A virtual lane line dynamic regulation method for intelligent vehicle-road interaction

By using a virtual lane line dynamic control method based on intelligent vehicle-road interaction, the lane layout is dynamically optimized, solving the problem of road fatigue damage caused by traditional lane lines and realizing proactive road health management and safety improvement.

CN122369256APending Publication Date: 2026-07-10HEFEI UNIV OF TECH +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEFEI UNIV OF TECH
Filing Date
2026-03-10
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Traditional physical lane markings cause repeated concentrated traffic loads in wheel track areas, which accelerate the accumulation of fatigue damage to pavement materials. Existing technologies have failed to effectively prevent the uneven development of damage.

Method used

Through intelligent vehicle-road interaction, a traffic flow distribution-road damage coupling prediction model is used to dynamically optimize lane layout, proactively allocate traffic loads from structurally weak areas to robust areas, establish an overall damage risk model, and achieve dynamic control of virtual lane lines.

Benefits of technology

It significantly extends the road overhaul cycle, reduces the total life-cycle maintenance cost, prevents sudden road safety hazards caused by localized loss of load-bearing capacity, and improves the road's durability and inherent safety level.

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Abstract

This invention discloses a method for dynamic control of virtual lane lines for intelligent vehicle-road interaction, relating to the field of lane line dynamic adjustment technology. The method includes: obtaining pavement deflection data for each lane of a target road segment using a non-destructive lane detection method, and calculating the relative damage index for each lateral position of each lane; establishing a load distribution model to calculate the load pressure exerted by traffic flow on each point of the pavement; establishing an overall damage risk model after virtual centerline offset, and solving for the optimal overall offset; determining the final virtual centerline position of each lane based on the optimal offset, and propagating the optimized lanes to intelligent driving vehicles in the target road segment; and actively and dynamically allocating traffic loads from structurally weak areas to robust areas.
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