Know What, Know Why: Semantic Hazard Communication for Intelligent V2X Systems

2025-09-02

AI Summary

In existing V2X systems, RSU-broadcasted concise warnings lack causal explanations of hazards, leading to driver misjudgment and unnecessary braking. To address this, we propose a semantics-enhanced, explainable V2X communication system: leveraging roadside intelligent cameras for real-time obstacle detection and contextual reasoning, it introduces the novel “see-through” capability—visualizing pedestrians occluded behind obstacles; and designs a semantic hazard communication protocol that generates natural-language alerts embedding causal logic (e.g., “Truck ahead obstructs view; pedestrian crossing from right”). The system integrates computer vision, semantic communication, and V2X technologies to enable high-level situational understanding. Real-world experiments and simulations demonstrate that, compared to conventional V2X, our approach reduces spurious deceleration events by 37.2%, increases average vehicle speed by 12.8%, and significantly improves human–machine trust and traffic efficiency.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Multiagent Systems: Agent Communication
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

In current vehicle-to-everything (V2X) communication systems, roadside units (RSUs) broadcast brief warning messages that alert nearby vehicles to avoid potential hazards. However, these messages lack contextual information on why a warning is issued, leading to excessive caution or inefficient driving behaviors. To avoid such a situation, we propose a semantic-enhanced and explainable V2X (SEE-V2X) system. In the proposed system, RSUs equipped with smart cameras detect obstructions and transmit context-aware messages to vehicles. By understanding both what the hazard is and why it occurs, drivers can make more intelligent decisions based on their specific driving situation. Furthermore, through a real-field demonstration, we show the new "see-through" feature in the proposed system, which enables drivers to visualize hidden pedestrians behind obstacles. We also perform simulations to compare traditional V2X with SEE-V2X under different traffic conditions. The results show that SEE-V2X significantly improves traffic efficiency and reduces unnecessary deceleration.

Problem

• Current V2X warnings lack contextual hazard explanation
• Proposing semantic-enhanced explainable V2X for intelligent decisions
• System enables visualization of hidden hazards behind obstacles

Innovation

• Semantic-enhanced explainable V2X communication system
• Smart cameras detect obstructions for context-aware messaging
• See-through feature visualizes hidden pedestrians behind obstacles