A driver fatigue state data determination method, device and computer equipment

By acquiring environmental and driver data for risk prediction and mutual information analysis, an information efficiency decay fatigue index is constructed, which solves the problem of insufficient adaptability to individual differences and environmental changes in traditional methods, and achieves efficient and stable detection of driver fatigue.

CN121196544BActive Publication Date: 2026-06-26GUANGDONG YUEYUN DEVELOPMENT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGDONG YUEYUN DEVELOPMENT CO LTD
Filing Date
2025-09-11
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Traditional methods for determining driver fatigue rely on fixed rules and limited scenarios, making it difficult to adapt to individual differences and environmental changes, resulting in insufficient generalization ability and stability.

Method used

By acquiring environmental status data and driver action data, risk event prediction and mutual information analysis are performed to construct an information efficiency decay fatigue index. Sequential statistical tests are introduced to achieve second-level online judgment and continuous updates.

Benefits of technology

It improves the generalization ability and stability of driver fatigue state determination, reduces equipment dependence, enhances driving safety and comfort, and improves the overall operational efficiency of the system.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to a driver fatigue state data determination method, device and computer equipment. The method comprises the following steps: acquiring environment state data corresponding to a running automobile and driver action data corresponding to a driver; performing prediction analysis on a risk event of the running automobile in a future preset time domain according to the environment state data, to obtain task complexity analysis data; performing mutual information analysis on a corresponding relationship between the environment state data and the driver action data, to obtain driver-vehicle mutual information analysis data; calculating a fatigue index of the driver at a current time according to the task complexity analysis data and the driver-vehicle mutual information analysis data, to obtain an information efficiency attenuation fatigue index; and performing sequential statistical test analysis on the information efficiency attenuation fatigue index, to obtain fatigue state data corresponding to the driver. The method can effectively improve the generalization ability and stability of the determination of the fatigue state of the driver.
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