Shogo Nagasaka

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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)
— In this paper, we propose an online algorithm for multimodal categorization based on the autonomously acquired multimodal information and partial words given by human users. For multimodal concept formation, multimodal latent Dirichlet allocation (MLDA) using Gibbs sampling is extended to an online version. We introduce a particle filter, which(More)
This paper provides a novel summarization method for drive videos using driving behavior, such as driver maneuvers and vehicle reaction, recorded simultaneously alongside video. We segmented the driving behavior into chunks via an unsupervised manner and summarized the drive videos using the chunks, i.e., the switching points of the chunks were emphasized(More)
— Continuous driving-behavioral data can be converted automatically into sequences of " drive topics " in natural language; for example, " gas pedal operating, " " high-speed cruise, " then " stopping and standing still with brakes on. " In regard to developing advanced driver-assistance systems (ADASs), various methods for recognizing driver behavior have(More)
— Time-series driving behavioral data and image sequences captured with car-mounted video cameras can be annotated automatically in natural language, for example, " in a traffic jam, " " leading vehicle is a truck, " or " there are three and more lanes. " Various driving support systems nowadays have been developed for safe and comfortable driving. To(More)