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1. Denial-of-Service Attacks on C-V2X Networks

   Trkulja, Nataša; Starobinski, David; Berry, Randall (February 2021, Third International Workshop on Automotive and Autonomous Vehicle Security (AutoSec) 2021)

   null (Ed.)

   Cellular Vehicle-to-Everything (C-V2X) networks are increasingly adopted by automotive original equipment manufacturers (OEMs). C-V2X, as defined in 3GPP Release 14 Mode 4, allows vehicles to self-manage the network in absence of a cellular base-station. Since C-V2X networks convey safety-critical messages, it is crucial to assess their security posture. This work contributes a novel set of Denial-of-Service (DoS) attacks on C-V2X networks operating in Mode 4. The attacks are caused by adversarial resource block selection and vary in sophistication and efficiency. In particular, we consider "oblivious" adversaries that ignore recent transmission activity on resource blocks, "smart" adversaries that do monitor activity on each resource block, and "cooperative" adversaries that work together to ensure they attack different targets. We analyze and simulate these attacks to showcase their effectiveness. Assuming a fixed number of attackers, we show that at low vehicle density, smart and cooperative attacks can significantly impact network performance, while at high vehicle density, oblivious attacks are almost as effective as the more sophisticated attacks. 

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2. 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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3. Efficient Integrity Monitoring for KF-based Localization

   https://doi.org/10.1109/ICRA.2019.8794362  

   Arana, Guillermo Duenas; Joerger, Mathieu; Spenko, Matthew (May 2019, 2019 International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety. 

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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. 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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