Research on drivers' cognitive distracted recognition model based on random forest

doi:10.16265/j.cnki.issn1003-3033.2018.01.004

Abstract

Abstract: In order to recognize the cognitive distracted state of drivers, a driving simulator test was carried out with 13 drivers recruited as testees. Data on eye movement of the testees were collected by an eye tracker when they performed a normal driving task or a cognitive secondary tasks. Eye movement features were extracted through 5 s time window. A cognitive distracted recognition model was built using random forest. The optimal model parameters were determined by grid searching, and the model performance were evaluated by using the 100 times hold out tests method. The effects of cognitive load on drivers' fixation duration and blink duration were analyzed according to feature importance measurements generated by the random forest model. The results show that when the number of decision trees is 125 and max features are 5, the mean accuracy can achieve 83.69%, that fixation duration and noise data duration are the two features having a key role in recognizing cognitive distraction, and that an increase in cognitive load will result in longer fixation duration and shorter blink duration.

Key words: transportation safety, cognitive distraction, eye movement feature, random forest, driving simulator test

CLC Number:

ZHOU Yang, FU Rui, YUAN Wei, WANG Dong, ZHANG Ruibin. Research on drivers' cognitive distracted recognition model based on random forest[J]. China Safety Science Journal, 2018, 28(1): 20-25.

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