Facial feature based driver attention state detection method

Abstract

The invention relates to a facial feature based driver attention state detection method. The detection method is characterized by comprising the following steps of performing PERCLOS based driver fatigue state detection; performing blink frequency based driver fatigue state detection; performing sight line distribution based driver attention distraction state detection; and sending out early warning for driver fatigue and attention distraction state, wherein the early warning for fatigue or attention distraction state is carried out continuously for a certain time to avoid frequent switching between different states; the early warning priority is as follows: fatigue driving is greater than attention distraction that is greater than normal driving; and the attention state of the driver can be accurately detected through the physiological reaction characteristics of the driver, and early warning is sent out for driver fatigue and the attention distraction state; collection and extraction of the facial features of the driver are not included; the sampling efficiency of the adopted driver facial feature collection and extracting system is fHz; and the frequency for reading the driver facial feature is f<0> Hz.

Description

technical field [0001] The invention relates to a method for detecting a driver's attention state, in particular to a method for detecting a driver's attention state based on facial features, and belongs to the field of intelligent vehicle safety assisted driving. Background technique [0002] Driver fatigue and distraction are important factors causing road traffic accidents. Studies have shown that more than 23% of collisions and near-collisions are related to the state of driver's attention. However, with the continuous increase of in-vehicle information systems, this phenomenon is becoming more and more serious. [0003] Real-time and effective detection of driver's fatigue and distraction is an important measure to improve driving safety and road traffic environment. [0004] At present, the methods for detecting the driver's attention state can be roughly divided into five types: [0005] 1) Detection methods based on the driver's physiological signals, such as EEG, ...

AI Technical Summary

Problems solved by technology

However, after successfully extracting the driver's physiological response characteristics (eyelid size, blink times, gaze direction, etc.), there are certain problems in judging from the driver's physiological response characteristics to the driver's attention state, which urgently needs to be improved.

At the same time, due to certain differences among different drivers, the accuracy of the existing driver fatigue and distraction state detection system is further reduced.

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