Hiroaki Fujinami

Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model

This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched autoregressive exogenous (SS-ARX) model. First, a parameter estimation algorithm for the SS-ARX model with multiple measured input-output sequences is developed based on the expectation-maximization algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model. Second, the developed parameter estimation algorithm is applied to driving data with the focus being on drivers collision avoidance behavior. The driving data were collected using a driving simulator based on the cave automatic virtual environment, which is a stereoscopic immersive virtual reality system. Then, the parameter set for each driver is obtained, and certain driving characteristics are identified from the viewpoint of switched control mechanism. Finally, the performance of the SS-ARX model as a behavior recognizer is examined. The results show that the SS-ARX model holds remarkable potential to function as a behavior recognizer.

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.