Random forest–based feature selection and detection method for drunk driving recognition

Abstract

A method for drunk driving detection using Feature Selection based on the Random Forest was proposed. First, driving behavior data were collected using a driving simulator at Beijing University of Technology. Second, the features were selected according to the Feature Importance in the random forest. Third, a dummy variable was introduced to encode the geometric characteristics of different roads so that drunk driving under different road conditions can be detected with the same classifier based on the random forest. Finally, the linear discriminant analysis, support vector machine, and AdaBoost classifiers were used and compared with the random forest. The accuracy, F1 score, receiver operating characteristic curve, and area under the curve value were used to evaluate the performance of the classifiers. The results show that Accelerator Depth, Speed, Distance to the Center of the Lane, Acceleration, Engine Revolution, Brake Depth, and Steering Angle have important influences on identifying the drivers’ states and can be used to detect drunk driving. Specifically, the classifiers with Accelerator Depth outperformed the other classifiers without Accelerator Depth. This means that Accelerator Depth is an important feature. Both the AdaBoost and random forest classifiers have an accuracy of 81.48%, which verified the effectiveness of the proposed method.