Semiotic prediction of driving behavior using unsupervised double articulation analyzer
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 Mar...
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| creator | Taniguchi, Tadahiro Nagasaka, Shogo Hitomi, Kentarou Chandrasiri, Naiwala P. Bando, Takashi |
| description | 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 a driver's behavior is affected by various contextual information. To overcome this problem, we assume that contextual information has a double articulation structure and develop a novel semiotic prediction method by extending nonparametric Bayesian unsupervised morphological analyzer. Effectiveness of our prediction method was evaluated using synthetic data and real driving data. In these experiments, the proposed method achieved long-term prediction 2-6 times longer than some conventional methods. |
| doi_str_mv | 10.1109/IVS.2012.6232243 |
| format | Conference Proceeding |
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| language | eng ; jpn |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Context Data models Hidden Markov models Predictive models Semiotics Time series analysis Vehicles |
| title | Semiotic prediction of driving behavior using unsupervised double articulation analyzer |
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