A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors

2024-10-01

arXiv.org

AI Summary

This study addresses key challenges in V2X corridor digital twin systems—namely, difficulty integrating legacy infrastructure, poor fusion of heterogeneous multi-source data, insufficient real-time performance, and latency in cyber-physical synchronization. To this end, we propose the first full-element (vehicle, road, communication, signal, traffic flow) digital twin framework tailored to real-world C-V2X vehicle-infrastructure cooperative corridors. Our approach innovatively integrates C-V2X communications, edge-cloud collaborative computing, real-time data pipelines, multimodal simulation, and high-precision time synchronization to enable high-fidelity, low-latency closed-loop feedback between physical and digital entities. Experimental evaluation demonstrates a 32% improvement in traffic situation awareness accuracy and end-to-end latency under 200 ms. The framework successfully supports dynamic signal optimization, cooperative hazard warnings, and real-time event dissemination—marking the first large-scale validation of digital twin deployment feasibility and operational efficacy within traffic cyber-physical systems (T-CPS).

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, communications, and more remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the proposed DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT's scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Furthermore, its ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the reliability and safety of transportation systems.

Problem

• Integration of physical-virtual systems in V2X corridors.
• Challenges in real-time data processing and synchronization.
• Enhancing traffic safety and mobility through Digital Twins.

Innovation

• Digital Twin for V2X integration
• Real-time data synchronization
• Scalable C-V2X architecture