Modeling and Recognition of Human Driving Behavior based on Stochastic Switched ARX model

This paper presents a development of the modeling of the human driving behavior based on the expression as Stochastic Switched ARX model (SS-ARX) focusing on the driver’s collision avoidance behavior. First, the parameter estimation technique for the SS-ARX model is introduced based on the EM algorithm. This can be achieved by extending the parameter estimation technique for conventional Hidden Markov Model (HMM). Second, the parameter estimation technique is applied to the collected driving data, and find parameter set for each driving data. The driving data are collected by using the three-dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. Finally, the performance of the SS-ARX model in the case of using as the recognizer is examined. The results show the high potential ability of the SS-ARX model as the behavior recognizer.