Connected Vehicles Data Classification and the Influence of a Sustainable
Data Governance for Optimal Utilisation of In-Vehicle Data
Ali Karimi
1
, Asma Adnane
1
, Iain W. Phillips
1
and Elhadj Benkhelifa
2
1
Department of Computer Science, Loughborough University, Loughborough, U.K.
2
Staffordshire University, Stoke-on-Trent, U.K.
Keywords:
Data Governance, Connected Cars, CAV, Security, Privacy.
Abstract:
The growth of connected vehicles and their associated services has endowed them with the remarkable ability
to rapidly generate vast volumes of data. This proliferation has led to an increasing demand for effective data
governance solutions. This paper delves into the exploration of currently available in-vehicle data, meticu-
lously assessing the aspects of data velocity and heterogeneity. By scrutinising these factors, the paper aims to
pinpoint and address critical gaps in how to deal with in-vehicle data, ultimately striving to create a seamless
platform for managing and harnessing in-vehicle data. This project explores approaches for various connected
vehicle communications, including V2V, V2I, and V2X, to define data feeds in the connected vehicle data
landscape. The results of the study could influence the design of in-vehicle data governance by providing in-
formation on a stronger integrated framework, helping data owners and users make informed decisions about
managing their data assets.
1 INTRODUCTION
The Intelligent Transport System (ITS) is most effec-
tive when vehicles communicate with other vehicles
(V2V), infrastructure (V2I), pedestrians (V2P), and
other smart entities (V2X), known as connected ve-
hicles (Andra
ˇ
sko et al., 2021). Equipped with wire-
less communication, these vehicles can exchange data
with various networks. As connectivity increases, ve-
hicles will coordinate through smart infrastructure,
improving traffic management for safety and effi-
ciency. Essential in-vehicle technologies include sen-
sor technologies such as VLC, RADAR, LiDAR, in-
frared; vision technologies such as cameras, SVS, and
HD; and positioning technologies like radar cruise
control, RBOD, and GPS (Sadaf et al., 2023).
Connected vehicles use data from short-range
communication technologies in the 5.9 GHz band-
width (Tahir et al., 2022), and long-range technolo-
gies like 3G, 4G, or 5G networks. They receive
and share data with third parties. Entities like pub-
lic authorities, OEMs, insurers, and service providers
responsible for technology updates, safety, security,
road traffic, and infrastructure (Andra
ˇ
sko et al., 2021)
can access these data with restrictions. Data include
safety software updates (Dibaei et al., 2020), main-
tenance requests, personal data of vehicle owners or
drivers (Kerber, 2019), traffic rules, vehicle location,
speed limits, road conditions, and collision reports.
The EU Commission has implemented guidelines
on personal data protection and cybersecurity starting
in February 2019, followed by Standard Contractual
Clauses in 2021, to regulate event data recorders in
vehicles. These measures prioritise data protection,
improve the determination of liability in collisions,
and address technological advances.
The EU guidelines on cybersecurity call for Orig-
inal Equipment Manufacturers (OEMs) to design ve-
hicles using state-of-the-art technologies while en-
suring compliance with EU data protection regula-
tions. Vehicles must be equipped with robust protec-
tions against automated hacking, incorporating design
measures, risk assessments, and processes to mitigate,
prevent, and respond to cyber threats (EU, 2023/588).
Additionally, manufacturers are required to imple-
ment safety-critical updates, such as software patches
e.g. UNECE Cybersecurity (UN R155 / R156), to
maintain cybersecurity throughout the vehicle’s life-
time and ensure ongoing protection against emerging
threats.
Motivation: An effective data governance would
establish an efficient use of in-vehicle data in terms
of data ownership, access control, and business im-
Karimi, A., Adnane, A., Phillips, I. W. and Benkhelifa, E.
Connected Vehicles Data Classification and the Influence of a Sustainable Data Governance for Optimal Utilisation of In-Vehicle Data.
DOI: 10.5220/0013371100003899
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 11th International Conference on Information Systems Security and Privacy (ICISSP 2025) - Volume 2, pages 643-650
ISBN: 978-989-758-735-1; ISSN: 2184-4356
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
643

provement. Since a significant amount of data is ex-
changed, disseminated and processed by connected
vehicles. it is crucial to define the various types of
in-vehicle data. By categorisation of in-vehicle data,
the design of data governance for connected vehicles
would be more effective with the appropriate imple-
mentation of its main elements (such as data own-
ership, data use, data assets, security, privacy and
safety).
2 RELATED WORKS
Connected vehicles exchange real-time data on traf-
fic and road conditions with other vehicles and in-
frastructure. Traditionally, research in this field has
focused on communication and network layers, cru-
cial to ensuring secure and reliable data exchange and
enabling real-time coordination between vehicles and
their surroundings. However, recent advances em-
phasise the efficient use of data within the connected
vehicle ecosystem. The introduction of data ontolo-
gies, such as the Connected Traffic Data Ontology
(CTDO) and the Vehicle Signal and Attribute On-
tology (VSAO), provides structured frameworks for
managing and integrating data from connected vehi-
cles. These ontologies enhance interoperability and
data use by defining semantic models that establish
relationships between data objects. This focus on
the data layer is essential for the transition from a
communication-based utility to a more data-driven
transportation ecosystem.
CTDO, for example, provides a standardised
framework for traffic-related data, allowing vehicles
from different manufacturers to communicate seam-
lessly with one another and with traffic systems. Sim-
ilarly, VSAO standardises the data generated by ve-
hicle sensors and attributes, ensuring that data from
various sources can be efficiently integrated and used
in the vehicle ecosystem (Klotz et al., 2018). This
ontological approach not only improves the function-
ality of connected vehicles, but also contributes to the
development of broader smart city initiatives, where
data from connected vehicles can be combined with
data from other urban systems to improve traffic man-
agement, reduce congestion, and improve overall ur-
ban mobility.
Data ontology plays a crucial role in defining the
relationships between various data types within the
connected vehicle ecosystem, serving as a founda-
tional structure for better data utilisation.When com-
bined with a data governance framework, this struc-
ture ensures that data is managed securely and in com-
pliance with privacy regulations and standards, such
as GDPR. The framework not only enforces security
protocols but also guides how data is shared and ac-
cessed, ensuring that it aligns with legal requirements
and best practices.
2.1 Interoperability of Data in
Connected Vehicles
Due to the high mobility of connected vehicles, along
with significant security and privacy concerns, inter-
operability and system design have become major re-
search areas. It is often assumed that these chal-
lenges have been addressed, allowing data within the
connected vehicle paradigm to move freely and effi-
ciently across the vehicle infrastructure. Proprietary
software components often limit the interoperability
between products from different manufacturers, espe-
cially in connected vehicles(Lim et al., 2021). A uni-
fied data platform processing data from all ecosystem
sensors and applications would greatly benefit OEMs,
policymakers, and others by enhancing data use and
advancing connected vehicle technologies. Recently,
advances in cloud technology (Huang et al., 2023) in
protocol standards and service availability have im-
proved data interoperability.
2.2 In-Vehicle Data
In-vehicle data covers technical aspects like software
updates, maintenance alerts, and more, generated via
GPS, sensors, and vision tech. This data ensures ve-
hicle functionality, error correction, and optimisation.
Processed data aids road safety, management, and de-
velopment. Vehicles can receive and process external
data (e.g., from RSUs, driver activities) and exchange
them with others via V2V, V2I, V2X. Such data is
valuable to OEMs, service providers, and companies
for personalised services, product improvement, pre-
dictive maintenance, and marketing strategies.
Public authorities are also interested in the traffic
data captured by connected vehicles (Kerber, 2018)
(Sola-Morales et al., 2023). In fact, these data can
be used to improve traffic management systems, opti-
mise infrastructure planning, monitor environmental
impacts, and enhance public safety initiatives.
Car manufacturers and OEMs currently employ a
model in which the captured data is transmitted di-
rectly to proprietary servers controlled exclusively by
the OEMs (Liu et al., 2020). As a result, OEMs de-
fend the current proposal of extended vehicle concept,
where they remain the sole controllers of the data,
citing concerns over consumer privacy, security, and
safety (Kerber, 2019; Andra
ˇ
sko et al., 2021; Monday
et al., 2019).
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644

Once data ownership is clearly defined, decisions
regarding what data to share and how to share it be-
come considerably less challenging.
3 CLASSIFICATION OF DATA IN
CONNECTED VEHICLES
The classification of in-vehicle data is essential to
managing its collection, storage, sharing, and protec-
tion effectively. Data generated by connected vehi-
cles can be broadly categorised into several types, in-
cluding personal data (such as driver behavior and
preferences), technical data (such as engine perfor-
mance and diagnostics), environmental data (such
as road conditions and weather), and communica-
tion data (including vehicle-to-vehicle and vehicle-to-
infrastructure exchanges). Understanding these clas-
sifications is crucial for developing a robust data gov-
ernance framework that addresses key concerns such
as privacy, security, compliance with regulations, and
data ownership.
The first layer of classification in connected ve-
hicle data processing involves the identification of
data subjects. Data subjects are defined as individ-
uals or entities whose data is processed, as outlined
in GDPR regulations. Typical data subjects in the
context of connected vehicles include drivers, vehi-
cle owners, passengers, service users, and subscribers
to connected vehicle services.
Data originated from connected vehicles can be
identified in four main groups as follows:
C1- Technical Data. refers to information related
to the vehicle as a product, encompassing various
elements that ensure the vehicle’s operation, main-
tenance, and overall quality. This includes data on
specific vehicle components, such as software ver-
sions, hardware specifications, and serial numbers of
key parts such as engines, sensors, and control units.
It also covers diagnostic data, including error codes
generated by vehicle on-board systems, performance
metrics (e.g. fuel efficiency, battery status in electric
vehicles) and emissions data (Uhlemann, 2015).
C2- Infotainment Data. Refers to individuals
whose personal data is handled in the connected vehi-
cle ecosystem, including drivers, vehicle owners, pas-
sengers, or service users. This includes a broad spec-
trum of personal and behavioural data, such as driving
patterns (e.g., speed, braking, route choices) and data
provided by the individual through activities such as
entertainment preferences or mobile device connec-
tions to the vehicle infotainment system (Yu and Cai,
2022). It also covers financial and contractual details
such as warranties, service agreements, insurance, in-
voices, and payments.
C3- Environment Data. Consists of information
from the vehicle’s external environment, which in-
cludes metrics such as external temperature, humid-
ity levels, road surface conditions, weather patterns
(e.g., rain, snow, fog), and real-time traffic condi-
tions. It also includes data on the presence of ob-
stacles, nearby vehicles, and infrastructure-related de-
tails such as traffic signals, signage, and road layout.
This type of data is critical for improving situational
awareness and enabling advanced driver assistance
systems (ADAS) and autonomous driving technolo-
gies.
C4- Telematic Data. Telematic data refers to in-
formation related to vehicle performance, operational
conditions, and real-time status. This category in-
cludes data generated by various sensors and systems
that monitor vehicle activity and communicate with
external systems, often through satellite or cellular
networks. Telematic data can include critical perfor-
mance metrics such as fuel efficiency, vehicle speed,
and geographic location (GPS data) (Kumar et al.,
2023) (Sadaf et al., 2023). Telematic data play a criti-
cal role in the functionality, user experience, and data
governance of connected vehicles.
3.1 Illustration of In-Vehicle Data
The list below outlines the data classification frame-
work for connected vehicles by identifying data types
within each category. This approach clearly visualises
data classes, showing how data are generated, used,
and managed. Data categorisation supports a robust
governance model that defines data ownership, shar-
ing protocols, and usage. This structure clarifies roles
and permissions, improves safety, security, and effi-
ciency in connected vehicle systems.
A- Data Related to Connected Vehicles: These
types of data provide details on vehicle components,
data related to vehicle quality and maintenance, Vehi-
cle identification:
• Vehicle Identification Number (VIN), which con-
sists of: Registration number, connected vehicle
type, model year, component identification num-
ber (ID), technical specifications.
Connected Vehicles Data Classification and the Influence of a Sustainable Data Governance for Optimal Utilisation of In-Vehicle Data
645

• Vehicle Configuration including: type of engine,
type of gearbox, hardware and software versions,
Electronic Control Unit (ECU) parameters
• Network and Communication: Ip address, Media
Access Control (MAC) address of connected ve-
hicle, WiFi password, Bluetooth name of vehicle
B- Data Related to the Data Subjects: such as
owner, driver, passenger, subscriber, service user, and
data directly provided by the data subject (such as mo-
bile phone, music, etc.).
• Navigation and information: Phone address book
and call history, navigation destination, radio
preferences, music, movies, pictures, etc.
• Personal attributes: Biometry data captured inside
the vehicle
• Dynamic data include: Gear, engine Revolutions
Per Minutes (RPM), average fuel consumption,
automatic braking, lane departure warning. accel-
eration, speed, mileage, AdBlue level
• Data from vehicle setting and control: Privacy set-
tings, status of windows and doors, lights on/off,
air condition,
• Accounting, warranty and Service subscription:
Purchase or leasing invoices, after sale and ser-
vice invoices.
C- Data Related to Other Subjects Outside of
the Connected Vehicles: Data regarding external
environment of the vehicle and data from other data
subjects: External temperature, video and images
captured from outside environment.
In summary, all the above data types represent five
distinct yet complementary categories within the con-
nected vehicle ecosystem. Each of these types of data
plays a crucial role in advancing the functionality of
connected vehicles, but all raise important considera-
tions regarding data governance, security, and privacy.
4 IN-VEHICLE DATA ACCESS
An important part of the current controversial policy
discussion is about the control and access of data cap-
tured by connected vehicles or data in vehicles (Ker-
ber, 2019). Car manufacturers or Original Equipment
Manufacturers (OEMs) use a concept to transmit cap-
tured data directly to a server (Liu et al., 2020) (Song
et al., 2021), which is proprietary to the OEMs (ex-
tended vehicle concept). The aim of introducing this
concept by OEMs is to have exclusive control over in-
vehicle data with the argument of preserving data se-
curity and privacy. However, stakeholders and many
independent service providers also demand access to
in-vehicle data with the argument of ensuring fair
and undistorted competition to provide maintenance
and after-sales services to connected vehicles (Kerber,
2019) (Tahir et al., 2022).
In a short-term solution, a shared platform might
be a solution to allow all parties to access in-vehicle
data on demand. In the long term, the development of
a technological solution such as an on-board applica-
tion platform allows the vehicle owner to control ac-
cess to the vehicle or to the in-vehicle data. Some key
considerations regarding in-vehicle data access and
sharing within the context of connected vehicles are
as follows:
4.1 Privacy and Security
In-vehicle data frequently contains sensitive and per-
sonally identifiable information (PII) related to the
driver, passengers, vehicle performance, and its sur-
rounding environment (Majid, 2023). These data in-
clude, but are not limited to, driving patterns, location
history, biometric data, and real-time telemetry, all of
which can be vulnerable to security breaches or unau-
thorised access if not properly protected.(Figure 1).
Figure 1: Requirements for CVs data privacy and security.
To safeguard these sensitive data, a multilayered
approach to security must be adopted, incorporat-
ing measures such as end-to-end encryption, Access
controls, anonymisation and pseudonymisation tech-
niques are essential to protects data in transit and to
protect the privacy of individuals by ensuring that per-
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646

sonal data cannot be directly linked to a specific per-
son particularly when shared with third-parties.
4.2 Data Ownership
The question of who should control or have access
to in-vehicle data remains a contentious issue among
vehicle manufacturers, OEMs, leasing companies, in-
surers, and service and maintenance providers (Ker-
ber, 2018). Although the European Union (EU) is ac-
tively working to regulate in-vehicle data ownership,
the process faces significant challenges. OEMs and
connected vehicle manufacturers are resisting spe-
cific regulations, citing concerns related to consumer
safety, data privacy, and security (Andra
ˇ
sko et al.,
2021). The European Automobile Manufacturers As-
sociation (ACEA) has affirmed that the EU automo-
tive industry is committed to providing access to in-
vehicle data, but emphasises that uncontrolled access
to such data could pose major risks in terms of se-
curity, data protection, safety and privacy (Monday
et al., 2019).
Data insights can optimise predictive maintenance
and usage-based insurance pricing, cutting costs, and
enhancing service quality. However, ensuring access
requires strict security, privacy and data governance to
prevent data misuse, balancing accessibility and pro-
tection (Schellekens, 2022) (Khan et al., 2023). Clear
data ownership is crucial for governance, simplifying
decisions on data sharing. The next section outlines
key principles for sharing in-vehicle data.
4.3 Data Sharing
Connected vehicle manufacturers may already be
heading down a familiar path of controlling data (Xu
and Guo, 2022). Such control over in-vehicle data
could lead to an anticompetitive market and a con-
centration of power, resulting in monopolistic prac-
tices. The European Commission is currently in the
process of defining the scope of its Data Act to ensure
fairness in digital environments, promote opportuni-
ties for data-driven innovation , stimulate a competi-
tive data market and make data more accessible to all
(Sola-Morales et al., 2023).
If OEMs allowed third-party access to in-vehicle
data, several fundamental principles must be consid-
ered (Figure 2.
The following lists explain the importance of data
sharing principles in connected vehicles and why
these elements should be considered for sharing in-
vehicle data.
• In-vehicle data: Before introducing regulations on
data sharing and making in-vehicle data available
Data Sharing
Principles
Privacy &
Data
Protection
Services &
Fair
Competition
Interoperability
Fair
Competition
Data
Utilisation
Security &
Safety
Business
Improvement
In-vehicle
Data & Third
Party
Figure 2: Principles of Sharing In-vehicle Data.
for third-party services, it is essential to ensure
the protection of driver personal data, ensuring
the secure and safe operation of the vehicle, not
undermining the liability of the vehicle manufac-
turer, and avoiding damage to intellectual prop-
erty rights of the vehicle.
• Maintenance option and fair competition: Drivers
and vehicle users can obtain services from dif-
ferent garages that have concluded an agreement
with OEMs, the vehicle manufacturer, or its net-
work of authorised maintenance providers and in-
dependent aftermarket organisations.
• Data protection and privacy: In accordance with
EU data protection and privacy law and GDPR
personal data must be protected and not to be
shared without the consent of the owners.
• Security and safety: Third-party access to vehic-
ular electronic devices may jeopardise safety, in-
tegrity, and security of the vehicle. Thus, any ac-
cess outside of the regulated access to in-vehicle
data such as repair, maintenance, emissions con-
trol, and diagnosis data access must occur via an
off-board process.
• Interoperability: For third-party and independent
service providers to access in-vehicle data, mean
of access and the interfaces must be standard-
ised to ensure interoperability. The International
Organisation for Standardisation (ISO) developed
for this purpose to provide web access to the Ex-
tended Vehicle (ExVe) as defined in ISO 20077-
1 (ExVe consists of a vehicle with external hard-
ware and software extensions that are developed,
implemented and managed by the ExVe manufac-
turer).
• Business improvement: The vehicle manufacturer
Connected Vehicles Data Classification and the Influence of a Sustainable Data Governance for Optimal Utilisation of In-Vehicle Data
647

and the OEM invest significant amounts of funds
and resources to develop and introduce the final
product to the intended market. In addition, main-
taining, managing, and keeping the available data
is also a costly process, which could add to the ini-
tial production costs. It is a clear mission for every
business to expect Return on Investment (RoI).
Connected vehicles produce extensive data in var-
ious categories. However, not all of this data is shared
with third parties or service providers. Specifically,
shared data excludes personally identifiable informa-
tion (PII) related to the vehicle driver, such as con-
tact lists from mobile devices, destination and trip
histories, and other sensitive personal data. Further-
more, data received from RSUs, infrastructure, V2V
communications, or other road users may also be re-
stricted from being shared with external parties.
4.4 Data Access Control
OEMs and third-party service providers may require
access to in-vehicle data for various purposes, such
as vehicle diagnostics, maintenance, and personalised
services. Clear agreements and guidelines should
be established to govern data access and specify the
scope and purpose for which the data will be used.
This helps ensure that data sharing is conducted in a
fair and transparent manner.
Even if ownership is attributed to a specific entity,
the discussions revolve around who has access and
control over the data. Data governance frameworks
should address access rights, data sharing agreements,
and mechanisms to obtain consent from drivers for
specific uses of their data.
4.5 Legal and Regulatory
Considerations
Data access and sharing in connected vehicles are
governed by various legal and regulatory frameworks,
which differ between regions and encompass data
protection laws, privacy regulations, and industry-
specific guidelines. For example, in the Euro-
pean Union, the General Data Protection Regulation
(GDPR) imposes strict requirements on how personal
data, including in-vehicle data, are processed, stored,
and shared. GDPR mandates clear consent from
data subjects and the application of privacy-by-design
principles to ensure that data handling practices pri-
oritise security and privacy. Similarly, the California
Consumer Privacy Act (CCPA) in the United States
provides a regulatory framework for data privacy, giv-
ing consumers greater control over their personal in-
formation and requiring businesses to disclose how
they collect and share data (Shatz and Lysobey, 2022).
In addition, cross-border data sharing in the con-
text of connected vehicles also introduces complexi-
ties, as differing regulatory requirements between re-
gions can create challenges (Miller, 2022). For exam-
ple, the EU-U.S. Privacy Shield, although invalidated
in 2020, highlighted the need for robust data transfer
mechanisms between regions with different privacy
standards. Current frameworks like Standard Con-
tractual Clauses (SCCs) and Binding Corporate Rules
(BCRs) are now employed to facilitate cross-border
data flows while ensuring compliance with stringent
data protection standards. Given the global nature
of the automotive industry, manufacturers and stake-
holders must navigate a landscape of evolving regula-
tions.
5 THE IMPORTANCE OF DATA
CLASSIFICATION
Figure 3 illustrates the data interconnection within the
connected vehicle ecosystem. All these processes, in-
cluding data generation, processing, and sharing, oc-
cur within the vehicle’s network, managed by the cen-
tral processing unit (CPU) and data controller unit, as
represented by the circle surrounding data classifica-
tion and data types. This signifies that manufacturers
or data controllers have direct access to all categories
of in-vehicle data, such as technical, telematics, info-
tainment, and environmental data. Given this compre-
hensive access, it is imperative to implement a robust
data governance scheme specifically designed for in-
vehicle data.
This approach is critical in mitigating the risks of
data security breaches, safeguarding data privacy, and
preventing cyberattacks.
6 DISCUSSION AND
CONCLUSION
The discussion of in-vehicle data in connected ve-
hicles highlights the critical role of data ontology in
defining the relationships between various data types
and their interactions within the vehicle ecosystem.
Connected vehicle data classification systems cat-
egorise data into groups such as telematics, informa-
tion technology, technical, and environmental. This
organisation clarifies data handling, sharing, and pro-
tection, primarily addressing data governance, own-
ership, privacy, and security. It helps in creating data
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648

-
IN-VEHICLE DATA
TECHNICAL DATA
FUEL & POLUTION
ALERT
MAINTENANCE
ALERT
SOFTWARE UPDATE
ENVIRONMENT
DATA
STATE OF ROADS
ROAD TRAFFICS
COLLISION
INFOTAINMENT
DATA
PERSONALISED
EXPERIENCES
GAMES & MUSIC
WEBSITES VISITED
TELEMATIC DATA
GPS
SENSORS & VISION
TECHNOLOGIES
DRIVER STATUS
Financial Contract
V2I Communications
Weather Patterns
Control Unit
Hardware
Specifications
SENSORS & VISION
TECHNOLOGIES
SENSORS & VISION
TECHNOLOGIES
Figure 3: Connection of data classification in connected ve-
hicles.
sharing methods that balance privacy, innovation, and
safety.
Moreover, connected vehicles have revolutionised
mobility and data-driven transportation. Their suc-
cess depends on effective data governance. This in-
volves not only data quality and security, but also
compliance with regulations and responsible data
handling to build trust. The diverse types of data
require a structured governance approach to gain in-
sight, improve safety, and improve efficiency. Fur-
thermore, the monetisation of data presents a signif-
icant opportunity, and data governance is key to bal-
ancing data utilisation for economic purposes with the
preservation of individual privacy.
7 FUTURE WORK AND
RECOMMENDATION
Future work focuses on the development of a com-
prehensive data governance framework that enables
relevant organisations to effectively utilise in-vehicle
data while addressing critical elements of cyberse-
curity and privacy. Additionally, it is recommended
to introduce a model designed to assess an organi-
sation’s current data governance capabilities, accom-
panied by a structured approach for identifying areas
of improvement and implementing targeted enhance-
ments.
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