Driving fatigue detection system and identification method based on eye movement index data

Abstract

The invention discloses a driving fatigue detection system and identification method based on eye movement index data. A data acquisition module collects four kinds of eye movement data according to a frequency of 200HZ; a data analysis processing module transmits the eye movement data into a computer program to carry out preprocessing; the eye movement index data of a driver and a driver fatigue degree corresponding to each data are collected to be original data to establish a random forest model, the classification judgment of the eye movement data of the driver is carried out through each decision tree in the random forest model, and a result is outputted; finally the classification results of decision trees are integrated, voting is carried out by using the random forest model, and a result with a highest integrated voting probability is the final result of the classification. The random forest model of machine learning is employed, the training speed is fast, the classification and prediction performance is excellent, whether the driver is fatigue at present can be identified quickly, the random forest model is continuously increased and updated with data amount, and the discrimination performance is continuously optimized and improved.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, relates to the field of machine learning, and specifically relates to a system and a recognition method for quickly identifying a driver's driving fatigue state by using a random forest algorithm to mine driver's eye movement behavior data. Background technique [0002] In recent years, the number of motor vehicles has increased year by year, and the problem of road traffic safety has become increasingly serious. During the driving process, when the driver engages in driving activities for a long time, it is easy to enter a state of fatigue, which affects his perception and judgment and driving operation, and even causes traffic accidents in extreme cases. [0003] In China, there were 136,386 motor vehicle accidents in 2014, resulting in 42,847 deaths and 141,718 injuries, of which about 14.3% were caused by driver fatigue. Although the consequences of driving fatigue are extremely ser...

AI Technical Summary

Problems solved by technology

However, because the collection of EEG and heart rate data is very intrusive to the driver, it is often not easily accepted by the driver in the actual driving environment. In addition, the difference caused by different vehicle states is relatively large

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