Sequence Prediction of Driving Behavior Using Double Articulation Analyzer

A sequence prediction method for driving behavior data is proposed in this paper. The proposed method can predict a longer latent state sequence of driving behavior data than conventional sequence prediction methods. The proposed method is derived by focusing on the double articulation structure lat...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2016-09, Vol.46 (9), p.1300-1313
Hauptverfasser:	Taniguchi, Tadahiro, Nagasaka, Shogo, Hitomi, Kentarou, Chandrasiri, Naiwala P., Bando, Takashi, Takenaka, Kazuhito
Format:	Artikel
Sprache:	eng
Schlagworte:	Analyzers Automobile driving Bayesian nonparametrics Context Cybernetics Data models driving behavior data Factories Hidden Markov models Industrial plants machine learning Mathematical models Natural language processing prediction Predictive models Sentences Test procedures Time series analysis Vehicles
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