A method and system for dynamically perceiving traffic risk of a fog area of an expressway

By combining an integrated air-space-ground three-dimensional perception network and a graph neural network model, real-time dynamic perception data of traffic risks in foggy areas along highways is acquired. This solves the problems of existing technologies being unable to accurately quantify the unevenness of fog distribution and lacking real-time traffic flow and fog dynamic coupling risk analysis. It achieves comprehensiveness and accuracy in fog risk perception and accuracy in the dynamic evolution of traffic risks.

CN122416712APending Publication Date: 2026-07-17AI SUPER EYE TECH CO LTD
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Patent Information

Application Number
CN202610298137.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-12
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies cannot accurately quantify the uneven distribution of fog on highways and lack real-time analysis of the dynamic coupling risks between traffic flow and fog, resulting in delayed risk warnings and crude control strategies.

Method used

Construct an integrated air-space-ground three-dimensional perception network to acquire multi-source perception data in real time. By fusing dynamic feature fields of fog and traffic flow data through a graph neural network model, generate lane-level dynamic intervention strategies.

Benefits of technology

It improves the comprehensiveness and accuracy of fog risk perception, and realizes the accuracy of dynamic evolution of traffic risks and the real-time nature of lane-level intervention strategies.

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Abstract

本发明公开了一种高速公路团雾区域的交通风险动态感知方法及系统，涉及智慧交通相关技术领域，方法包括：构建立体感知网络实时获取高速公路团雾区域的多源感知数据；提取团雾的能见度空间分布特征并量化团雾的不均匀性，生成团雾动态特征场；构建交通流‑团雾耦合的图神经网络模型，输出时空动态风险场；生成车道级的动态干预策略，并通过车路协同设施和路侧诱导装置执行。解决了现有技术中存在的无法精准量化团雾分布的不均匀性且缺乏对交通流与团雾动态耦合风险的实时分析，导致风险预警滞后、管控策略粗放的技术问题，达到了提升团雾风险感知的全面性与精准度、交通风险动态演化表征的准确性以及车道级干预策略的实时性的技术效果。
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