In order to improve the classification accuracy of random forest algorithm in driver’s gaze estimation task

an improved random forest model is designed.Firstly

the driver ’s head and eye posture features are extracted

then the driver features are preprocessed by the Gaussian Mixture Model

which can increase the independence between random forest decision trees.After the completion of the random forest tree

the corresponding weights of each decision tree are given to improve the classification effect of the random forest model.Simulation experiments show that the average recognition rate of the improved random forest algorithm for the driver’s 13 sight areas can reach 93.47 %

which is higher than the traditional random forest classification effect.