A method for level classification of fatigue state of a driver

A driver fatigue and classification method technology, applied in the field of driver fatigue state level classification, can solve the problems of low accuracy, high false alarm rate, poor real-time performance, etc., to reduce error transmission, improve interpretation rationality, and overcome model adaptation. Effects of Sexual Expansion Questions

Active Publication Date: 2019-01-04
DALIAN UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problems of high false alarm rate, low accuracy and poor real-time performance in t

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  • A method for level classification of fatigue state of a driver
  • A method for level classification of fatigue state of a driver
  • A method for level classification of fatigue state of a driver

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[0045] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. It should be understood that the specific embodiments described here are only used to understand the present invention, but not used to limit the present invention.

[0046] Step A: Obtain the personal attribute information of the driver, and collect the EEG signals of the corresponding driver in different fatigue states;

[0047] The driver’s personal attribute information includes gender, age, and driving experience;

[0048] The EEG signal collection frequency is set to 100Hz, and the EEG instrument used to collect EEG signals should ensure that the impact on the driver’s working environment is as small as possible;

[0049] Step B: Preprocess the acquired personal attribute information of the dr...

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Abstract

The invention discloses a driver fatigue state level classification method, belonging to the technical field of automobile safety driving assistance. The invention accurately classifies the level of fatigue state on the basis of considering the dynamic generation characteristics of driving fatigue, and has an important practical support function for related technical research and vehicle-mounted system development. This method uses reliable EEG data as the data source of fatigue level classification to reduce the error transmission caused by indirect detection and subjective evaluation methods. The level classification model based on LSTM network solves the problem of dynamic fatigue generation from time series. Attention mechanism is introduced into the model to analyze the differences ofthe characteristics under different fatigue levels, so as to improve the rationality of the model's interpretation of fatigue evolution law. The modeling method synthesizing the characteristics of different people overcomes the problem of model adaptability expansion caused by the difference of driver style types.

Description

technical field [0001] The invention belongs to the technical field of automobile safety assisted driving, and in particular relates to a driver fatigue state level grading method. Background technique [0002] With the increase of the per capita car ownership in our country, traffic accidents have become another major problem threatening human life. According to statistics, driver fatigue driving accounts for the largest proportion in all serious road traffic accidents. If the driver is warned 0.5 seconds before the danger occurs, most similar traffic accidents can be reduced. At present, the detection methods of the driver's fatigue state can be divided into the following two categories: (1) detection methods based on objective indicators: mainly divided into direct detection and indirect detection. Direct detection usually refers to EEG signal detection. This detection method can directly reflect the driver's brain activity state, and its index changes are directly relat...

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Application Information

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IPC IPC(8): A61B5/0476A61B5/18G06K9/00G06N3/06
CPCA61B5/165A61B5/18A61B5/4884G06N3/061A61B5/7203A61B2503/22A61B5/369G06V20/597
Inventor 张明恒陈冉翟晓娟方超李佳栗
Owner DALIAN UNIV OF TECH
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