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1. FAQ: A Fuzzy-Logic-Assisted Q Learning Model for Resource Allocation in 6G V2X

   https://doi.org/10.1109/JIOT.2023.3294279  

   Zhang, Minglong; Dou, Yi; Marojevic, Vuk; Chong, Peter_Han_Joo; Chan, Henry_C B (January 2024, IEEE Internet of Things Journal)

   This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the six generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee quality of service (QoS). Based on this observation, a novel fuzzy-logic-assisted Q learning model (FAQ) is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the Q-learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning, Q-learning and other advanced allocation strategies regarding the convergence speed and the network throughput. 

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2. DT-CoVeSS: Advancing NextG C-V2X Security Evaluation through High-Fidelity Digital Twinning

   https://doi.org/10.1109/INFOCOMWKSHPS65812.2025.11152971  

   Twardokus, Geoff; Rahbari, Hanif (May 2025, IEEE)

   In NextG smart transportation, connected vehicles (CVs) promise to enhance roadway safety and efficiency, but only if the underlying Cellular Vehicle-to-Everything (C-V2X) communications are secure and reliable. However, realistic evaluation of C-V2X security is extremely challenging, as simulators cannot fully capture the nuances of CV environments, while field tests are risky, expensive, and legally restricted. As a bridge between these restrictive options, in this paper we present a zero-risk, low-cost, and highly realistic Digital Twin for Connected Vehicle Safety and Security (DT-CoVeSS), featuring multiple advancements that bring the benefits of digital twins to sidelink C-V2X for the first time. These include (1) automated digital twin construction based on open-source geospatial data; (2) high-fidelity emulation of multipath signal propagation for these rich scattering environments, considering the specific waveform properties of C-V2X; (3) support for commercial C-V2X hardware "in the loop"' to study actual device behavior under various attacks without risking human safety; and (4) an open-source implementation of multi-layer C-V2X services, compliant with current standards like IEEE 1609.2 for security, to enable scalable, repeatable, and comprehensive testing. DT-CoVeSS facilitates realistic exploration of threats in the CV ecosystem; for example, we demonstrate its use to gain deeper insights into jamming efficacy against C-V2X. Moreover, we show our channel twinning even offers a 73% runtime advantage over alternative approaches while capturing factors like wireless scattering that alternatives do not consider, moving closer to real-time evaluation of real-world CV security. 

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3. V2X-Enabled Communication for Traffic Operations via ROS

   https://doi.org/10.1145/3722573.3727834  

   Ji, Junyi; Richardson, Alex; Sprinkle, Jonathan; Work, Daniel B (May 2025, ACM)

   Our work features Vehicle to Everything (V2X) communication integration via ROS to enable cooperative traffic operations between connected vehicles and smart infrastructure. By abstracting low-level vehicle-specific interfaces using ROS nodes, the system supports the interaction with traffic infrastructure. This allows upper-level applications, including computationally intensive tasks (e.g., generative AI), to process and respond to traffic conditions in real time. 

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4. A Co-Operative Perception System for Collision Avoidance Using C-V2X and Client–Server-Based Object Detection

   https://doi.org/10.3390/s25175544  

   Park, Jungme; Kavathekar, Vaibhavi; Bhuduri, Shubhang; Amin, Mohammad Hasan; Devaraj, Sriram Sanjeev (September 2025, Sensors)

   Martín-Sacristán, David; Garcia-Roger, David (Ed.)

   With the recent 5G communication technology deployment, Cellular Vehicle-to-Everything (C-V2X) significantly enhances road safety by enabling real-time exchange of critical traffic information among vehicles, pedestrians, infrastructure, and networks. However, further research is required to address real-time application latency and communication reliability challenges. This paper explores integrating cutting-edge C-V2X technology with environmental perception systems to enhance safety at intersections and crosswalks. We propose a multi-module architecture combining C-V2X with state-of-the-art perception technologies, GPS mapping methods, and the client–server module to develop a co-operative perception system for collision avoidance. The proposed system includes the following: (1) a hardware setup for C-V2X communication; (2) an advanced object detection module leveraging Deep Neural Networks (DNNs); (3) a client–server-based co-operative object detection framework to overcome computational limitations of edge computing devices; and (4) a module for mapping GPS coordinates of detected objects, enabling accurate and actionable GPS data for collision avoidance—even for detected objects not equipped with C-V2X devices. The proposed system was evaluated through real-time experiments at the GMMRC testing track at Kettering University. Results demonstrate that the proposed system enhances safety by broadcasting critical obstacle information with an average latency of 9.24 milliseconds, allowing for rapid situational awareness. Furthermore, the proposed system accurately provides GPS coordinates for detected obstacles, which is essential for effective collision avoidance. The technology integration in the proposed system offers high data rates, low latency, and reliable communication, which are key features that make it highly suitable for C-V2X-based applications. 

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5. Edge-Assisted Collaborative Perception in Autonomous Driving: A Reflection on Communication Design

   https://doi.org/10.1145/3453142.3491413  

   Yu, Ruozhou; Yang, Dejun; Zhang, Hao (December 2021, ACM/IEEE Symposium on Edge Computing (SEC) Workshops)

   Collaborative perception enables autonomous driving vehicles to share sensing or perception data via broadcast-based vehicle-to-everything (V2X) communication technologies such as Cellular-V2X (C-V2X), hoping to enable accurate perception in face of inaccurate perception results by each individual vehicle. Nevertheless, the V2X communication channel remains a significant bottleneck to the performance and usefulness of collaborative perception due to limited bandwidth and ad hoc communication scheduling. In this paper, we explore challenges and design choices for V2X-based collaborative perception, and propose an architecture that lever-ages the power of edge computing such as road-side units for central communication scheduling. Using NS-3 simulations, we show the performance gap between distributed and centralized C-V2X scheduling in terms of achievable throughput and communication efficiency, and explore scenarios where edge assistance is beneficial or even necessary for collaborative perception. 

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