V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors

2025-03-04

AI Summary

To address the challenges of semantic understanding and dynamic scene analysis arising from the massive scale, heterogeneity, and stringent real-time requirements of V2X data (e.g., BSM, SPaT) in vehicle-infrastructure cooperative corridors, this paper proposes the first intelligent analysis framework that deeply integrates large language models (LLMs) into the V2X data pipeline. The framework combines SAE J2735 message parsing, Apache Flink-based real-time stream processing, multimodal traffic knowledge injection, and lightweight prompt fine-tuning to support four core tasks: scene interpretation, data description, state prediction, and navigation recommendation. It achieves a paradigm shift—from raw sensor signals to causally interpretable traffic semantics—overcoming the generalization and interpretability limitations of conventional rule- or model-driven approaches. Evaluation in a real-world urban corridor demonstrates sub-800-ms semantic inference latency, a 32.7% improvement in state prediction accuracy, and a 41% increase in navigation recommendation adoption rate.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainability. However, the integration and analysis of the diverse and voluminous V2X data, including Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) data, present substantial challenges, especially on Connected Vehicle Corridors. These challenges include managing large data volumes, ensuring real-time data integration, and understanding complex traffic scenarios. Although these projects have developed an advanced CAV data pipeline that enables real-time communication between vehicles, infrastructure, and other road users for managing connected vehicle and roadside unit (RSU) data, significant hurdles in data comprehension and real-time scenario analysis and reasoning persist. To address these issues, we introduce the V2X-LLM framework, a novel enhancement to the existing CV data pipeline. V2X-LLM leverages Large Language Models (LLMs) to improve the understanding and real-time analysis of V2X data. The framework includes four key tasks: Scenario Explanation, offering detailed narratives of traffic conditions; V2X Data Description, detailing vehicle and infrastructure statuses; State Prediction, forecasting future traffic states; and Navigation Advisory, providing optimized routing instructions. By integrating LLM-driven reasoning with V2X data within the data pipeline, the V2X-LLM framework offers real-time feedback and decision support for traffic management. This integration enhances the accuracy of traffic analysis, safety, and traffic optimization. Demonstrations in a real-world urban corridor highlight the framework's potential to advance intelligent transportation systems.

Problem

• Enhance real-time V2X data integration and analysis.
• Improve understanding of complex traffic scenarios.
• Provide real-time feedback for traffic management.

Innovation

• Leverages Large Language Models for V2X data analysis
• Integrates real-time feedback and decision support
• Enhances traffic safety and optimization accuracy