A method for detection and recognition of driver distraction state in mixed traffic environment

Abstract

The invention relates to a driver distraction state detection and recognition method in a mixed traffic environment, comprising: building a mixed traffic scene, collecting driver eye movement parameter data and driving performance data, processing data and selecting the best, and building a fusion-based attention The Bi-LSTM driver distraction recognition model of the mechanism, the steps such as identifying the distracted state of the driver, the present invention adopts the Bi-LSTM algorithm of the fusion attention mechanism to carry out the recognition modeling of distracted driving, and improves the accuracy of distraction recognition High degree of accuracy and robustness, fills the gap in driver distraction recognition in mixed traffic environment, solves the technical problem of road traffic accidents caused by distracted driving in the process of driving in mixed traffic environment, and improves Driver's vehicle driving safety.

Description

technical field [0001] The invention relates to a driving state detection and recognition method, in particular to a driver distraction state detection and recognition method in a mixed traffic environment. Background technique [0002] Distracted driving is the leading cause of traffic accidents and constitutes a growing traffic safety problem, with 25% of traffic accidents due to distracted driving, according to the U.S. Highway Safety Administration, a survey based on 1,367 drivers The study found that large-scale traffic accidents caused by distracted driving account for about 14%-33%. The mixed driving of connected and autonomous vehicles will become the norm, the mixed traffic environment will become more complex, and the factors that lead to driving distraction will also increase significantly. Therefore, it is extremely important to identify the driver's driving state distracted in the mixed traffic environment. , When a distracted driving state occurs, an effective...

AI Technical Summary

Problems solved by technology

[0003] At present, most of the distracted driving detection methods are to identify the driver's distraction in the traditional driving environment. However, based on the inevitable mixed traffic environment, there are few studies on distracted identification. The method is generally based on the superposition processing of the driving posture picture and then used as input. The distracted driving recognition method based on the deep convolutional neural network recognizes the distracted driving state, which is based on the traditional driving environment, without considering the eye movement parameters, driving parameters, and driving conditions. Performance parameters, especially eye movement parameters, are important indicators for identifying distracted driving. If the impact indicators of driving distraction cannot be fully considered, it will have a certain impact on the accuracy of distracted driving

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