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Connected and Autonomous Vehicles: Trends, Applications and Security Challenges
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Connected and Autonomous Vehicles: Trends, Applications and Security Challenges

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 9037

Special Issue Editors

Dr. Francisco J. Aparicio-Navarro

E-Mail Website
Guest Editor
Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
Interests: cyber security; intrusion detection systems; network security; connected vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Asma Adnane

E-Mail Website
Guest Editor
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
Interests: cyber security; vehicular network; trust management
Dr. Hesam Jadidbonab

E-Mail Website
Guest Editor
Centre for Future Transport and Cities, Coventry University, Coventry CV1 5FB, UK
Interests: cyber security; automotive; calibration; electronic measurement and instrumentation
Dr. Ali Safaa Sadiq Al Shakarchi

E-Mail Website
Guest Editor
Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK
Interests: intelligent cybersecure systems; IoT; Internet of Vehicles IoV; optimisation algorithms for intelligent networks; trustworthy AI solutions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in sensing hardware, communication technology, and software development have driven the expansion of connected and autonomous vehicles (CAVs) as complex information systems that deliver a safe, reliable, and efficient driving experience. As technology continues to evolve, new trends and applications around artificial intelligence, blockchain, cloud, 5G/6G connectivity, digital twins, extended and augmented reality, and cyber security are shaping the future of the innovation of autonomous transportation. However, the rapid development of new technological advancement also creates novel challenges and emphasizes existing ones. Similarly, since sophisticated cyber threats continue to target intelligent transportation systems with significant destructive effects, the cyber security of connected and autonomous vehicles has also become an agenda item for academics, practitioners, and policy makers.

This Special Issue seeks original, high-quality submissions in the domain of connected and autonomous vehicles with a particular focus on trends, applications, and security challenges. Authors from academia, governments, and industry are welcome to propose and validate the use of new technological solutions, and to contribute new research results. Submissions focused on, but not limited to, one or more of the following topics of interest to this Special Issue are encouraged:

  • Communication technologies for CAVs;
  • 5G/6G implications on CAVs latency and reliability;
  • Advanced driver assistance systems;
  • Intelligent transportation infrastructure;
  • AI for real-time decision-making in CAVs;
  • Cyber security and privacy in CAVs;
  • Data acquisition and data sharing of CAVs;
  • Data-driven traffic flow and road safety optimization;
  • On-board and edge-assisted sensor data fusion;
  • CAVs and infrastructure cooperation;
  • Blockchain technology in CAVs;
  • Cloud, edge, and fog computing in CAVs;
  • Extended and augmented reality in CAVs;
  • Digital twin in CAVs development and operation;
  • Data governance and policies in CAVs;
  • Planning, simulation, execution, and validation of experiments;
  • Sensor fusion and vehicle-to-everything (V2X) communication.

Dr. Francisco J. Aparicio-Navarro
Dr. Asma Adnane
Dr. Hesam Jadidbonab
Dr. Ali Safaa Sadiq Al Shakarchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • connected vehicles
  • autonomous vehicles
  • cyber security

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Published Papers (5 papers)

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Research

20 pages, 1435 KiB  
Article
Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function
by Ujunwa Madububa Mbachu, Rabeea Fatima, Ahmed Sherif, Elbert Dockery, Mohamed Mahmoud, Maazen Alsabaan and Kasem Khalil
Sensors 2025, 25(5), 1629; https://doi.org/10.3390/s25051629 - 6 Mar 2025
Abstract
Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the [...] Read more.
Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5μs for 100 bits and rising to 39 μs for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 μs for 100 bits and rising to less than 8 μs for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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28 pages, 10511 KiB  
Article
Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles
by Marwa Ziadia, Sousso Kelouwani, Ali Amamou and Kodjo Agbossou
Sensors 2025, 25(4), 1175; https://doi.org/10.3390/s25041175 - 14 Feb 2025
Viewed by 339
Abstract
This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative [...] Read more.
This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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17 pages, 4402 KiB  
Article
Quality Evaluation for Colored Point Clouds Produced by Autonomous Vehicle Sensor Fusion Systems
by Colin Schaefer, Zeid Kootbally and Vinh Nguyen
Sensors 2025, 25(4), 1111; https://doi.org/10.3390/s25041111 - 12 Feb 2025
Viewed by 368
Abstract
Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding [...] Read more.
Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding environment and provide information for the detection and tracking of objects. Hence, evaluation methods are necessary to compare existing and future sensor systems through quantifiable measurements given the wide range of sensor models and design choices. This paper presents an evaluation method to compare colored point clouds, a common fused data type, among two LiDAR–camera fusion systems and a stereo camera setup. The evaluation approach uses a test artifact measured by the fusion system’s colored point cloud through the spread, area coverage, and color difference of the colored points within the computed space. The test results showed the evaluation approach was able to rank the sensor fusion systems based on its metrics and complement the experimental observations. The proposed evaluation methodology is, therefore, suitable towards the comparison of generated colored point clouds by sensor fusion systems. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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29 pages, 865 KiB  
Article
Adversarial Attacks on Intrusion Detection Systems in In-Vehicle Networks of Connected and Autonomous Vehicles
by Fatimah Aloraini, Amir Javed and Omer Rana
Sensors 2024, 24(12), 3848; https://doi.org/10.3390/s24123848 - 14 Jun 2024
Cited by 3 | Viewed by 2973
Abstract
Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting [...] Read more.
Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting focus from the common research on manipulating CAV perception models. Considering the relatively simple nature of IVN data, we assess the susceptibility of IVN-based IDSs to manipulation—a crucial examination, as adversarial attacks typically exploit complexity. We propose an adversarial attack method using a substitute IDS trained with data from the onboard diagnostic port. In conducting these attacks under black-box conditions while adhering to realistic IVN traffic constraints, our method seeks to deceive the IDS into misclassifying both normal-to-malicious and malicious-to-normal cases. Evaluations on two IDS models—a baseline IDS and a state-of-the-art model, MTH-IDS—demonstrated substantial vulnerability, decreasing the F1 scores from 95% to 38% and from 97% to 79%, respectively. Notably, inducing false alarms proved particularly effective as an adversarial strategy, undermining user trust in the defense mechanism. Despite the simplicity of IVN-based IDSs, our findings reveal critical vulnerabilities that could threaten vehicle safety and necessitate careful consideration in the development of IVN-based IDSs and in formulating responses to the IDSs’ alarms. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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29 pages, 591 KiB  
Article
Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle
by Yufeng Li, Wenqi Liu, Qi Liu, Xiangyu Zheng, Ke Sun and Chengjian Huang
Sensors 2024, 24(6), 1848; https://doi.org/10.3390/s24061848 - 13 Mar 2024
Cited by 9 | Viewed by 4313
Abstract
A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV’s safety and security [...] Read more.
A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV’s safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA’s abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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