Performance Evaluation of V2X Communication Using Large-Scale Traffic Data

2026-02-06

AI Summary

This study addresses the limitations of existing V2X communication performance evaluations, which predominantly rely on synthetic traffic data and fail to capture real-world dynamics. For the first time, the authors construct a large-scale simulation environment based on real-world trajectory datasets—HighD and InD—encompassing over 100,000 vehicles, and integrate a standard V2X protocol stack to systematically assess message-level performance metrics, including packet delivery ratio and channel occupancy, in both highway and urban intersection scenarios. The analysis reveals the nuanced impacts of traffic density, mobility patterns, and communication range on V2X performance, demonstrating that channel congestion under real traffic conditions is significantly lower than predictions from synthetic data. Furthermore, the study validates the scalability of cooperative perception services in high-density traffic environments.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Vehicular communication (V2X) technologies are widely regarded as a cornerstone for cooperative and automated driving, yet their large-scale real-world deployment remains limited. As a result, understanding V2X performance under realistic, full-scale traffic conditions continues to be relevant. Most existing performance evaluations rely on synthetic traffic scenarios generated by simulators, which, while useful, may not fully capture the features of real-world traffic. In this paper, we present a large-scale, data-driven evaluation of V2X communication performance using real-world traffic datasets. Vehicle trajectories derived from the Highway Drone (HighD) and Intersection Drone (InD) datasets are converted into simulation-ready formats and coupled with a standardized V2X networking stack to enable message-level performance analysis for entire traffic populations comprising over hundred thousands vehicles across multiple locations. We evaluate key V2X performance indicators, including inter-generation gap, inter-packet gap, packet delivery ratio, and channel busy ratio, across both highway and urban intersection environments. Our results show that cooperative awareness services remain feasible at scale under realistic traffic conditions. In addition, the findings highlight how traffic density, mobility patterns, and communication range influence V2X performance and how synthetic traffic assumptions may overestimate channel congestion.

Problem

• V2X communication
• large-scale traffic
• performance evaluation
• real-world traffic data
• cooperative driving

Innovation

• data-driven evaluation
• real-world traffic data
• V2X performance analysis
• large-scale simulation
• cooperative awareness