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In this paper, we propose a novel semiotic prediction method for driving behavior based on double articulation 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 because(More)
Various advanced driver assistance systems (ADASs) have recently been developed, such as Adaptive Cruise Control and Precrash Safety System. However, most ADASs can operate in only some driving situations because of the difficulty of recognizing contextual information. For closer cooperation between a driver and vehicle, the vehicle should recognize a wider(More)
An unsupervised learning method, called double articulation analyzer with temporal prediction (DAA-TP), is proposed on the basis of the original DAA model. The method will enable future advanced driving assistance systems to determine driving context and predict possible scenarios of driving behavior by segmenting and modeling incoming driving-behavior time(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 moves(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 actuators, are reported to overcome the sensitivity. In this article,(More)
This paper presents a novel method for integrating driving behavior and traffic context through signal symbolization in order to summarize driving semantics from sensor outputs. The method has been applied to risky lane change detection. Language models (nested Pitman-Yor language model) and speech recognition algorithms (hidden Markov Model) have been(More)
We investigate a possible method for detecting a driver's negative adaptation to an automated driving system by analyzing consistency of driver decision making and driver gaze behavior during automated driving. We focus on an automated driving system equivalent to Level 2 automation per the NHTSA's definition. At this level of automation, drivers must be(More)