Feature articles – March 2026
Our first monthly feature paper is an invited tutorial paper co-authored by industry practitioners from Qualcomm Technologies Inc., which describes the 5G New Radio Vehicle-to-Everything (5G NR-V2X) air interface, with a specific focus on the features and capabilities introduced in 3GPP Release 16. Complementing the invited paper is a feature article coauthored by researchers from Beijing Institute of Technology and Tsinghua University, which proposed a risk assessment and motion planning framework for safe operation of automated vehicles in complex traffic scenarios.
We’ve provided short summaries of these feature articles, written in accessible language that we hope will make your reading experience enjoyable.
A Tutorial on 5G NR-V2X: Enhancements, Real-World Applications, and Performance Evaluation
Abolfazl Hajisami; Ralf Weber; Jim Misener; Ahmed Farhan Hanif
Published in Volume 7, IEEE Open Journal of Vehicular Technology
Read on IEEE Xplore
Summary contributed by Abolfazl Hajisami (author):
This tutorial explains how 5G NR-V2X—the latest evolution of vehicle-to-everything communication—enables cars to “talk” directly to each other and to roadside infrastructure with unprecedented reliability. Unlike previous technologies, 5G NR-V2X introduces advanced features such as connectionless groupcast and flexible resource allocation. These enhancements are purpose-built to support complex interactions such as maneuvers like cooperative lane merging and intersection coordination. The tutorial not only summarizes these technical enhancements but also shows, through realistic simulations, that 5G NR-V2X can achieve highly reliable message delivery even in dense traffic and high-speed scenarios. This capability is a critical step toward safer roads, 5G V2X as an ADAS sensor and eventually expedite and enhance cooperative automated driving.
An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution
Zhiqiang Zhang; Lei Zhang; Mingqiang Wang; Cong Wang; Zhenpo Wang
Published in Volume 5, IEEE Open Journal of Vehicular Technology
Read on IEEE Xplore
Summary contributed by Hei Cheng:
In complex traffic environments, automated vehicles must make safe and efficient driving decisions while continuously interacting with surrounding vehicles, whose behaviors are inherently uncertain. This uncertainty makes it difficult for traditional motion planning methods based on single trajectory predictions to fully capture real driving risks. To address this challenge, this work integrates vehicle interaction modeling, trajectory uncertainty prediction, and risk assessment into a unified framework. By predicting the probabilistic distribution of surrounding vehicles’ future motions, uncertainty is explicitly incorporated into the decision-making process and transformed into a spatiotemporal risk representation. The resulting dynamic risk map enables the vehicle to anticipate potentially hazardous regions and select more reasonable driving actions among multiple alternatives, achieving a better balance between safety and efficiency in complex traffic scenarios.
Open Call for Editors
The IEEE Open Journal of Vehicular Technology (OJVT) is calling for editors in any research areas that belong to the below-mentioned scopes:
(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.
If you are interested in serving as an editor in these areas, please send your resume with the contact of two references for consideration to the Editor in Chief, Dr. Edward Au, no later than 1 May 2026.