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In order to support the increasing number of submissions in the areas of motor vehicles and land transportation, especially related to:
- Components and systems and motive power for propulsion and auxiliary functions; and
- Sensing and perception as well as multimodal and hyperspectral Imaging for automotive applications
IEEE OJVT is looking for at least 2 researchers to join the Editorial Board starting 1 January 2026.
Please send your resume to Dr. Edward Au, Editor-in-Chief, no later than 1 September 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.
This month, we highlight a survey paper, co-authored by industry researchers at NVIDIA and academic researchers at Shanghai Jiao Tong University, that examines various aspects of graph neural backdoors, including the technical foundations, existing attack mechanisms and corresponding defense strategies, potential benign applications of this backdoor technology, as well as possible future research directions.
Also featured is a paper from Brunel University of London and Civil Aviation University of China, on the design of a novel ground-connected approach that optimizes the site selection and evaluation model for the deployment of electric Vertical Take-off and Landing (eVTOL) aircraft.
We’ve provided short summaries of these feature articles, written in accessible language that we hope will make your reading experience enjoyable.
Exploring Graph Neural Backdoors in Vehicular Networks: Fundamentals, Methodologies, Applications, and Future Perspectives
Xiao Yang, Gaolei Li, Kai Zhou, Jianhua Li, Xingqin Lin, and Yuchen Liu
Full article: IEEE Open Journal of Vehicular Technology, Volume 6
Summary by Gaolei Li: The widespread adoption of Graph Neural Networks (GNNs) in Vehicular Networks (VNs) has created a critical need for robust security frameworks to protect these systems in increasingly complex transportation ecosystems. This survey investigates the emerging threat of backdoor attacks in GNN-based VN applications, analyzing attack paradigms such as training data poisoning and adversarial trigger injection that lead to malicious model behaviors (e.g., erroneous traffic predictions or compromised vehicle coordination). We present a systematic taxonomy of these vulnerabilities and demonstrate how they exploit the structural and temporal dependencies in vehicular graph data. Our work further examines these security challenges within the context of next-generation intelligent transportation systems, where GNNs power critical functions ranging from traffic signal optimization to autonomous fleet coordination. By comprehensively assessing attack patterns and defense mechanisms, this survey provides new insights into securing graph-based vehicular systems, whereby we offer a unique perspective on protecting spatiotemporal network architectures against evolving threats.
Optimizing Urban Air Mobility: A Ground-Connected Approach to Select Optimal eVTOL Takeoff and Landing Sites for Short-Distance Intercity Travel
Yantao Wang, Jiashuai Li, Yujie Yuan, and Chun Sing Lai
Full article: IEEE Open Journal of Vehicular Technology, Volume 6
Summary by Chun Sing Lai: This work studied how electric air taxis (eVTOLs) could help improve short-distance travel within and between cities. The goal is to reduce traffic jams and cut pollution from cars. To make these flying taxis useful, we choose the right locations for them to take off and land. we developed a method to find the best locations by looking at how they connect to existing roads and public transport. First, we analyzed traffic patterns, economic activity, and travel habits to figure out where eVTOL stops would be most helpful. We used a smart sorting technique to group the best locations, ensuring they ease traffic congestion and follow air travel rules. Then, we investigated if these locations would actually improve transportation using a demand model. When tested in the Beijing-Tianjin-Xiong’an area, the model found six ideal takeoff and landing spots. These locations could handle over 75,000 trips, helping to reduce pressure on the roads.
About the IEEE Open Journal of Vehicular Technology (OJVT)
The IEEE OJVT covers the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation. A brief summary of these fields of interest are as follows:
- Mobile radio shall include all terrestrial mobile services
- Motor vehicles shall include the components and systems and motive power for propulsion and auxiliary functions
- Land transportation shall include the components and systems used in both automated and non-automated facets of ground transport technology
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