Abstract

This project advances connected vehicle applications by developing and testing an enhanced RampCast system, a comprehensive traffic management system using C-V2X technology for Indiana highways. The system features a dual-mode architecture integrating both short-range (PC5) and long-range cellular (Uu) C-V2X communication pathways, utilizing commercial-grade Cohda MK6 hardware and adhering to SAE J2735 standards to ensure interoperability. A key innovation is the integration of an AI-based prioritization framework, which leverages a large language model to enhance the contextual relevance of traffic messages. This AI system introduces two intelligent agents: one to dynamically estimate the appropriate display distance for an event based on its severity, and another to prioritize the order of messages based on urgency and potential driver impact. Field tests conducted on I-65 and I-70 in Indianapolis validated the system's hybrid design. Results confirmed that the PC5 link provides very low latency (around 25 ms), ideal for time-critical alerts, while the Uu link ensures highly reliable coverage in complex environments, albeit with higher latency (around 45 ms). The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver. These findings support a hybrid C-V2X architecture as a robust model for future smart highway deployments.

Keywords

C-V2X, connected vehicle, geocast, AI decision engine, location-aware prioritization, context-aware geofencing

Report Number

FHWA/IN/JTRP-2026/11

SPR Number

4934

Performing Organization

Joint Transportation Research Program

Publisher Place

West Lafayette, Indiana

Date of Version

2026

DOI

10.5703/1288284318619

Additional Files
SPR-4934 Technical Summary.pdf (1789 kB)
SPR-4934 Technical Summary
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