Kentarou Hitomi

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— In this paper, we propose a novel semiotic prediction method for driving behavior based on double artic-ulation structure. It has been reported that predicting driving behavior from its multivariate time series behavior data by using machine learning methods, e.g., hybrid dynamical system, hidden Markov model and Gaussian mixture model, is difficult(More)
A class of biped locomotion called Passive Dynamic Walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, studies of Quasi-PDW which incorporates supplemental actuators have been reported to overcome this sensitivity. In this article,(More)
— In order for robots to safely move in human-robot coexisting environment, they must be able to predict their surrounding people's behavior. In this study, a pedestrian behavior model that produces humanlike behavior was developed. The model takes into account the pedestrian's intention. Based on the intention, the model pedestrian sets its subgoal and(More)
— A class of biped locomotion called Passive Dynamic Walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, some studies of Quasi-PDW, which introduces supplementary actua-tors, are reported to overcome the sensitivity. In this(More)
— Future advanced driver assistance systems (ADASs) should observe a driving behavior and detect con-textual changing points of driving behaviors. In this paper, we propose a novel method for predicting the next contex-tual changing point of driving behavior on the basis of a Bayesian double articulation analyzer. To develop the method, we extended a(More)