Abstract-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 driver's 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. Index Terms-Behavior recognition, driving behavior, expectation-maximization (EM) algorithm, hidden Markov model (HMM), stochastic switched autoregressive exogenous (SS-ARX) model.
In REF the authors introduce a stochastic-switched autoregressive exogenous (SS-ARX) to imitate drivers behavior in the lane-change scenario.
Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Engineering Computer Science