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Sensing, Communication, and Security for Connected and Autonomous Vehicles

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

Deadline for manuscript submissions: 25 September 2026 | Viewed by 3097

Special Issue Editors


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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

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Guest Editor
Department of Computer Science, Nottingham Trent University, Clifton Lane NG11 8NS, Nottingham, 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

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Guest Editor
Centre for Future Transport and Cities, Coventry University, Coventry CV1 5FB, UK
Interests: cyber security; automotive; calibration; electronic measurement and instrumentation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
Interests: cyber security; vehicular network; trust management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in sensing hardware, communication technologies, and software development have accelerated the evolution of connected and autonomous vehicles (CAVs) into complex information systems designed to deliver safer, more reliable, and more efficient transportation. Emerging trends in artificial intelligence, blockchain, cloud and edge computing, 5G/6G connectivity, digital twins, extended/augmented reality, and cybersecurity are shaping the next generation of autonomous mobility.

However, the rapid adoption of these technologies also introduces new challenges while intensifying existing ones. In particular, the rise in sophisticated cyber threats against intelligent transportation systems underscores the urgent need for secure, resilient, and trustworthy CAV infrastructures. This has made cybersecurity a central concern for researchers, industry practitioners, and policymakers.

This Special Issue invites original, high-quality contributions addressing the latest trends, applications, and security challenges in connected and autonomous vehicles. Submissions from academia, industry, and government are encouraged, encompassing both theoretical advancements and applied research validated through real-world scenarios.

Topics of interest include, but are not limited to, the following:

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

Dr. Francisco J. Aparicio-Navarro
Dr. Ali Safaa Sadiq Al Shakarchi
Dr. Hesam Jadidbonab
Dr. Asma Adnane
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 250 words) can be sent to the Editorial Office for assessment.

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

  • sensing
  • communication
  • security 
  • connected and autonomous vehicle

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Published Papers (2 papers)

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Review

48 pages, 5585 KB  
Review
Sensors in Self-Driving Vehicles: A Detailed Literature Review and New Trends
by Patrik Viktor and Gabor Kiss
Sensors 2026, 26(7), 2153; https://doi.org/10.3390/s26072153 - 31 Mar 2026
Viewed by 2100
Abstract
Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research [...] Read more.
Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving. Full article
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28 pages, 2970 KB  
Review
Securing Data in Vehicles: Privacy-Preserving Frameworks for Dynamic CAV Environments
by Rahma Hammedi, David J. Brown, Omprakash Kaiwartya and Pramod Gaur
Sensors 2026, 26(4), 1326; https://doi.org/10.3390/s26041326 - 19 Feb 2026
Viewed by 528
Abstract
Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative [...] Read more.
Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems. Full article
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