Feature articles – March 2025
Our journal welcomes not only original high-quality papers covering the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation, but also industry-focused publication focusing on research findings and suggesting ideas that may be useful to those conducting similar research.
In the following, let us provide you with a one-stop destination to know about a recently published article from our industry authors who propose a novel approach in improving safety verification for evasive obstacle avoidance in autonomous vehicles, as well as an article from highly cited researchers on the use of a novel data-oriented approach to clusters of IoT devices. We hope that the short summary of these feature articles written in layman language may make your reading a pleasure!
High-Resolution Safety Verification for Evasive Obstacle Avoidance in Autonomous Vehicles
Authors: Aliasghar Arab, Milad Khaleghi, Alireza Partovi, Alireza Abbaspour, Chaitanya Shinde, Yashar Mousavi, Vahid Azimi, Ali Karimmoddini
Published in volume 6, IEEE Open Journal of Vehicular Technology
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Summary contributed by Aliasghar Arab (Author):
Autonomous vehicles must undergo rigorous safety verification to ensure they can execute evasive maneuvers in response to unexpected obstacles. Our latest research introduces a high-resolution safety verification framework specifically designed to assess automated driving features in edge-case scenarios, such as evasive obstacle avoidance. Inspired by ISO 26262 Hazard Analysis and Risk Assessment (HARA), this method enhances the granularity of evaluating minimum risk maneuvers, addressing critical gaps in safety verification for complex driving situations.
Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques
Authors: Chenrui Sun, Gianluca Fontanesi, Berk Canberk, Amirhossein Mohajerzadeh, Symeon Chatzinotas, David Grace, Hamed Ahmadi
Published in volume 5, IEEE Open Journal of Vehicular Technology
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Summary contributed by Hamed Ahmadi (Author):
The rapid proliferation of Unmanned Aerial Vehicles (UAVs) in diverse applications has created an urgent need for advanced technological solutions to enhance their capabilities in complex, dynamic environments. This survey explores the application of cutting-edge machine learning (ML) techniques in addressing these challenges, examining approaches such as meta-learning, explainable AI, and federated learning. We provide a detailed classification of these methodologies, demonstrating how they enhance UAV autonomy, adaptability, and decision-making. We also examine the integration of these advanced ML techniques within the context of 6G networks, where UAVs serve as mobile base stations, relays, and infrastructure components. By incorporating modern ML approaches, UAVs can expand their potential applications, enabling deeper integration into wireless communication systems, which offers a fresh perspective on the evolving landscape of UAV communication and the potential for future advancements.
About IEEE Open Journal of Vehicular Technology (OJVT)
The IEEE Open Journal of Vehicular Technology covers the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation. (a) Mobile radio shall include all terrestrial mobile services. (b) Motor vehicles shall include the components and systems and motive power for propulsion and auxiliary functions. (c) Land transportation shall include the components and systems used in both automated and non-automated facets of ground transport technology.