Fatigue driving detection method based on multi-modal information fusion

Abstract

The invention discloses a fatigue driving detection method based on multi-modal information fusion. The method comprises the steps: collecting electroencephalogram signals, facial images and vehicle driving lane images at the same time, constructing and training convolutional neural networks respectively, collecting and inputting to-be-detected data into the trained convolutional neural networks for preliminary prediction, and judging whether a driver is in a fatigue state or not by integrating preliminary prediction results. The method comprises the following specific steps: generating a training set, respectively constructing and training electroencephalogram signal classification, positioning eye and mouth areas, judging opening and closing states of eyes and mouth, positioning a convolutional neural network of lane line positions, collecting to-be-detected data, preliminarily predicting a fatigue driving state, and fusing and judging a fatigue state of a driver. According to the method, the problems of single driver fatigue state judgment basis mode and incomplete representation of the driver fatigue state are solved, the method has good robustness and stability, and the fatigue driving detection precision is improved.

Description

technical field [0001] The invention belongs to the technical field of detection, and further relates to a fatigue driving detection method based on multimodal information fusion in the technical field of information detection. The present invention can collect the driver's EEG signal, facial image and vehicle lane image to carry out multi-modal information fusion analysis to judge whether the driver is in a fatigue driving state. Background technique [0002] Fatigue driving refers to the phenomenon that the driver's physiological reaction is obviously slowed down in the fatigue state, which leads to the phenomenon that the driving skills are significantly reduced. Accurate and real-time detection of the driver's fatigue state is of great significance to driving safety. At present, research on fatigue driving detection mainly includes fatigue detection based on driver's physiological indicators, fatigue driving detection based on driver's behavior characteristics, and fatig...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a fatigue driving detection method based on multi-modal information fusion, which is used to solve the problem of complicated preprocessing operations, incomplete representation of the driver's fatigue state, and fatigue driving detection in complex environments. The problem of poor robustness and stability and low detection accuracy

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