Abstract-This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched auto-regressive 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 (EM) algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model (HMM). Second, the developed parameter estimation algorithm is applied to driving data with the focus being on driver's collision avoidance behavior. The driving data were collected using a driving simulator based on the CAVE virtual environment, which is a stereoscopic immersive virtual reality (VR) 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.