Study on Identification of Driver Steering Behavior Characteristics Based on Pattern Recognition

In this paper, a pattern recognition approach is developed to identify the driver steering behavior characteristics. The detailed process is divided into three parts, feature parameters extracting, clustering process and identification model building. The driving simulator experiments are designed to obtain the primitive data, and the feature parameters are extracted to profile each driver steering manipulation sample. After that, all the driver steering manipulation samples are clustered with the aid of K-means and Gaussian mixture model (GMM). Based on the samples and the corresponding cluster labels, two identification models of driver steering behavior characteristics are built with two typical pattern recognition methods respectively, which are BP Artificial neural network (BP_ ANN) and Support vector machine (SVM). The result shows that the BP_ANN model has higher identification accuracy and can realize the pattern recognition of driver’s driving habits.