Study on classification and identification methods of driver steering characteristics

Abstract

Aiming at the vehicle driver's steering characteristic classification and identification, the research method is initially explored based on CarSim simulation platform. The simulation experiment of steering condition is designed and the test data is collected. According to the maximum yaw rate of the vehicle, the driver steering characteristics are classified by K-means clustering algorithm. The driver steering characteristics identification models are established by learning vector quantization (LVQ) neural network, BP neural network, and support vector machine (SVM) respectively in the environment of Matlab software. The test experiment and comparison are done for the three kinds of approaches, and the results show that all those three kinds of identification approaches have high accuracy, and the SVM method has a certain advantage on driver steering characteristics identification.

Keywords

• vehicle engineering
• driver
• steering characteristics
• simulation platform of CarSim
• classification
• identification model