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1. 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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2. Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration

   Luo, Katie Z; Dao, Minh-Quan; Liu, Zhenzhen; Campbell, Mark; Chao, Wei-Lun; Weinberger, Kilian Q; Malis, Ezio; Fremont, Vincent; Hariharan, Bharath; Shan, Mao; et al (October 2025, IEEE)

   Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45.1k point clouds and 240.6k bounding boxes collected from three connected autonomous vehicles (CAVs) equipped with two different configurations of LiDAR sensors, plus a roadside unit with dual LiDARs. Our dataset provides point clouds and bounding box annotations across 10 classes, ensuring reliable data for perception training. We provide detailed statistical analysis on the quality of our dataset and extensively benchmark existing V2X methods on it. Mixed Signals is ready-to-use, with precise alignment and consistent annotations across time and viewpoints. We hope our work advances research in the emerging, impactful field of V2X perception. Dataset details at https://mixedsignalsdataset.cs.cornell.edu/. 

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3. The Role of Integrity Monitoring in Connected and Automated Vehicles: Current State of Practice and Future Directions

   https://doi.org/10.1109/MITS.2025.3589632  

   Nayak, Saswat Priyadarshi; Barth, Matthew (November 2025, IEEE Intelligent Transportation Systems Magazine)

   Positioning integrity refers to the trust in the performance of a navigation system. Accurate and reliable position information is needed to meet the requirements of connected and automated vehicle applications, particularly in safety-critical scenarios. Receiver autonomous integrity monitoring (IM) and its variants have been widely studied for global navigation satellite system-based vehicle positioning, often fused with kinematic (e.g., odometry) and perception sensors (e.g., cameras). However, IM for cooperative positioning solutions that leverage vehicle-to-everything (V2X) communication has received comparatively limited attention. This article reviews existing research in the field of positioning IM and identifies various research gaps. Particular attention has been placed on identifying research that highlights cooperative-IM methods. It also examines key automotive safety standards and public V2X datasets to map current research priorities and uncover critical gaps. Finally, the article outlines promising future directions, highlighting research topics aimed at advancing and benchmarking positioning integrity. 

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4. Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety

   https://doi.org/10.1177/03611981241252779  

   Chen, Hanlin; Bandaru, Vamsi K; Wang, Yilin; Romero, Mario A; Tarko, Andrew; Feng, Yiheng (December 2024, Transportation Research Record: Journal of the Transportation Research Board)

   Cooperative perception that integrates sensing capabilities from both infrastructure and vehicle perception sensors can greatly benefit the transportation system with respect to safety and data acquisition. In this study, we conduct a preliminary evaluation of such a system by integrating a portable lidar-based infrastructure detection system (namely, Traffic Scanner [TScan]) with a Society of Automotive Engineers (SAE) Level 4 connected and automated vehicle (CAV). Vehicle-to-everything (V2X) communication devices are installed on both the TScan and the CAV to enable real-time message transmission of detection results in the form of SAE J2735 basic safety messages. We validate the concept using a case study, which aims at improving CAV situation awareness and protecting vulnerable road user (VRU) safety. Field testing results demonstrate the safety benefits of cooperative perception from infrastructure sensors in detecting occluded VRUs and helping CAVs to plan safer (i.e., higher post-encroachment time) and smoother (i.e., lower deceleration rates) trajectories. 

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5. MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management

   Anjum, Abdullah; Rezaei, Abdolazim; Sookhak, Mehdi (September 2026, IEEE)

   Vehicle-to-Everything (V2X) communication systems are based on datasets that not only contain vehicle trajectory data but also wireless network parameters with a realistic level of fidelity, enabling the creation of prediction and optimization models. There is a very critical research infrastructure gap today, and publicly available datasets are likely to be limited to one of the two: mobility or network parameters, and rarely provide a single, integrated view that combines both. This paper introduces MINT-V2X, a comprehensive dataset generated by coupling SUMO traffic dynamics with OMNeT++/Simu5G network simulation. The validation framework is composed of 14 standardized tests based on 3GPP Release 14 (C-V2X), ETSI standards and Shannon capacity theory. The resulting dataset contains 9.87 million synchronized data points from 1,386 vehicles from 15 roadside units (RSUs) during 3 hours of urban traffic simulation. We demonstrate strict algorithmic consistency through network metric correlations (CQI-SINR: 0.993; SINRPDR: 0.946). Finally, we demonstrate the value of the dataset by conducting an RSU load prediction case study, showing that using trajectory data yields better predictive performance than network-history-only baselines. The dataset, experiments, and complete SUMO configuration files are available in the GitHub repository to facilitate reproduction on alternative simulation stacks. 

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