Application of Clustering Analysis and Fuzzy Logic in Driving Behavior Identification

The continuous movement process of vehicle should be processed by state discretization when the recognition of driving behavior and the prediction of vehicle state are performed.During state division,it would esaily appear the repeated state divisions and it is difficult for a hidden markov model(HMM)to determine state transition probability and the initial probability during identifying state sequences.Therefore,this paper adopts clustering analysis to process the datas in the sliding window so as to ensure the uniqueness of state division.In addition,the state logical decision is established to correct the wrong state division.The different driving behaviors are divided into several layers through CPNtool hierarchical analysis software.The interaction between layers is achieved through the state transfer between layer and layer.The time which state flows in the constant intervals occupy is used to the current driving behavior.Finally,the sample datas collected from the test car are used to validate the established model and the result shows that this model can show the switch of different driving behavior states in visual way.The model can reached more than 96%judgment accuracy for driving behavior.