TMSN is
an additional intelligent layer on an ecosystem of
services available on next generation cars.
In this paper a group-wise TMSN is proposed as a
design paradigm for in-car entertainment.
Specifically, in the context of social music, a
functional design is illustrated. A prototype has been
implemented, based on Spotify API (Spotify API,
2022), and experimented.
The paper is structured as follows. Section 2
illustrates the core concepts and functional design of
the proposed approach. Experimental studies are
covered by Section 3. Conclusions are drawn in
Section 4.
2 CORE CONCEPTS AND
FUNCTIONAL DESIGN
In the context of audio streaming, a TMSN based
playlist recommender exploits both ambient (car) and
MSN data. For interoperability reasons, the design
should be based on a standard music ontology (MO,
2022), which provides a vocabulary for managing
music-related data across multiple applications
(Ciaramella, 2010).
Figure 4 illustrates the fundamental concepts and
static relationships in the context of audio streaming
recommendation, as an ontology diagram. In figure,
each concept is enclosed in a rectangular shape.
Concepts are connected by relationships, represented
with labelled directed edges. Some concepts are also
characterized by properties, listed in lowercase
letters. The fundamental outcome of this ontology is
a recommendation of a track, based on the social
profiles of the passengers (containing listened tracks,
artists, and genres). Specifically, from the top-middle,
a Passenger is in a Car, is in a Mobile Social Net,
listens a Track. A Track is made by an Artist, and
belongs to a Genre. A Car plays a Track, and
manages a Temporary Mobile Social Net, which
generates a Passengers Profile. On the other side, a
Mobile Social Net builds a Social Profile, which is
made by listened tracks, artists and genres. A Social
Profile is merged in a Passengers Profile. Finally, the
Passenger Profile recommends a Track.
Figure 5 shows a protocol of an audio streaming
recommender based on TMSN and Spotify analytics,
in a standard graphical representation called Business
Process Model and Notation (BPMN). The protocol
is built on the ontology in Figure 4 and covers only
the essential aspects of the proposed approach, for the
sake of readability.
The BPMN is based on a solid mathematical
foundation, to enable the execution, simulation, and
automation of consistency checking (Cimino, 2017).
It is also suitable to standardize and facilitate
communication between all stakeholders. In BPMN,
a rectangular area represents a participant who takes
part in a protocol, via message exchange. In each
rectangular area, the protocol is managed via events,
activities, and decision/merge nodes, represented by
circles, rounded boxes, and diamonds, respectively.
Sequence flows and data flows are represented by
solid and dotted arrows, respectively. Finally, data
storages are represented by cylindrical shapes.
Figure 4: TMSN Ontology in the context of audio streaming
recommendation.
The protocol starts on the top left (white envelope
in a thin circle), when new passengers are detected by
the TMSN, and ends when a playlist is determined
(one of the black envelopes in thick circles). As a first
task, the TMSN asks the social profiles of all
VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems
434
Figure 5: BPMN protocol of an audio streaming recommender based on TMSN and Spotify analytics.
passengers to Spotify Analytics. The gear icon for the
task means that is a service task, i.e., supported by
web services offered by Spotify. As defined by the
ontology, the collected social profiles contain listened
tracks, artists and genres of each passenger. Social
profiles are archived for the next steps of the protocol.
Then, a script task (i.e., a task internally developed,
characterized by a sheet icon) identifies the set of
common tracks between all passengers. If some
tracks are found, then the recommended play list is
generated. Otherwise, a script task identifies the
common artists. If some common artists are found, a
service task asks the top tracks for them to Spotify
Analytics and, if some are found, the recommended
play list is generated. Otherwise, a service task asks
to Spotify Analytics the recommended tracks for
common artists and, if some are found, the
recommended play list is generated. Otherwise, a
script task identifies the common genres in social
profiles and, if some are found, a service task asks the
recommended tracks for common genres to Spotify
Analytics, to generate the recommended playlist.
Finally, if the recommended tasks are not found, a
service task asks the recommended tracks for top
artist to generate the playlist.
3 IMPLEMENTATION AND
EXPERIMENTAL STUDIES
The protocol is purposely designed to heavily exploit
Spotify Analytics services. Indeed, it has been
implemented and experimented on both a desktop
computer and a Raspberry PI3b+, a small Chip
Multiprocessor (CMP) (Foglia, 2014) single-board
computer, equipped with WIFI for interfacing with
smartphone, and 4G capabilities for interfacing with
Spotify web services. The web application controller
has been developed on Apache2 web server: it
manages the authentication required by Spotify API,
via Javascript. The host access point daemon has been
implemented on Hostapd. The protocol logic has
been developed on Python3, using Spotipy, a third-
party library to access the Spotify APIs, and
DBConnection to manage Social Profiles on a local
MariaDB data base management system. The client-
server communication is managed via the OAuth2, an
industry-standard protocol for providing token-based
authorization flows for web applications, mobile
phones, and living room devices. A keep-alive
mechanism has been also implemented to detect
dynamic join/disjoin of passengers. Finally, a
persistent buffering of social profiles and playlists has
been implemented to speed up the recommendation
requests time.
Six people have been involved to carry out the
following sessions: 8 sessions made by 2 passengers,
5 sessions made by 3 passengers, and 5 sessions made
by 4 passengers.
At the end of a recommended track, each
passenger has provided a binary like/dislike rating. At
the end of the recommended play list, for each
passenger, the approval rate has been calculated as
follows:
𝑎𝑝𝑝𝑟𝑜𝑣𝑎𝑙 𝑟𝑎𝑡𝑒 =
| |
| |
× 100 (1)
As a result, Figure 6, Figure 7 and Figure 8 show
the approval rate histograms for sessions of 2, 3 and
4 passengers, respectively. It is clear that, for
increasing number of passengers, the approval rate
decreases. This can be ascribed to the smaller
common tracks, artists and genres between
passengers. Figure 9 clearly shows this trend, via the
mean approval rate against passengers’ number.
Finally, Figure 10 shows the generalization
ability of the recommender, i.e., to go beyond the
in-Car Entertainment via Group-wise Temporary Mobile Social Networking
435
passenger’s playlist (known tracks). It is calculated as
follows:
𝑔𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 =
| |
| |
× 100 (2)
Assuming that the tracks in its own playlist are all
liked, the maximum value is 1 (i.e., no additional
liked tracks). For increasing unknown tracks that are
liked, the value decreases (i.e., better generalization).
Figure 6: Approval rate histogram for 2 passengers.
Figure 7: Approval rate histogram for 3 passengers.
Figure 8: Approval rate histograms for 4 passengers.
Figure 9: Mean approval rate against passengers’ number.
Figure 10: Mean generalization rate against passengers’
number.
Overall, for increasing passengers the
generalization ability increases, for the higher variety
of tracks, authors and genres available.
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436
4 CONCLUSIONS
In this paper, group-wise Temporary Mobile Social
Networking recommender is proposed as a design
paradigm for in-car entertainment. Specifically, in the
context of social music, a functional design is
illustrated. A prototype has been implemented, based
on Spotify Analytics and Raspberry PI3, and
experimented involving six people on various
sessions.
The achieved approval and generalization rates
show that, for increasing number of passengers, the
approval rate decreases, for the smaller common
tracks, artists and genres between passengers.
However, for increasing number of passengers, the
generalization ability increases, providing liked
tracks that are not already known.
Although a more in-depth exploration of the
techniques is needed, together with an enrichment of
the experiments, the early results are promising, and
show the potential of the proposed approach.
An extensive study in this direction can be a future
work to bring a stronger contribution in the field.
ACKNOWLEDGEMENTS
Work partially supported by the Italian Ministry of
Education and Research (MIUR) in the framework of
the CrossLab project (Departments of Excellence,
Lab Cloud Computing, Big Data & Cybersecurity,
Lab Augmented Reality, Lab Advanced
Manufacturing).
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