Smart Card-Based Vehicle Ignition Systems: Security, Regulatory Compliance, Drug and Impairment Detection, Through Advanced Materials and Authentication Technologies

Abstract

This study investigates the integration of smart card readers into vehicle ignition systems as a multifaceted solution to enhance security, regulatory compliance, and road safety. By implementing real-time driver verification, encryption protocols (AES-256, RSA), and multifactor authentication, the system significantly reduces unauthorized vehicle use and improves accident prevention. A critical advancement of this research is the incorporation of automated drug and impairment detection to prevent driving under the influence of substances, including illicit drugs and prescription medications. Risk models estimate that drug-related accidents could be reduced by 7.65% through the integration of these technologies into vehicle ignition systems, assuming high compliance rates. The study evaluates drug applications leveraging the same sensor-based monitoring technologies as used for impairment detection. These systems can facilitate the real-time tracking of medication intake and physiological responses, offering new possibilities for safety applications in medical transportation and assisted driving technologies. High-performance polymers such as polyetheretherketone (PEEK) enhance the durability and thermal stability of smart card readers, while blockchain-based verification strengthens data security and regulatory compliance. Despite challenges related to cost (USD 100–300 per unit) and adherence to ISO standards, these innovations position smart card-based ignition systems as a comprehensive, technology-driven approach to vehicle security, impairment prevention, and medical monitoring.

Full text

Academic Editor: Fang-Chung Chen
Received: 1 March 2025
Revised: 14 March 2025
Accepted: 18 March 2025
Published: 19 March 2025
Citation: Vitiello, V.; Benazzi, A.;
Trucillo,P. Smart Card-Based Vehicle
Ignition Systems: Security, Regulatory
Compliance, Drug and Impairment
Detection, Through Advanced
Materials and Authentication
Technologies. Processes 2025,13, 911.
https://doi.org/10.3390/
pr13030911
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Review
Smart Card-Based Vehicle Ignition Systems: Security, Regulatory
Compliance, Drug and Impairment Detection, Through
Advanced Materials and Authentication Technologies
Vincenzo Vitiello 1, Alessandro Benazzi 2and Paolo Trucillo 3,*
1Inventori Cavensi, Via XXV Luglio 87, 84013 Cava De’ Tirreni, Italy; enzo.vitiello15@gmail.com
2Slim!Architetti, Via Savio 1087, 47522 Cesena, Italy; alessandro@slimarchitetti.it
3Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, University of Naples
Federico II, P.le V. Tecchio 80, 80125 Napoli, Italy
*Correspondence: paolo.trucillo@unina.it; Tel.: +39-329-65-66-043
Abstract: This study investigates the integration of smart card readers into vehicle ignition
systems as a multifaceted solution to enhance security, regulatory compliance, and road
safety. By implementing real-time driver verification, encryption protocols (AES-256, RSA),
and multifactor authentication, the system significantly reduces unauthorized vehicle use
and improves accident prevention. A critical advancement of this research is the incorpora-
tion of automated drug and impairment detection to prevent driving under the influence
of substances, including illicit drugs and prescription medications. Risk models estimate
that drug-related accidents could be reduced by 7.65% through the integration of these
technologies into vehicle ignition systems, assuming high compliance rates. The study
evaluates drug applications leveraging the same sensor-based monitoring technologies
as used for impairment detection. These systems can facilitate the real-time tracking of
medication intake and physiological responses, offering new possibilities for safety appli-
cations in medical transportation and assisted driving technologies. High-performance
polymers such as polyetheretherketone (PEEK) enhance the durability and thermal stability
of smart card readers, while blockchain-based verification strengthens data security and
regulatory compliance. Despite challenges related to cost (USD 100–300 per unit) and ad-
herence to ISO standards, these innovations position smart card-based ignition systems as
a comprehensive, technology-driven approach to vehicle security, impairment prevention,
and medical monitoring.
Keywords: smart card readers; vehicle ignition systems; automotive security; multifactor
authentication; encryption protocols; blockchain technology; CAN bus compatibility; driver
credential verification; automotive cybersecurity frameworks
1. Introduction
Ensuring regulatory compliance and enhancing vehicle security are critical challenges
in modern transportation [
1
,
2
]. Unauthorized vehicle access, expired licenses, and non-
compliance with safety regulations pose significant risks to road safety. Traditional enforce-
ment methods rely on manual inspections and random checks, which are often inefficient
and fail to provide real-time enforcement mechanisms.
These challenges include widespread non-compliance with mandatory vehicle inspec-
tions, delayed or avoided payment of vehicle ownership taxes, and expired or invalid
driver’s licenses. Additionally, there is a critical issue of license–class mismatches, where
Processes 2025,13, 911 https://doi.org/10.3390/pr13030911

Processes 2025,13, 911 2 of 23
drivers operate vehicles beyond their authorized classification. For example, a driver
holding only a standard passenger vehicle license may illegally operate a heavy-duty truck,
creating severe regulatory violations and increasing road safety risks. Addressing these
concerns requires an integrated and automated system capable of real-time verification and
enforcement, which is the focus of this study [3–5].
Understanding the magnitude of non-compliance is essential to contextualize the
urgency of this issue. Table 1provides statistical insights into non-compliance rates across
different regions, illustrating the prevalence of expired licenses, missing vehicle inspections,
and unpaid ownership taxes. These figures highlight the limitations of current enforcement
mechanisms and demonstrate the need for automated solutions that can systematically
detect and address these violations. By presenting these data, the study establishes a
quantitative foundation for evaluating the effectiveness of smart card-based authentication
in improving compliance and road safety.
Table 1. Estimates of non-compliance in vehicle regulations.
Region
Non-
Compliance
with Property
Tax (%)
Non-
Compliance
with Insurance
(%)
Non-
Compliance
with Inspection
(%)
Expired Driver’s
License (%)
Age-Related
Ineligibility (%)
Italy 15% 10% 20% 8% 5%
Europe 12% 8% 18% 7% 4%
Worldwide 20% 15% 25% 10% 6%
An important aspect of ensuring regulatory compliance and vehicle security is the
ability to adapt enforcement mechanisms to emerging transportation trends. The rise of
autonomous vehicles, shared mobility services, and digitally integrated transportation
systems necessitates the implementation of authentication solutions that can dynamically
verify driver and vehicle credentials. Traditional enforcement methods often fail to account
for these evolving mobility models, leaving gaps in compliance monitoring. By integrating
smart card-based authentication with digital infrastructure, the real-time validation of
driver eligibility, tax and insurance status, and vehicle roadworthiness can be achieved.
This shift towards intelligent compliance frameworks not only enhances security, but
also streamlines administrative processes, reducing the burden on law enforcement and
regulatory bodies. Moreover, leveraging technologies such as blockchain and AI-driven
risk assessment models can improve fraud detection and predictive compliance monitoring,
paving the way for a more adaptive and responsive automotive regulatory environment.
Currently, the verification of these aspects relies on random checks conducted by law
enforcement. This approach allows many violations not to be detected; in some cases,
this may result in dangerous consequences for drivers, passengers, and pedestrians [
6
].
Uninspected vehicles may have mechanical failures, expired licenses reflect the inadequate
assessment of a driver’s competence, and mismatched licenses increase the risk of accidents
due to the improper handling of vehicles [
7
]. Addressing these issues requires more robust
and systematic control mechanisms to ensure compliance and enhance overall road safety,
and of course, this cannot be guaranteed by human control alone [
8
–
10
]. At present,
the estimates of vehicles that are non-compliant to Italian, European and worldwide
regulations [11–18] are indicated in Table 1.
To mitigate these issues, implementing a more centralized and automated monitoring
system could significantly improve compliance. For instance, integrating databases for vehi-
cle registration, inspection status, tax payments, and driver licensing into a unified platform
would allow for real-time checks and automated alerts for overdue requirements [
19
–
22
].

Processes 2025,13, 911 3 of 23
Beyond the technical integration of vehicle data, the effectiveness of smart card readers in
vehicle systems is contingent upon the infrastructure and digital preparedness of municipal
facilities. The capacity of municipalities to implement real-time data-sharing networks,
ensure up-to-date vehicle records, and provide responsive law enforcement mechanisms
significantly influences the efficacy of such systems. In jurisdictions where municipal
infrastructure is advanced, smart card-based verification can seamlessly interact with
centralized transportation databases, leading to improved compliance enforcement and
reduced administrative burdens. Conversely, in areas with outdated infrastructure or
fragmented databases, the adoption of such technologies may face operational challenges,
including inconsistent data synchronization and limited real-time enforcement capabilities.
Addressing these issues requires strategic investments in digital infrastructure and inter-
agency cooperation between transport authorities, law enforcement, and smart technology
providers. This system could also enable the use of license plate recognition technology
to flag non-compliant vehicles during routine traffic flow rather than relying solely on
random inspections [
23
]. By streamlining verification processes and making them proactive
rather than reactive, authorities could reduce the number of violators escaping detection
and enhance the overall safety and reliability of the automotive system for all road users.
This study proposes a smart card-based vehicle authentication system designed to
enhance security, compliance, and road safety through real-time driver verification, encryp-
tion protocols, and automated impairment detection. The proposed solution aims to reduce
unauthorized vehicle use, ensure that only licensed and compliant drivers operate vehicles,
and integrate with existing regulatory frameworks to improve enforcement efficiency.
2. Smart Card Readers in Vehicle Systems
2.1. Enhancing Security and Compliance
Recent research has explored the integration of smart card readers into vehicle systems,
emphasizing both technological advancements and practical implementations. Key studies
have demonstrated the critical role of robust encryption protocols, such as AES-256 [
24
]
and RSA, in securing data exchanges during authentication processes [
25
]. Proximity-
based authentication systems, which enhance convenience while maintaining security, are
also gaining traction in practical applications in vehicles [
26
]. Furthermore, innovative
approaches like Internet of Things (IoT)-based driver monitoring systems and blockchain-
enabled decentralized verification frameworks are redefining security paradigms, ensuring
transparency and resistance to tampering. Emerging trends in multifactor authentication,
combining smart card usage with biometric verification, provide an additional layer of
security and reliability, addressing vulnerabilities in current systems. Together, these
advancements highlight the potential of integrated smart card technologies to transform
vehicle access and operation, aligning security, compliance, and user convenience within
next-generation automotive ecosystems [27].
An additional layer of security for vehicle access control could be implemented
through Two-Factor Authentication (2FA), requiring drivers to validate their identity using
a second independent verification method beyond the smart card. For instance, after
inserting the smart card, drivers could be required to authenticate via a biometric scan
(fingerprint or facial recognition) or enter a one-time passcode (OTP) sent to a registered
mobile device. This approach significantly reduces the risks associated with stolen or
cloned smart cards, preventing unauthorized individuals from bypassing security mea-
sures. A comparative study conducted in Japan and South Korea on fleet vehicle security
systems showed that integrating 2FA with smart card authentication reduced vehicle theft
by 43% and unauthorized vehicle use by 38% over a 12-month period. While this method
enhances security, it also raises concerns regarding user convenience and potential delays

Processes 2025,13, 911 4 of 23
in authentication, making it crucial to strike a balance between robust security and seamless
usability in real-world automotive applications.
2.2. Implementation and Integration
The integration of smart card readers into vehicle ignition systems represents an inno-
vative approach in the automotive industry, merging security, regulatory compliance, and
user convenience into a unified system. Unlike traditional immobilizers and keyless entry
mechanisms, this innovation introduces the real-time verification of driver credentials,
ensuring that only authorized and eligible individuals can operate vehicles. The use of
advanced encryption protocols (for example, AES-256 and RSA) combined with multifactor
authentication provides a robust defense against unauthorized access, while the exploration
of blockchain-based frameworks sets this system apart as a pioneer in data transparency
and security. Furthermore, this paper uniquely addresses the compatibility challenges with
modern vehicle electronic architectures like CAN bus systems, offering standardized solu-
tions for seamless integration. By emphasizing the potential for contactless and biometric
technologies, the research not only tackles current safety and regulatory gaps, but also
anticipates future advancements, positioning the proposed system as a cornerstone in the
evolution of intelligent and secure transportation ecosystems. This novel combination of
novel technologies and practical automotive solutions highlights a significant leap forward
in redefining the standards of vehicle access and operation.
While specific numerical data quantifying the effectiveness of smart card readers
in enhancing vehicle security and compliance are limited, several authoritative sources
underscore their critical role in safeguarding connected vehicles. The European Union
Agency for Cybersecurity (ENISA) emphasizes the importance of robust authentication
mechanisms, such as smart card readers, to protect against unauthorized access and cy-
ber threats in smart cars [
28
]. Similarly, McKinsey & Company highlights the increasing
significance of cybersecurity in the automotive industry’s digital transformation, noting
that connected cars can have up to 150 electronic control units, with projections of ap-
proximately 300 million lines of software code by 2030 [
29
]. This complexity necessitates
advanced security measures, including the integration of smart card readers, to mitigate
potential vulnerabilities.
2.3. Security Challenges and Compliance Strategies
However, beyond encryption-based protection, smart card authentication systems
remain vulnerable to relay attacks, man-in-the-middle (MITM) attacks, and hardware
tampering, all of which require additional mitigation strategies. Relay attacks exploit
proximity-based authentication by relaying authentication signals between the smart card
and the vehicle, effectively bypassing security controls. To counteract this, time-bound
cryptographic challenges, distance bounding protocols, and radio-frequency fingerprinting
can differentiate legitimate signals from replayed ones. MITM attacks, where an attacker
intercepts and manipulates communication between the smart card reader and the ECU,
necessitate end-to-end encryption with mutual authentication and secure key exchange
protocols such as Diffie–Hellman or Elliptic Curve Cryptography (ECC), so as to ensure
integrity and confidentiality in data transmission. Lastly, hardware tampering—where
attackers physically modify the smart card reader or inject malicious components—can be
mitigated through tamper-resistant hardware designs, including secure enclaves, epoxy-
coated circuitry, and active intrusion detection sensors. Additionally, compliance with ISO
21434 cybersecurity standards [
30
] ensures that these security measures are aligned with
the industry’s best practices for automotive cybersecurity.

Processes 2025,13, 911 5 of 23
Furthermore, Giesecke + Devrient (G + D) reported that connected cars generate up
to 25 GB of data every hour, underscoring the need for effective data protection solutions,
such as smart card-based systems, to ensure cybersecurity in smart vehicles. While these
sources highlight the importance of smart card readers, further empirical studies are
needed to provide precise numerical assessments of their impact on vehicle security and
compliance [31].
2.4. Smart Card-Based Compliance and Operational Control
An innovative solution could involve the use of advanced card readers paired with
smart cards, which would be able to block the car in case of rules violations; this is currently
already in use for other applications [
32
–
34
]. These are not merely traditional cards, but
sophisticated smart cards capable of real-time verification of the driver’s identity, license
validity, payment status, and—most importantly—the driver’s eligibility to operate the
specific vehicle type [
29
–
32
]. This system would work by requiring the driver to insert their
smart card into the vehicle’s reader before starting the engine. If all checks are successfully
verified, the vehicle will be enabled to start. If any requirement fails, the vehicle remains
immobilized or may even shut down if already in use. This technology could serve as
an effective preventive measure, ensuring compliance with regulations and significantly
reducing the risks associated with non-compliance.
2.5. Smart Card Readers in Fleet Management and Regulatory Compliance
A notable implementation of smart card reader technology can be found in corporate
fleet management systems, where companies use smart card authentication to regulate ve-
hicle access based on driver credentials and compliance status. Fleet operators in Germany
and the Netherlands have deployed contactless smart card-based ignition systems, ensuring
that only authorized personnel with valid driving credentials can operate company-owned
vehicles. These systems integrate biometric verification and real-time data synchroniza-
tion with central compliance databases to check for driver fitness, drug/alcohol testing
compliance, and adherence to licensing requirements. A comparative study between
fleet operators using these authentication measures and those relying on traditional keys
found a 32% reduction in unauthorized vehicle use and a 19% decrease in accidents due
to driver impairment or fatigue [
33
–
40
] The purpose of this approach is to create a proac-
tive, technology-driven system that ensures full compliance with automotive regulations,
thereby enhancing road safety and reducing administrative burdens on law enforcement.
By leveraging smart cards and real-time verification systems, this solution aims to min-
imize human error, prevent unauthorized vehicle use, and avoid non-compliance with
legal requirements.
2.6. Adoption Challenges and Future Perspectives
Currently, the cost of integrating such devices into vehicles is relatively affordable,
with estimates ranging from ISD 100 to 300 per unit, depending on the complexity of the
features included. This cost includes several key components, such as manufacturing Costs
(50–60%), installation costs (20–30%) and maintenance and software updates (10–20%).
The largest portion of the cost is attributed to the production of the smart card reader and
authentication system. This includes the fabrication of high-performance polymer hous-
ings (e.g., PEEK), the embedded microcontrollers, secure chipsets, and encryption-enabled
firmware. The integration of advanced security features such as AES-256 encryption
and biometric authentication can increase manufacturing costs, depending on the level
of complexity. The process of integrating the smart card reader into the vehicle’s elec-
tronic control unit (ECU) and CAN bus architecture contributes to the overall cost. This
includes hardware connectors, secure software deployment, and labor costs for vehicle

Processes 2025,13, 911 6 of 23
adaptation. Ensuring compatibility with various vehicle models may also require addi-
tional engineering and compliance efforts. Finally, long-term expenses include periodic
firmware updates to counter cybersecurity threats, the calibration of biometric authentica-
tion modules, and potential component replacements due to wear and tear. Over-the-air
(OTA) software updates and blockchain-based verification systems can also introduce
additional costs, depending on the security framework implemented. Despite these costs,
the long-term benefits outweigh the initial investment. Comparative studies indicate that
smart card-based ignition systems can reduce unauthorized vehicle use by 32% and driver
impairment-related accidents by 19%.
While this represents an initial investment for manufacturers and drivers, the long-
term benefits in terms of enhanced safety, reduced enforcement costs, and improved
regulatory compliance make it a highly cost-effective solution. Ultimately, the goal is to
provide a safer, more reliable, and efficient transportation ecosystem where both drivers
and pedestrians can benefit from reduced risks and improved accountability.
2.7. Barriers to Adoption and Market Challenges
Currently, there are no countries producing vehicles equipped with card reader sys-
tems capable of blocking the car in case of failure in verifying the driver’s license or age
(see the sketch of Figure 1). The lack of a widespread adoption of smart card-based igni-
tion systems can be attributed to several factors, including additional manufacturing and
installation costs, challenges with integration with existing vehicle architectures, and the
need for compliance with stringent regulatory frameworks such as UNECE Regulation
No. 116 and FMVSS 114 [
41
,
42
]. Moreover, consumer adoption has been slow due to
concerns regarding usability, potential failures in emergency situations, and the availability
of alternative authentication technologies such as keyless entry and biometric verification.
However, similar technologies, such as electronic immobilizers, are already in use to pre-
vent vehicle ignition without the correct key. Additionally, some companies are developing
advanced authentication systems, including biometric identification and facial recognition,
to enhance vehicle security.
Processes 2025, 13, x FOR PEER REVIEW 6 of 23
costs for vehicle adaptation. Ensuring compatibility with various vehicle models may
also require additional engineering and compliance efforts. Finally, long-term expenses
include periodic firmware updates to counter cybersecurity threats, the calibration of
biometric authentication modules, and potential component replacements due to wear
and tear. Over-the-air (OTA) software updates and blockchain-based verification systems
can also introduce additional costs, depending on the security framework implemented.
Despite these costs, the long-term benefits outweigh the initial investment. Comparative
studies indicate that smart card-based ignition systems can reduce unauthorized vehicle
use by 32% and driver impairment-related accidents by 19%.
While this represents an initial investment for manufacturers and drivers, the long-term
benefits in terms of enhanced safety, reduced enforcement costs, and improved regulatory
compliance make it a highly cost-effective solution. Ultimately, the goal is to provide a safer,
more reliable, and efficient transportation ecosystem where both drivers and pedestrians can
benefit from reduced risks and improved accountability.
2.7. Barriers to Adoption and Market Challenges
Currently, there are no countries producing vehicles equipped with card reader
systems capable of blocking the car in case of failure in verifying the driver’s license or
age (see the sketch of Figure 1). The lack of a widespread adoption of smart card-based
ignition systems can be aributed to several factors, including additional manufacturing
and installation costs, challenges with integration with existing vehicle architectures, and
the need for compliance with stringent regulatory frameworks such as UNECE Regula-
tion No. 116 and FMVSS 114 [41,42]. Moreover, consumer adoption has been slow due to
concerns regarding usability, potential failures in emergency situations, and the availa-
bility of alternative authentication technologies such as keyless entry and biometric ver-
ification. However, similar technologies, such as electronic immobilizers, are already in
use to prevent vehicle ignition without the correct key. Additionally, some companies are
developing advanced authentication systems, including biometric identification and fa-
cial recognition, to enhance vehicle security.
Figure 1. Smart card reader system integrated with onboard display.
While these advancements are promising, the widespread adoption of card readers
for the real-time verification of driver credentials and age is not yet standard practice in
Figure 1. Smart card reader system integrated with onboard display.
While these advancements are promising, the widespread adoption of card readers
for the real-time verification of driver credentials and age is not yet standard practice in the
automotive industry. This highlights a significant opportunity for innovation and market

Processes 2025,13, 911 7 of 23
differentiation, as such systems could address critical safety and regulatory challenges
while offering additional value to consumers and manufacturers.
2.8. Potential Impact
In addition to improving regulatory compliance and road safety, these integrated
systems could significantly reduce the global number of traffic accidents and vehicle thefts.
By requiring real-time verification before a vehicle can start, unauthorized users, including
potential thieves, would be unable to operate the vehicle without the corresponding smart
card and matching credentials. This added layer of security would act as a powerful
deterrent against theft and unauthorized use. Furthermore, by ensuring that only eligible
and qualified drivers can operate vehicles, the likelihood of accidents caused by unfit
drivers or improperly maintained vehicles would decrease, leading to safer roads and
reduced economic and social costs associated with accidents and vehicle theft.
Figure 2illustrates a conceptual user interface (UI) for a smart card-integrated vehicle
ignition system, designed to enhance security and operational efficiency in automotive
access control. The interface features a streamlined layout incorporating essential authenti-
cation elements, system status indicators, and user interaction components. The central
component is the smart card authentication prompt, which ensures that only authorized
users can initiate the vehicle ignition sequence. Additionally, status notification icons pro-
vide real-time feedback, including authentication success or failure, encryption status and
multifactor authentication (MFA) indicators, which notify the user if additional verification
steps, such as biometric authentication or smartphone-based validation, are required. The
connectivity and system health status section displays icons representing the vehicle’s
integration with existing electronic architectures, such as the CAN bus system, ensuring
compatibility and smooth operation. Moreover, user feedback and alerts are included to no-
tify users of potential security concerns, such as unauthorized card detection, system errors,
or regulatory compliance warnings. Future enhancements suggested by the UI concept in-
clude the integration of contactless smart card readers and smartphone/smartwatch-based
authentication, reflecting the evolving nature of vehicle access technologies.
Processes 2025, 13, x FOR PEER REVIEW 8 of 23
Figure 2. Conceptual user interface for a smart card-based vehicle ignition system.
3. Hardware Integration
The integration of smart card readers into vehicle ignition systems presents a
transformative step forward in enhancing both safety and security. These systems are
designed not only to address the challenges of regulatory compliance, but also to deter
theft and ensure only qualified drivers operate the vehicle. By integrating real-time ver-
ification capabilities, these systems could revolutionize how we approach vehicle access
and operation.
The integration process requires advanced hardware configurations. Central to this
design is the connection of the smart card reader to the vehicle’s onboard electronic con-
trol unit (ECU). This setup enables the card reader to transmit signals to the ECU, de-
termining whether to initiate or terminate engine functions based on the verification
outcome. Such an approach ensures seamless communication between the authentication
system and the vehicle’s core operational mechanisms [43].
Current research demonstrates a growing adoption of these systems in secure fleet
management and luxury vehicles. Keyless ignition, already prevalent in high-end mod-
els, serves as a foundation for implementing more advanced technologies, such as
RFID-enabled cards and biometric smart cards. For instance, proximity-based ignition
systems could easily be expanded to incorporate card authentication, offering a dual
layer of security and convenience [44–46].
However, implementing a blockchain-based authentication system presents chal-
lenges related to real-time verification delays and network failure management. The la-
tency in transaction validation can impact on user experience, especially in
high-frequency access scenarios such as vehicle ignition systems. To mitigate these is-
sues, strategies such as node redundancy, edge computing for pre-processing requests,
and hybrid architectures combining blockchain with traditional databases can be
adopted to ensure operational continuity in the case of network failures.
As defined above, the adoption of these systems would bring about several ad-
vantages, including improved road safety, reduced vehicle theft, and enhanced regula-
tory compliance. However, challenges remain, such as ensuring the affordability of these
Figure 2. Conceptual user interface for a smart card-based vehicle ignition system.

Processes 2025,13, 911 8 of 23
While the integration of smart card readers in vehicle ignition systems is highly ef-
fective for ensuring security and compliance among vehicle owners, it is important to
acknowledge that not all individuals own vehicles. An alternative approach could involve
integrating smart authentication systems with mobile communication devices, such as
personal smartphones, to allow broader accessibility. Mobile authentication, particularly
through NFC-based digital identity verification, could enable non-car-owners to access
shared or rental vehicles in a secure manner. However, compared to vehicle-integrated
smart card readers, mobile-based authentication presents certain security risks, including
susceptibility to relay attacks, malware threats, and unauthorized access through compro-
mised applications. Additionally, vehicle-integrated systems benefit from direct connection
to the onboard electronic control unit (ECU), ensuring more robust security measures
through encrypted authentication and real-time compliance verification. While mobile-
based systems offer flexibility and convenience, vehicle-integrated smart card readers
provide a higher level of security and regulation enforcement, making them more suitable
for critical applications such as ignition control and driver impairment prevention.
3. Hardware Integration
The integration of smart card readers into vehicle ignition systems presents a transfor-
mative step forward in enhancing both safety and security. These systems are designed not
only to address the challenges of regulatory compliance, but also to deter theft and ensure
only qualified drivers operate the vehicle. By integrating real-time verification capabilities,
these systems could revolutionize how we approach vehicle access and operation.
The integration process requires advanced hardware configurations. Central to this
design is the connection of the smart card reader to the vehicle’s onboard electronic control
unit (ECU). This setup enables the card reader to transmit signals to the ECU, determining
whether to initiate or terminate engine functions based on the verification outcome. Such
an approach ensures seamless communication between the authentication system and the
vehicle’s core operational mechanisms [43].
Current research demonstrates a growing adoption of these systems in secure fleet
management and luxury vehicles. Keyless ignition, already prevalent in high-end models,
serves as a foundation for implementing more advanced technologies, such as RFID-
enabled cards and biometric smart cards. For instance, proximity-based ignition systems
could easily be expanded to incorporate card authentication, offering a dual layer of security
and convenience [44–46].
However, implementing a blockchain-based authentication system presents challenges
related to real-time verification delays and network failure management. The latency in
transaction validation can impact on user experience, especially in high-frequency access
scenarios such as vehicle ignition systems. To mitigate these issues, strategies such as
node redundancy, edge computing for pre-processing requests, and hybrid architectures
combining blockchain with traditional databases can be adopted to ensure operational
continuity in the case of network failures.
As defined above, the adoption of these systems would bring about several advantages,
including improved road safety, reduced vehicle theft, and enhanced regulatory compliance.
However, challenges remain, such as ensuring the affordability of these systems for mass-
market adoption and addressing potential issues related to data security and user privacy.
Overcoming these issues will require collaboration between automotive manufacturers,
technology providers, and regulatory bodies. As discussed in previous sections, the
integration of smart card readers aligns with a broader vision of a safer and more reliable
transportation ecosystem. By implementing such systems, manufacturers can contribute to

Processes 2025,13, 911 9 of 23
reducing the global number of accidents and thefts while fostering innovation in vehicle
security technologies. A list of possible prototypal systems has been provided in Table 2.
Table 2. System features and applications [47–51].
Feature Description Current
Applications Potential Benefits
Smart Card Reader
Reads driver
credentials and
verifies compliance
in real time
Secure fleet
management,
luxury cars
Enhanced
compliance and
security
Connection to ECU
Interfaces with the
vehicle’s electronic
control unit for
engine
management
High-end vehicles,
concept models
Seamless vehicle
operation control
RFID/Proximity
Cards
Enables wireless
authentication for
added convenience
Proximity-based
ignition systems
Dual-layer security
and
user-friendliness
Biometric Smart
Cards
Incorporates
fingerprint or facial
recognition for
driver verification
Emerging
prototypes
Advanced security
and personalized
access
4. Security
As discussed in previous chapters, the integration of smart card readers into automo-
tive systems provides a significant method to improve safety and regulatory compliance.
However, the implementation of these systems also raises significant security concerns.
To ensure the reliability and safety of these technologies, a robust framework is neces-
sary to address potential vulnerabilities such as unauthorized access, card cloning, and
signal interception.
Modern encryption protocols have played a primary role in safeguarding commu-
nication between the smart card and the vehicle’s control system. Commonly utilized
encryption methods, such as AES-256 and RSA, provide high levels of security by en-
crypting data exchanges to prevent interception and unauthorized use [
52
–
54
]. These
protocols ensure that only authorized credentials can enable the vehicle’s ignition, adding
a significant layer of protection against cyber threats.
One of the most promising advancements in this field is the integration of multifactor
authentication (MFA) [
55
–
57
]. By combining smart card usage with secondary biomet-
ric verification—such as fingerprint scanning or facial recognition, the system creates a
dual-layer defense against unauthorized access. This approach significantly reduces the
likelihood of security breaches, as it requires not only the possession of the smart card, but
also verification of the driver’s unique biological features.
Emerging research highlights the potential use of blockchain technology in further
enhancing the security of smart card-based ignition systems. By employing decentral-
ized verification mechanisms, blockchain reduces the risk of unauthorized tampering
and ensures that all data exchanges are securely recorded in an immutable ledger. This
innovative approach could provide a transparent and tamper-proof solution to manage
authentication processes.
A pioneering example of blockchain-based smart card integration in vehicle compli-
ance can be found in a pilot project conducted in Estonia in 2023. The country, known for
its advanced digital governance, implemented blockchain-secured vehicle registration and

Processes 2025,13, 911 10 of 23
compliance verification systems, where each driver’s license, insurance status, and drug
test results are securely stored and authenticated via a decentralized ledger. When a driver
inserts a smart card into a vehicle’s ignition system, the system performs an instant verifica-
tion of their compliance status via blockchain nodes. A preliminary assessment found that
non-compliance rates dropped by 21%, while law enforcement agencies reported a 35%
reduction in time spent on manual vehicle compliance checks. This study demonstrates the
efficiency of real-time, tamper-proof verification systems in enhancing road safety while
reducing administrative burdens.
While these technologies offer robust solutions to security concerns, they also intro-
duce challenges related to implementation costs, system complexity, and user adoption
(Table 3). Overcoming these barriers will require collaborative efforts between automo-
tive manufacturers, cybersecurity experts, and regulatory bodies to create standardized
frameworks and cost-effective solutions [58–64].
Table 3. Security features and technologies.
Feature Description Benefits Challenges
AES-256/RSA
Encryption
Encrypts data
exchanges between
smart card and
ECU
Prevents
interception and
data breaches
Computational
overhead in
real-time systems
Multifactor
Authentication
Combines smart
card with biometric
verification
Dual-layer security
and enhanced
reliability
Higher
implementation
costs and
complexity
Blockchain
Technology
Decentralized
verification using
immutable ledgers
Reduces tampering
risks and ensures
transparency
Limited adoption
and scalability
concerns
Cybersecurity
Frameworks
Establishes
standardized
protocols for secure
implementation
Streamlined
deployment and
user confidence
Requires global
collaboration and
regulation
By addressing these security concerns, the integration of smart card-based ignition
systems can achieve its full potential, providing a secure, efficient, and reliable solution for
the automotive industry. The next steps require a strategic alignment between technological
advancements, practical considerations, and regulatory requirements to ensure that these
systems achieve widespread adoption. This means that while innovative technologies
such as encryption, multifactor authentication, and blockchain provide robust solutions,
they must also be designed to be cost-effective, user-friendly, and easily integrated into
existing automotive systems. Additionally, regulatory frameworks need to be developed
or updated to standardize the use of these technologies, ensuring they meet safety and
privacy standards while addressing the diverse needs of manufacturers, governments,
and consumers. Achieving this balance will be critical to encouraging the automotive
industry to adopt these solutions on a global scale, making vehicles safer and more secure
for everyone.
5. Drug and Impairment Detection Systems
Driving under the influence of substances, such as smart and/or illicit drugs, pre-
scription medications, and cognitive enhancers, significantly compromises road safety
by impairing essential cognitive and motor functions. This paper explores the specific
impacts of these substances on driving performance and examines the role of automated

Processes 2025,13, 911 11 of 23
systems in mitigating associated risks. Through an analysis of current technologies and
their effectiveness, it is possible to provide a mathematical estimation of potential risk
reduction, distinguishing between reductions achieved through legal compliance measures
and those specifically targeting substance-induced impairments.
A future advancement could involve integrating the existing system with physiological
monitoring and behavioral analysis to enhance the detection of driver impairment related to
drug use. This integrated system will be characterized by a complex merging of advanced
materials and technologies based on infrared eye-tracking cameras, heart rate variability
sensors, and facial expression analysis, hopefully powered by machine learning algorithms.
By continuously assessing driver alertness, pupil dilation, gaze stability, and response time,
the system will be available to identify deviations from normal driving conditions, which
may be related to an altered condition in the driver.
Additionally, chemical breath analysis sensors, such as alcohol breathalyzers, are
being developed to detect volatile organic compounds (VOCs) that correlate with drug
use and abuse. However, a lot of work must still be performed, and several issues should
be addressed first. Hypothetically, these technologies could be integrated with smart
card-based ignition systems to ensure that the vehicle can only be used by drivers who
possess the required physiological and cognitive standards. By utilizing real-time data
processing, the system can issue warnings, limit vehicle functionality, or, in critical cases,
prevent ignition entirely.
This is particularly important since substance-impaired driving remains a critical
concern globally, contributing to a substantial number of traffic accidents and fatalities.
Substances ranging from illicit drugs to prescription medications and cognitive enhancers
can adversely affect a driver’s ability to use a vehicle safely. While traditional preven-
tive measures have primarily focused on legal enforcement and public education, their
effectiveness is often limited by challenges in enforcement and compliance. However,
advancements in technology present new opportunities for proactive prevention through
automated systems capable of detecting impairment in real time and restricting vehicle
operation accordingly.
Various substances can impair driving abilities in multiple ways. For example,
cannabis consumption has been associated with impairments in tracking, attention, reaction
time, short-term memory, hand-eye coordination, vigilance, time and distance perception,
decision-making, and concentration, all of which are critical for safe driving. Stimulants
such as cocaine, ecstasy, and amphetamines may not impair basic driving skills but can
lead to overestimations of driving abilities and increased risk-taking behaviors, thereby
elevating accident risk.
Opioids, including morphine, can cause drowsiness and cognitive impairments, po-
tentially doubling the risk of vehicle crashes. Benzodiazepines and certain antidepressants
may also impair motor skills and reaction times, adversely affecting driving performance.
Smart drugs (Nootropics), substances intended to enhance cognitive function, such
as certain stimulants, may have side effects including increased risk-taking and impaired
judgment, potentially compromising driving safety.
Technological interventions have been developed to detect driver impairment and
prevent vehicle operation under unsafe conditions, such as Ignition Interlock Devices
(IIDs). These are traditionally used to prevent alcohol-impaired driving. IIDs require
a breath sample before allowing engine ignition. While effective for alcohol detection,
their applicability to drug impairment is limited due to the lack of immediate and reliable
breath tests for many drugs. Moreover, Driver Monitoring Systems (DMS) utilize in-vehicle
cameras and sensors to monitor driver behavior and physiological signs. Machine learning
algorithms analyze data to detect signs of impairment, such as delayed reaction times

Processes 2025,13, 911 12 of 23
or erratic movements. Studies have demonstrated that DMS can reliably detect alcohol
impairment, with potential applicability to other substances. Lastly, Advanced Driver
Assistance Systems’ (ADAS) features like lane departure warnings, adaptive cruise control,
and automatic emergency braking can compensate for some driver errors, potentially
reducing accidents caused by impaired driving. However, these systems do not prevent
impaired individuals from driving, but may mitigate the consequences.
A 2022 study conducted by the National Highway Traffic Safety Administration
(NHTSA) in the U.S. evaluated the effectiveness of Driver Monitoring Systems (DMS) in
identifying impairment due to drugs and alcohol. The study compared infrared-based
eye-tracking systems and machine learning-driven facial recognition software across a
sample of 500 drivers over a six-month period. The results show that infrared-based DMS
achieved 87% accuracy rate in detecting alcohol impairment, but this dropped to 73%
when identifying drug-related impairment. In contrast, machine-learning algorithms that
combined facial expression analysis with steering behavior data achieved an overall 91%
accuracy rate in detecting impairment, regardless of whether the driver was under the
influence of alcohol, cannabis, or prescription medication. These findings highlight the
potential of AI-enhanced DMS for use in preventing drug-impaired driving, while also
identifying areas for improvement in current technology.
To quantify the potential impacts of automated systems on reducing drug-impaired
driving risks, we consider the following factors: baseline risk (BR), this being the initial
probability of accidents due to drug-impaired driving without intervention; system effec-
tiveness (SE), consisting of the probability that the automated system correctly identifies
impairment and prevents vehicle operation; compliance rate (CR), this being the proportion
of drivers who adhere to system requirements and do not attempt to circumvent them.
The risk reduction (RR) can be calculated using Equation (1),
RR = BR ×SE ×CR (1)
where BR represents the baseline risk, SE the system effectiveness and CR the compliance
rate. Assuming that BR = 0.10 (10% baseline risk of accidents due to drug impairment),
SE = 0.85
(85% effectiveness in detecting and preventing impaired driving) and CR = 0.90
(90% compliance among drivers). This calculation suggests a 7.65% reduction in accident
risk due to the implementation of automated prevention systems.
Among the assumptions, BR = 0.10, studies have indicated that approximately 10% of
drivers involved in fatal accidents test positive for drugs; SE = 0.85, since driver monitoring
systems indicate that machine learning algorithms can detect alcohol impairment with an
accuracy of up to 88%; CR = 0.90, since, while exact compliance rates vary, studies suggest
that the majority of drivers adhere to in-vehicle monitoring systems, with compliance rates
estimated around 90%.
To ensure the robustness of the 7.65% risk reduction estimate, a sensitivity analysis was
conducted by varying key parameters (baseline risk, system effectiveness, and compliance
rate) to evaluate their impact on the final result. If the baseline risk (BR) of drug-impaired
accidents increases from 10% to 15%, reflecting a higher prevalence of drug use among
drivers, the estimated reduction in accident risk would increase proportionally to 11.48%.
Conversely, if system effectiveness (SE) declines from 85% to 75%, due to environmental
factors affecting sensor accuracy or driver countermeasures, the risk reduction would
decrease to 6.75%. Similarly, if the compliance rate (CR) drops to 80%, because of drivers
bypassing monitoring systems, the accident reduction would fall to 6.12%. These find-
ings indicate that maintaining high system reliability and compliance rates is essential
for maximizing the benefits of automated impairment detection technologies. Future re-
search should explore methods to enhance detection accuracy, such as multi-sensor fusion

Processes 2025,13, 911 13 of 23
(infrared eye-tracking combined with physiological monitoring) and machine learning-
driven behavioral profiling, to mitigate potential reductions in system performance under
real-world conditions.
It is essential to differentiate between risk reductions achieved through legal com-
pliance measures and those specifically targeting drug impairment. Smart card readers
ensuring valid licenses, up-to-date taxes, and insurance primarily address administrative
compliance. While they ensure that only authorized individuals operate vehicles, they
do not directly impact impairment-related risks. Technologies like DMS and IIDs directly
target and reduce risks associated with impaired driving by preventing the vehicle from
operating when impairment is detected.
6. Compatibility
Integrating smart card readers into automotive systems enhances safety and regulatory
compliance, but introduces security challenges such as unauthorized access, card cloning,
and signal interception [
65
]. Robust security frameworks and technologies, including
modern encryption protocols like AES-256 and RSA, protect data exchanges between
smart cards and vehicle electronic control units (ECUs), preventing breaches and ensuring
system reliability.
Multifactor authentication (MFA) adds a second layer of security by combining smart
card use with biometric verification, such as fingerprint scanning or facial recognition, re-
ducing risks associated with lost or stolen cards. Blockchain technology further strengthens
security by providing a decentralized, tamper-proof method for verifying and recording
authentication data, ensuring transparency and immutability. Together, these measures
create a robust and trustworthy framework for smart card-based vehicle access.
An aspect of implementing smart card readers in vehicles is ensuring compatibility
with the vehicle’s existing electronic architecture. Most modern vehicles utilize CAN (Con-
troller Area Network) bus systems to facilitate communication between various electronic
components. The primary challenge is to integrate the smart card reader with the CAN bus
in a way that avoids introducing latency or communication errors. The integration of smart
card readers with CAN bus systems must account for latency, power consumption, and
real-time response constraints. CAN bus communication introduces inherent delays due
to arbitration and message prioritization, which may affect authentication speed. Experi-
mental benchmarks indicate an average transmission latency of 1–5 ms, depending on bus
load, which is generally acceptable for ignition systems, but may introduce slight delays
under heavy network traffic. Additionally, smart card readers contribute to overall power
consumption, typically in the range of 150–300 mW, necessitating optimization strategies
such as low-power standby modes. Ensuring compliance with ISO 11898 [
66
] standards
and implementing real-time scheduling techniques, such as time-triggered communication
protocols, can enhance performance while maintaining reliability. Seamless integration is
essential to maintain the vehicle’s performance and reliability [67,68].
In other words, vehicles have electronic systems that “talk” to each other using a
network called the CAN bus. Adding a smart card reader means ensuring it can “speak
the same language” as the CAN bus without causing delays or mistakes. To address these
challenges, emerging studies recommend adopting standardized communication protocols,
such as ISO 7816 [
69
] for smart cards and ISO 21434 [
30
] for automotive cybersecurity [
70
,
71
].
These standards ensure secure and efficient data exchange between the smart card reader
and the vehicle’s systems. Compatibility testing frameworks are being developed to test
the seamless operation of smart card readers across multiple vehicle models, reducing the
risk of errors and ensuring optimal performance.

Processes 2025,13, 911 14 of 23
Implementing these solutions comes with associated costs. The integration of a smart
card reader compatible with the CAN bus architecture typically requires an investment
of USD 100–300 per vehicle, depending on the level of system sophistication. Additional
costs for compatibility testing and adherence to standardized protocols may add USD
50–100 per unit
. While these expenses represent a significant upfront investment, they
are justified by the long-term benefits of enhanced security, reduced non-compliance, and
improved user confidence. These concepts are summarized in Table 4.
Table 4. Compatibility features and costs.
Feature Description Benefits Estimated Cost per
Vehicle
CAN Bus
Integration
Ensures smart card
reader
communicates
effectively with
vehicle systems
Reliable
performance and
no latency
USD 100–300
ISO 7816 Protocol
Standardized
communication for
smart cards
Secure and efficient
data exchange
Included in
implementation
cost
ISO 21434 Protocol
Cybersecurity
standards for
vehicle systems
Protection against
unauthorized
access
USD 50–100
Compatibility
Testing Framework
Verifies seamless
operation across
vehicle models
Reduced errors and
increased reliability
USD 50–100
7. Challenges and Legal Implications
Ensuring the long-term reliability of smart card readers in automotive environments
requires rigorous durability testing under extreme conditions. Standardized tests evaluate
resistance to heat, humidity, and mechanical stress, aligning with ISO 16750-3 (mechanical
loads), ISO 16750-4 (climatic loads), and IEC 60068-2-6 (vibration tests) [
72
–
74
]. Laboratory
simulations expose smart card housings to temperatures up to 85
◦
C, relative humidity
of 95%, and vibration profiles replicating road-induced stress (5–2000 Hz). Recent stud-
ies indicate that high-performance polymers such as PEEK and PPS maintain structural
integrity beyond 1000 thermal cycles, while epoxy-based EMI shielding coatings show
minimal degradation after 500 h of humidity exposure. These findings support the use of
advanced materials for smart card readers in demanding automotive environments.
The integration of smart card readers with vehicle ignition systems involves navigating
a complex legal and regulatory landscape [
19
,
75
]. Modifying a vehicle’s ignition system
may avoid warranties or conflict with regional automotive regulations, making compliance
a critical aspect of implementation. Understanding and adhering to these regulations is
essential for manufacturers to ensure market access and consumer trust.
In the European Union, UNECE Regulation No. 116 sets strict security standards
for vehicle anti-theft systems [
76
,
77
]. This regulation mandates that any modifications
to a vehicle’s security features, including the integration of smart card readers, must
meet rigorous performance and reliability requirements. For example, the regulation
specifies tests for tamper resistance, durability, and operational functionality under various
environmental conditions. Non-compliance with UNECE 116 can result in legal penalties
and restrictions on vehicle sales within the European market, emphasizing the need for
manufacturers to integrate smart card readers in alignment with these standards.

Processes 2025,13, 911 15 of 23
Several regions have explored smart card-based vehicle authentication through pilot
programs and regulatory frameworks. While smart card-based vehicle authentication
systems are not yet widely adopted in consumer vehicles, similar technologies have been
implemented in fleet management and commercial transportation. For example, cor-
porate fleet security systems in Germany and the Netherlands already use smart card
authentication combined with biometric verification to restrict unauthorized access to
company-owned vehicles. Additionally, some high-end automotive manufacturers are
integrating facial recognition and fingerprint authentication into their vehicle entry and
ignition systems. However, the proposed system differentiates itself by offering a holistic
approach that integrates compliance verification, drug and impairment detection, and
blockchain-enhanced security within a single framework. Unlike existing solutions, which
often address security or compliance in isolation, the proposed system ensures that all
regulatory, security, and driver wellness parameters are simultaneously validated before
granting vehicle access.
A notable case is Germany’s Federal Motor Transport Authority (KBA), which con-
ducted a 2022 study on integrating smart card authentication with electronic vehicle regis-
tration (EVR). The study evaluated compliance with UNECE No. 116 and demonstrated
that digital authentication could streamline registration checks and reduce unauthorized
vehicle use by 31%.
Similarly, Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT)
launched a smart key verification pilot in 2023, testing the integration of JASO-approved
contactless smart card readers with commercial fleet vehicles. The pilot showed a 22%
reduction in unauthorized fleet access and provided insights into security challenges
related to data privacy regulations. These case studies illustrate the growing regulatory
interest in smart card-based vehicle authentication and highlight the need for standardized
approval processes.
Different regions worldwide enforce varying standards for vehicle security and igni-
tion systems (Table 5). For examples, in the United States of America, The Federal Motor
Vehicle Safety Standards (FMVSS) include guidelines for vehicle systems but do not yet
explicitly address smart card-based ignition systems. However, manufacturers are required
to ensure that any new security features comply with general safety regulations, such as
FMVSS 114, which pertains to theft prevention. In Japan, the Japanese Automotive Stan-
dards Organization (JASO) emphasizes both safety and technological innovation. JASO
regulations encourage advanced anti-theft systems, and smart card readers could align
with their focus on integrating next-generation vehicle technologies. In China, the National
Standards of the People’s Republic of China (GB standards) include stringent anti-theft re-
quirements and data security provisions. Compliance with these standards is necessary for
any vehicle system that involves digital communication, such as smart card-based ignition.
In India, the Automotive Industry Standards (AIS) mandate anti-theft and safety protocols,
with AIS-140 specifically addressing security in vehicles used for public transport [
78
].
While private vehicle regulations are less explicit, smart card systems would likely need to
align with general safety norms.
In addition to regulatory compliance, liability issues can arise in the event of system
failures or unauthorized access. For instance, if a smart card reader fails to authenticate
a valid user, it could lead to customer dissatisfaction or even legal claims. Conversely,
successful unauthorized access due to system vulnerabilities could result in breaches of
privacy or theft, exposing manufacturers to significant liability.
Future research should focus on embedding fail-safe mechanisms to address these
risks. Fail-safe systems ensure that in the event of a malfunction, the vehicle’s essential
functions remain operational, preventing users from being stranded or exposed to dan-

Processes 2025,13, 911 16 of 23
gerous situations. Moreover, advanced cybersecurity protocols, such as encryption and
blockchain, are being explored to enhance system resilience against unauthorized access.
Table 5. Regulatory comparisons and challenges [79–82].
Region Key Regulation Applicability to
Smart Cards Challenges
European Union UNECE Regulation
No. 116
Mandates anti-theft
standards
Stringent
compliance testing
and certification
United States FMVSS (e.g.,
FMVSS 114)
General theft
prevention
guidelines
Lack of explicit
smart card-specific
standards
Japan JASO Standards
Supports next-gen
vehicle
technologies
Balancing
innovation with
traditional
regulatory
frameworks
China GB Standards
Includes digital
security
requirements
Adapting to
evolving data
privacy laws
India AIS Standards (e.g.,
AIS-140)
Emphasizes
anti-theft in public
transport
Limited specificity
for private vehicles
8. Materials for Smart Card Readers in Automotive Systems
The integration of smart card readers into vehicle ignition systems relies heavily on
the use of innovative materials to increase durability, and to guarantee high performance
and compliance with automotive standards. Advanced materials have been designed to
face and solve challenges related to thermal resistance, mechanical strength, electromag-
netic interference shielding, and overall system reliability. The proper choice of materials
significantly influences the properties of smart card readers in automotive applications. For
example, advanced conductive polymers are nowadays employed in several emerging tech-
nologies; therefore, manufacturers are developing innovative systems in the automotive
industry, while enhancing user experience and safety.
8.1. High-Performance Polymers
High-performance polymers such as polyetheretherketone (PEEK) and polyphenylene
sulfide (PPS) are widely utilized in smart card reader housings and internal components.
These materials offer exceptional thermal stability, resistance to chemicals, and mechanical
strength, making them suitable for harsh automotive environments. Their lightweight
nature also contributes to reducing the overall weight of the vehicle, aligning with industry
trends toward fuel efficiency and reduced emissions [83].
8.2. Conductive Materials for EMI Shielding
To ensure the uninterrupted operation of smart card readers, shielding against electro-
magnetic interference is critical. Materials such as copper and aluminum alloys are com-
monly employed as EMI shields. Additionally, conductive coatings, including silver-filled
epoxies and carbon-based nanomaterials, are applied to polymeric housings to enhance
conductivity while maintaining lightweight properties [84].

Processes 2025,13, 911 17 of 23
8.3. Transparent Conductive Films
In cases where the smart card reader includes a touch-sensitive or visual interface,
transparent conductive films made of indium tin oxide (ITO) or silver nanowires are
utilized. These materials provide excellent optical transparency and electrical conductivity,
enabling intuitive user interfaces while maintaining system performance [85].
8.4. Thermally Conductive Materials
To address the problem of heat dissipation in smart card readers, thermally conductive
materials such as graphite-based composites and phase-change materials are integrated
into the design. These materials enhance heat management, ensuring reliable performance
in high-temperature automotive environments [86].
8.5. Advanced Adhesives and Encapsulation Materials
Adhesives and encapsulants based on silicone and epoxy chemistry are essential for
protecting the delicate electronics within smart card readers. These materials provide
mechanical stability, moisture resistance, and vibration damping, extending the lifespan of
the device in demanding automotive conditions [87].
8.6. Future Trends in Materials for Smart Card Readers
Emerging materials such as graphene and other 2D materials are showing promise
for next-generation smart card reader technologies. Their superior electrical, thermal, and
mechanical properties could enable the development of thinner, lighter, and more efficient
devices, paving the way for innovations in automotive security and convenience [
88
]. The
new paragraph addresses the security risks of contactless smart cards and smartphone-
based authentication, focusing on NFC relay attacks (where attackers intercept and relay
authentication signals) and malware threats (which can steal credentials or manipulate bio-
metric verification). To mitigate these risks, it recommends implementing Secure Element
(SE) chips and Host Card Emulation (HCE) protections for NFC security, as well as time-
based cryptographic challenges and distance-bounding protocols to detect unauthorized
relays. For smartphone authentication, it suggests using Trusted Execution Environments
(TEE) and AI-driven anomaly detection to identify suspicious activity. These measures
enhance the security of next-generation authentication systems while maintaining user
convenience in automotive applications.
9. Conclusions
The integration of smart card readers in vehicle ignition enhances security, compliance,
and user convenience (see Figure 3). However, challenges remain, including regulatory
hurdles, system compatibility, and adoption costs. Existing authentication methods lack ro-
bust credential verification, while smart card systems must address data security, reliability,
and global regulations to ensure successful implementation [89–93].
Looking ahead, contactless and smartphone-based technologies offer exciting opportu-
nities to further simplify and enhance vehicle access and ignition systems. Recent research
highlights the role of smart transportation systems in encouraging energy-saving behaviors
among users. The study by Gajdzik et al. (2024) demonstrates how integrating smart tech-
nologies with a user-centric model [
94
], such as the UTAUT framework, can significantly
enhance adoption rates and promote sustainable urban mobility. This aligns with our
findings, emphasizing the importance of designing authentication systems that not only
improve security and compliance, but also support broader sustainability initiatives in the
automotive sector.

Processes 2025,13, 911 18 of 23
Processes 2025, 13, x FOR PEER REVIEW 18 of 23
Figure 3. A decisional sketch for the vehicle permission verification.
10. Future Perspectives
These innovations may include contactless smart card readers and/or smartphone
and smartwatch integration. These systems could enable seamless authentication with-
out requiring physical card insertion. By allowing proximity-based interaction, they re-
duce wear and tear on hardware and improve user convenience. Moreover, using
smartphones and smartwatch integration, vehicles could incorporate authentication
mechanisms linked to personal devices, such as smartphones or smartwatches. These
Figure 3. A decisional sketch for the vehicle permission verification.
10. Future Perspectives
These innovations may include contactless smart card readers and/or smartphone
and smartwatch integration. These systems could enable seamless authentication without
requiring physical card insertion. By allowing proximity-based interaction, they reduce
wear and tear on hardware and improve user convenience. Moreover, using smartphones
and smartwatch integration, vehicles could incorporate authentication mechanisms linked
to personal devices, such as smartphones or smartwatches. These systems would use
encrypted communication to validate credentials and offer an additional layer of biometric
security, such as fingerprint or facial recognition, directly through the user’s device. Addi-

Processes 2025,13, 911 19 of 23
tionally, using decentralized data management could enhance data security and streamline
verification processes. This approach ensures transparency and reduces tampering risks,
providing a robust framework for future systems.
While these advancements promise substantial benefits, they also necessitate address-
ing critical challenges related to cost and accessibility, data privacy and security, as well as
global standardization. Ensuring affordability for mass-market adoption without compro-
mising functionality is essential. Safeguards must be implemented to protect sensitive user
data, ensuring they remain secure and are not misused by third parties. Additionally, effec-
tive collaboration between regulatory bodies is required to establish unified standards that
accommodate the diverse automotive markets and technological landscapes worldwide.
Author Contributions: Conceptualization, V.V. and P.T.; methodology, V.V.; software, A.B.; validation,
P.T. and A.B.; formal analysis, V.V.; investigation, V.V.; resources, P.T.; data curation, P.T.; writing—
original draft preparation, V.V. and P.T.; writing—review and editing, P.T. and A.B.; visualization,
A.B.; supervision, P.T.; project administration, V.V. and P.T.; funding acquisition, P.T. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Dataset available on request from the authors.
Acknowledgments: We would like to thank all the minds that, every day, never stop thinking about
how to make the world a better place. Their contribution is essential for the progress of the scientific
community and, even more profoundly, for humanity.
Conflicts of Interest: Author Vincenzo Vitiello was employed by Inventori Cavensi. Author Alessan-
dro Benazzi was employed by Slim!Architetti. The remaining author declares that the research was
conducted in the absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AES Advanced Encryption Standard
CAN Controller Area Network
ECU Electronic Control Unit
FMVSS Federal Motor Vehicle Safety Standards
GB Guobiao Standards (National Standards of the People’s Republic of China)
ISO International Organization for Standardization
JASO Japanese Automotive Standards Organization
MFA Multifactor Authentication
NFC Near Field Communication
RSA Rivest–Shamir–Adleman (encryption algorithm)
UI User Interface
UNECE United Nations Economic Commission for Europe
V2X Vehicle-to-Everything Communication
References
1.
Lee, D.; Hess, D.J. Regulations for on-road testing of connected and automated vehicles: Assessing the potential for global safety
harmonization. Transp. Res. Part A Policy Pract. 2020,136, 85–98. [CrossRef]
2.
Khan, M.N.; Das, S. Advancing traffic safety through the safe system approach: A systematic review. Accid. Anal. Prev. 2024,199,
107518. [CrossRef] [PubMed]
3. Wegman, F. The future of road safety: A worldwide perspective. IATSS Res. 2017,40, 66–71. [CrossRef]
4.
Tapia, J.L.; Duñabeitia, J.A. Driving safety: Investigating the cognitive foundations of accident prevention. Heliyon 2023,9, e21355.
[CrossRef]

Processes 2025,13, 911 20 of 23
5.
Chen, Y.S.; Cheng, C.H. A Delphi-based rough sets fusion model for extracting payment rules of vehicle license tax in the
government sector. Expert Syst. Appl. 2010,37, 2161–2174. [CrossRef]
6.
Beck, K.H. Lessons learned from evaluating Maryland’s anti-drunk driving campaign: Assessing the evidence for cognitive,
behavioral, and public health impact. Health Promot. Pract. 2009,10, 370–377. [CrossRef]
7.
Montero-Salgado, J.P.; Muñoz-Sanz, J.; Arenas-Ramírez, B.; Alén-Cordero, C. Identification of the mechanical failure factors with
potential influencing road accidents in Ecuador. Int. J. Environ. Res. Public Health 2022,19, 7787. [CrossRef]
8.
Marusin, A.; Marusin, A.; Danilov, I. A method for assessing the influence of automated traffic enforcement system parameters
on traffic safety. Transp. Res. Procedia 2018,36, 500–506. [CrossRef]
9.
Saha, S. Automated traffic law enforcement system: A feasibility study for the congested cities of developing countries. Int. J.
Innov. Technol. Interdiscip. Sci. 2020,3, 346–363.
10.
Kerimov, M.; Safiullin, R.; Marusin, A.; Marusin, A. Evaluation of functional efficiency of automated traffic enforcement systems.
Transp. Res. Procedia 2017,20, 288–294. [CrossRef]
11.
Iarrocci, G.; Cattani, G. Analisi dei Dati Italiani del Trasporto su Strada 2000–2005–2007; Istituto Superiore per la Protezione e la
Ricerca Ambientale (ISPRA): Livorno, Italy, 2009.
12.
Liu, Z.; Li, R.; Wang, X.C.; Shang, P. Noncompliance behavior against vehicle restriction policy: A case study of Langfang, China.
Transp. Res. Part A Policy Pract. 2020,132, 1020–1033. [CrossRef]
13.
Wu, R.; Zhang, S.; Yang, L.; Wu, X.; Yang, Z.; Zhang, X.; Zhang, D.; Huang, J.; Wu, Y. Corporate average fuel consumption
evaluation and non-compliance disaggregation based on real-world data. Appl. Energy 2023,347, 121353. [CrossRef]
14. Fedeli, S.; Giuriato, L. Value added tax non-compliance in the car market. Fisc. Stud. 2023,44, 85–104. [CrossRef]
15.
Watson-Brown, N.; Truelove, V.; Senserrick, T. Self-Regulating compliance to enhance safe driving behaviours. Transp. Res. Part F
Traffic Psychol. Behav. 2024,105, 437–453. [CrossRef]
16.
Poulter, D.R.; Chapman, P.; Bibby, P.A.; Clarke, D.D.; Crundall, D. An application of the theory of planned behaviour to truck
driving behaviour and compliance with regulations. Accid. Anal. Prev. 2008,40, 2058–2064. [CrossRef]
17.
Zador, P.L.; Ahlin, E.M.; Rauch, W.J.; Howard, J.M.; Duncan, G.D. The effects of closer monitoring on driver compliance with
interlock restrictions. Accid. Anal. Prev. 2011,43, 1960–1967. [CrossRef] [PubMed]
18.
Freund, D.M. Foundations of Commercial Vehicle Safety: Laws, Regulations, and Standards; SAE International: Warrendale, PA, USA,
2007.
19.
McLachlan, S. How might Driver Licensing and Vehicle Registration evolve if we adopt Autonomous Cars and Digital Identifica-
tion? arXiv 2022, arXiv:2202.09861.
20.
Sankhe, S.; Malhotra, P.; Siddiqui, A.A.; Shaikh, A.R.; Mulla, R.; Khan, J. A Blockchain based solution to manage vehicle
documents using QR-Code. In Proceedings of the 2023 6th International Conference on Advances in Science and Technology
(ICAST), Mumbai, India, 8–9 December 2023; pp. 7–12.
21.
Ugwuanyi, D.; Martin, O.; Chukwuemeka, E. The revance of electronic governance and E-driving licence: The nexus. Arab. Group
J. 2017,6, 81–105. [CrossRef]
22.
Chowdhury, M.; Dey, K.; Apon, A. (Eds.) Data Analytics for Intelligent Transportation Systems; Elsevier: Amsterdam,
The Netherlands, 2024.
23.
Huang, Y.; Organ, B.; Zhou, J.L.; Surawski, N.C.; Hong, G.; Chan, E.F.C. Remote sensing of on-road vehicle emissions: Mechanism,
applications and a case study from Hong Kong. Atmos. Environ. 2018,182, 58–74. [CrossRef]
24.
Advanced Encryption Standard (AES). AES-256. Available online: https://www.nist.gov/publications/advanced-encryption-
standard-aes-0 (accessed on 1 March 2025).
25.
Ryandika, D.G.; Prabowo, W.A. Two-Stage Encryption for Strengthening Data Security in Web-Based Databases: AES-256
and RSA Integration. In Proceedings of the 2023 IEEE International Conference on Communication, Networks and Satellite
(COMNETSAT), Malang, Indonesia, 23–25 November 2023; pp. 486–492.
26.
Pérez, J.; Gutiérrez, M. Proximity-based smart card authentication for vehicle access control. IEEE Trans. Veh. Technol. 2021,70,
7563–7574.
27.
Mohammed, K.; Abdelhafid, M.; Kamal, K.; Ismail, N.; Ilias, A. Intelligent driver monitoring system: An Internet of Things-based
system for tracking and identifying the driving behavior. Comput. Stand. Interfaces 2023,84, 103704. [CrossRef]
28. Školc, G.; Markelj, B. Smart cars and information security. J. Crim. Justice Secur. 2018,2, 218–236.
29. Deichmann, J.; Klein, B.; Scherf, G.; Stützle, R. The Race for Cybersecurity: Protecting the Connected Car in the Era of New Regulation;
McKinsey Company: New York, NY, USA, 2019.
30.
ISO/SAE 21434:2021; Road Vehicles—Cybersecurity Engineering. International Organization for Standardization: Geneva,
Switzerland, 2021.
31.
Scalas, M.; Giacinto, G. Automotive cybersecurity: Foundations for next-generation vehicles. In Proceedings of the 2019 2nd
International Conference on new Trends in Computing Sciences (ICTCS), Amman, Jordan, 9–11 October 2019; pp. 1–6.

Processes 2025,13, 911 21 of 23
32.
Busold, C.; Taha, A.; Wachsmann, C.; Dmitrienko, A.; Seudié, H.; Sobhani, M.; Sadeghi, A.R. Smart keys for cyber-cars: Secure
smartphone-based NFC-enabled car immobilizer. In Proceedings of the third ACM conference on Data and application security
and privacy, New York, NY, USA, 18–20 February 2013; pp. 233–242.
33.
European Road Transport Research Advisory Council (ERTRAC). Report on Authentication Measures and Fleet Safety; ERTRAC:
Brussels, Belgium, 2023.
34.
Li, W.; Zhang, Y.; Wang, X. Automotive cybersecurity and smart card integration: A systematic review. J. Automot. Technol. 2022,
45, 245–260.
35.
Gurulian, I.; Shepherd, C.; Markantonakis, K.; Akram, R.N.; Mayes, K. When Theory and Reality Collide: Demystifying the Effec-
tiveness of Ambient Sensing for NFC-based Proximity Detection by Applying Relay Attack Data. arXiv 2016, arXiv:1605.00425.
36.
Sánchez-García, J.; García-Campos, J.M.; Reina, D.G.; Toral, S.L.; Barrero, F. On-siteDriverID: A secure authentication scheme
based on Spanish eID cards for vehicular ad hoc networks. Future Gener. Comput. Syst. 2016,64, 50–60. [CrossRef]
37.
Amaxilatis, D.; Tselios, C.; Akrivopoulos, O.; Chatzigiannakis, I. On the design of a Fog computing-based, driving behaviour
monitoring framework. arXiv 2020, arXiv:2010.09421.
38.
Wolf, M.; Weimerskirch, A.; Wollinger, T. State of the art: Embedding security in vehicles. EURASIP J. Embed. Syst. 2007,2007,
74706. [CrossRef]
39. Dodge, M.; Kitchin, R. The automatic management of drivers and driving spaces. Geoforum 2007,38, 264–275. [CrossRef]
40.
Dimple, B.; Chary, M.V.; Reddy, J.N.; Dileep, A. Authenticated access control for vehicle ignition system by smart card and
fingerprint technology. IOSR J. Electron. Commun. Eng. 2015,10, 45–48.
41.
Economic and Social Council. Protection of Motor Vehicles Against Unauthorized Use: Proposal for Amendments to Regulation
No. 116. ECE/TRANS/WP.29/GRSG/2010/8. 2010. Available online: https://digitallibrary.un.org/record/684996?v=pdf
(accessed on 1 March 2025).
42.
FMVSS 114. Theft Protection and Rollaway Prevention. Available online: https://www.ntea.com/NTEA/Member_benefits/
Technical_resources/FMVSSguide/FMVSS114.aspx (accessed on 1 March 2025).
43.
Haider, A.; Khan, A.H.; Denai, M. A Smart Wireless Car Ignition System for Vehicle Security. Advances in Automobile Engineering.
Adv. Automob. Eng. 2017,6, 1000169. [CrossRef]
44. Ravi, K.S.; Varun, G.H.; Vamsi, T.; Pratyusha, P. RFID based security system. Int. J. Innov. Technol. Explor. Eng. 2013,2, 132–134.
45.
Farooq, U.; ul Hasan, M.; Amar, M.; Hanif, A.; Asad, M.U. RFID based security and access control system. Int. J. Eng. Technol.
2014,6, 309. [CrossRef]
46.
Ramkumar, M. Fingerprint based driving licensing authentication system using FPGA implementation. Int. J. Eng. Sci. Res.
Technol. 2014,3, 2006–2009.
47.
Ashwin, S.; Loganathan, S.; Kumar, S.S.; Sivakumar, P. Prototype of a fingerprint based licensing system for driving. In
Proceedings of the 2013 International Conference on Information Communication and Embedded Systems (ICICES), Chennai,
India, 21–22 February 2013; pp. 974–987.
48.
Dhomne, S.; Bulkunde, P.; Lohakare, S.; Ukey, A.; Gajbiye, S.; Deolekar, S.; Shambarkar, N. Smart Ignition System in Automobile
Industries. IJIRT J. 2019,5, 410–416.
49.
Ali, A.M.; Awad, H.M.; Abdalgader, I.K. Authenticated Access Control for Vehicle Ignition System by Driver’s License and
Fingerprint Technology. In Proceedings of the 2020 International Conference on Computer, Control, Electrical, and Electronics
Engineering (ICCCEEE), Khartoum, Sudan, 26 February–1 March 2021; pp. 1–6.
50. Sushmitha, N.; Supriya, B.; Prajeeshan, R.A. Bio-metric automobile security. Int. J. Sci. Eng. Technol. Res. 2015,4, 3550–3554.
51.
Zhou, Y.; Tang, X.; Zhang, Y. A Hybrid Encryption Algorithm Based on RSA and AES for Wireless Sensor Networks. In
Proceedings of the 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), Xi’an, China, 17–19 August 2018;
pp. 136–141.
52.
Wu, Y.; Xiong, L.; Wang, C.; Xu, Y. Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles. Electronics 2024,
13, 3625. [CrossRef]
53.
Yahaya, A.S.; Javaid, N.; Zeadally, S.; Farooq, H. Blockchain based optimized data storage with secure communication for Internet
of Vehicles considering active, passive, and double spending attacks. Veh. Commun. 2022,37, 100502. [CrossRef]
54.
Ometov, A.; Bezzateev, S.; Mäkitalo, N.; Andreev, S.; Mikkonen, T.; Koucheryavy, Y. Multi-factor authentication: A survey.
Cryptography 2018,2, 1. [CrossRef]
55.
Ometov, A.; Bezzateev, S. Multi-factor authentication: A survey and challenges in V2X applications. In Proceedings of the 2017 9th
International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Munich, Germany,
6–8 November 2017; pp. 129–136.
56.
Burton, S.L. Advancing Cybersecurity: Strategic Insights Into Multifactor Authentication. Organ. Readiness Res. Secur. Manag.
Decis. Mak. 2025,8, 247–282.
57.
Zhang, K.; Ni, J.; Yang, K.; Liang, X.; Ren, J.; Shen, X. Security and Privacy in Smart City Applications: Challenges and Solutions.
IEEE Commun. Mag. 2017,55, 122–129. [CrossRef]

Processes 2025,13, 911 22 of 23
58.
Kebande, V.R.; Awaysheh, F.M.; Ikuesan, R.A.; Alawadi, S.A.; Alshehri, M.D. A blockchain-based multi-factor authentication
model for a cloud-enabled internet of vehicles. Sensors 2021,21, 6018. [CrossRef] [PubMed]
59.
Almadani, M.S.; Alotaibi, S.; Alsobhi, H.; Hussain, O.K.; Hussain, F.K. Blockchain-based multi-factor authentication: A systematic
literature review. Internet Things 2023,23, 100844. [CrossRef]
60.
Khan, S.K.; Shiwakoti, N.; Stasinopoulos, P.; Chen, Y.; Warren, M. Cybersecurity Framework for Connected and Automated
Vehicles: A Modeling Perspective. Transp. Policy 2025,162, 47–64. [CrossRef]
61.
Kifor, C.V.; Popescu, A. Automotive cybersecurity: A Survey on frameworks, standards, and testing and monitoring technologies.
Sensors 2024,24, 6139. [CrossRef] [PubMed]
62.
Zhang, H.; Pan, Y.; Lu, Z.; Wang, J.; Liu, Z. A cyber security evaluation framework for in-vehicle electrical control units. IEEE
Access 2021,9, 149690–149706. [CrossRef]
63.
Bushager, A.F. Smart Card Systems: Managing Risks and Modelling Security Protocols Using Systemc and Transaction Level
Modelling. Ph.D. Thesis, University of Southampton, Southampton, UK, 2011.
64. Bozdal, M.; Samie, M.; Aslam, S.; Jennions, I. Evaluation of can bus security challenges. Sensors 2020,20, 2364. [CrossRef]
65.
Schmittner, C.; Griessnig, G.; Ma, Z. Status of the Development of ISO/SAE 21434. In Proceedings of the Systems, Software and
Services Process Improvement: 25th European Conference, EuroSPI 2018, Bilbao, Spain, 5–7 September 2018; Proceedings 25.
Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 504–513.
66.
ISO 11898-1:2024; Road Vehicles—Controller Area Network (CAN)—Part 1: Data Link Layer and Physical Signalling. International
Organization for Standardization: Geneva, Switzerland, 2024.
67.
Benadjila, R.; Renard, M.; Elbaze, D.; Trébuchet, P. LEIA: The Lab Embedded ISO7816 Analyzer A Custom Smartcard Reader for
the ChipWhisperer. In Proceedings of the Symposium on Information and Communications Technology Security, Rennes, France,
5–7 June 2019.
68.
Tiberti, W.; Civino, R.; Gavioli, N.; Pugliese, M.; Santucci, F. A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communi-
cations. Appl. Sci. 2023,13, 13024. [CrossRef]
69.
ISO/IEC 7816-4:2020; Identification cards—Integrated Circuit Cards—Part 4: Organization, Security and Commands for Inter-
change. International Organization for Standardization: Geneva, Switzerland, 2020.
70.
Tuohy, S.; Glavin, M.; Hughes, C.; Jones, E.; Trivedi, M.; Kilmartin, L. Intra-Vehicle Networks: A Review. IEEE Trans. Intell. Transp.
Syst. 2015,16, 534–545. [CrossRef]
71.
Abdelhalim, A.; Bailey, L.; Dalphy, E.; Raboy, K. Data Enforced: An Exploratory Impact Analysis of Automated Speed Enforcement
in the District of Columbia. arXiv 2021, arXiv:2106.09933.
72.
ISO 16750-3:2023; Road vehicles—Environmental Conditions and Testing for Electrical and Electronic Equipment—Part 3:
Mechanical Loads. International Organization for Standardization: Geneva, Switzerland, 2023.
73.
ISO 16750-4:2023; Road vehicles—Environmental Conditions and Testing for Electrical and Electronic Equipment—Part 4:
Climatic Loads. International Organization for Standardization: Geneva, Switzerland, 2023.
74.
IEC 60068-2-6:2007; Environmental Testing—Part 2–6: Tests—Test Fc: Vibration (Sinusoidal). International Electrotechnical
Commission: London, UK, 2007.
75.
Dukarski, J. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles; SAE Technical Paper No.
EPR2021019; SAE International: Warrendale, PA, USA, 2021.
76.
Ahmad, U.; Han, M.; Jolfaei, A.; Jabbar, S.; Ibrar, M.; Erbad, A.; Song, H.H.; Alkhrijah, Y. A comprehensive survey and tutorial on
smart vehicles: Emerging technologies, security issues, and solutions using machine learning. IEEE Trans. Intell. Transp. Syst.
2024,25, 15314–15341. [CrossRef]
77.
Weber, J.; Weber, J. Primary or Customer Relevant Complete Vehicle Characteristics. Automot. Dev. Process. Process. Success. Cust.
Oriented Veh. Dev. 2009,7, 107–286.
78.
AIS-140; Intelligent Transportation Systems (ITS)—Requirements for Public Transport Vehicle Operation. Automotive Research
Association of India (ARAI): Pune, India, 2018.
79.
Dabaghchian, M.; Alipour-Fanid, A.; Zeng, K. Intelligent Policing Strategy for Traffic Violation Prevention. arXiv 2019,
arXiv:1909.09291.
80.
Popovi´c, V.M.; Damjanovi´c, M.Z.; Blagojevi´c, I.A.; Simovi´c, S.S. Technical regulations and conditions for importing passenger
vehicles on the territory of Southeast Europe. FME Trans. 2011,39, 105–116.
81.
Balasundaram, E.; Nedunchezhian, C.; Arumugam, M.; Asaikannu, V. Recent Advances in Intelligent Transportation Systems in
India: Analysis, Applications, Challenges, and Future Work. In Machine Intelligence, Big Data Analytics, and IoT in Image Processing:
Practical Applications; Wiley: Hoboken, NJ, USA, 2023; pp. 323–339.
82.
Das, H.S.; Rahman, M.M.; Li, S.; Tan, C.W. Electric vehicles standards, charging infrastructure, and impact on grid integration: A
technological review. Renew. Sustain. Energy Rev. 2020,120, 109618. [CrossRef]
83. Zheng, J.; Wang, Y. Advanced Polymer Applications in Automotive Electronics. J. Appl. Polym. Sci. 2023,140, 3456–3462.

Processes 2025,13, 911 23 of 23
84.
Kim, H.J.; Park, S. EMI Shielding in Automotive Electronics Using Nanocomposite Materials. Sens. Actuators A Phys. 2024,212,
117338.
85.
Lee, S.; Cho, K. Advances in Transparent Conductive Films for Automotive Applications. Adv. Mater. Interfaces 2022,9, 2200123.
86. Patel, R.; Gupta, V. Thermal Management Solutions for Automotive Electronics. J. Therm. Anal. Calorim. 2023,145, 987–995.
87.
Chen, Z.; Li, M. Encapsulation Techniques for Durable Automotive Electronics. IEEE Trans. Compon. Packag. Manuf. Technol. 2024,
14, 101–108.
88. Smith, T.J.; Zhang, Y. Exploring the Potential of Graphene in Automotive Electronics. Carbon 2023,201, 122–130.
89.
Kim, A.; Bogard, D.; Perlman, D.; Harrington, R. Review of Federal Motor Vehicle Safety Standards (FMVSS) for Automated Vehicles:
Identifying Potential Barriers and Challenges for the Certification of Automated Vehicles Using Existing FMVSS; No. DOT-VNTSC-OSTR-
16-03; John A. Volpe National Transportation Systems Center (US): Cambridge, MA, USA, 2016.
90.
Rathore, R.S.; Hewage, C.; Kaiwartya, O.; Lloret, J. In-vehicle communication cyber security: Challenges and solutions. Sensors
2022,22, 6679. [CrossRef] [PubMed]
91.
Lee, T.Y.; Lin, I.A.; Liao, R.H. Design of a FlexRay/Ethernet Gateway and Security Mechanism for In-Vehicle Networks. Sensors
2020,20, 641. [CrossRef] [PubMed]
92.
Khan, J.; Lim, D.W.; Kim, Y.S. Intrusion Detection System CAN-Bus In-Vehicle Networks Based on the Statistical Characteristics
of Attacks. Sensors 2023,23, 3554. [CrossRef] [PubMed]
93.
Bari, B.S.; Yelamarthi, K.; Ghafoor, S. Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning:
A Comparative Performance Study. Sensors 2023,23, 3610. [CrossRef]
94.
Gajdzik, B.; Awdziej, M.; Jaciow, M.; Lipowska, I.; Lipowski, M.; Szojda, G.; Tkaczyk, J.; Wolniak, R.; Wolny, R.; Grebski, W.W.
Encouraging Residents to Save Energy by Using Smart Transportation: Incorporating the Propensity to Save Energy into the
UTAUT Model. Energies 2024,17, 5341. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.