A multilayer stacking method base on RFE-SHAP feature selection strategy for recognition of driver’s mental load and emotional state

Abstract

The driver state monitoring is becoming one of the research hotspots in the field of traffic and vehicle safety, which can ensure driving safety by monitoring the driver’s state. Therefore, this work makes an attempt to recognize driver's mental load and emotional states. However, the reliability and accuracy of driver status detection largely depend on the extracted features and the detection algorithm. The existing methods mainly improve accuracy by increasing the number of features, but for the problem with limited training samples, the increase in features may lead to an increase in model variance, and resulting in overfitting. In addition, single-model based methods may fall into local optimal solutions when performing local search. To alleviate these, we firstly propose an RFE-SHAP algorithm that improves the recursive feature elimination algorithm by using the Shapley Additive exPlanning value, and paired it with the eXtreme Gradient Boosting (XGBoost) algorithm to screen out the subset of features that best represent the driver's mental load and emotional state. Secondly, we proposed a multilayer stacking ensemble learning method based on different optimal feature subsets to further improve the accuracy of the driver’s mental load and emotional state recognition. For each base models used for integration learning, the corresponding optimal feature subsets are selected using the RFE-SHAP algorithm, and then the different optimal feature subsets are combined with multilayer stacking ensemble learning. The validation via experimental data demonstrates that the XGBoost-RFE-SHAP algorithm achieves superior model performance with fewer feature combinations than the classic algorithm. The proposed driver state detection model based on multiple optimum feature subsets and multilayer stacking integration methods would more accurately identify the driver's mental load and emotional state than the single model, commonly used machine learning and integrated learning algorithms.