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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Semiotic prediction of driving behavior using unsupervised double articulation analyzer


    Beteiligte:


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    1149486 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Semiotic Prediction of Driving Behavior Using Unsupervised Double Articulation Analyzer

    Taniguchi, T. / Nagasaka, S. / Hitomi, K. et al. | British Library Conference Proceedings | 2012


    PREDICTION OF NEXT CONTEXTUAL CHANGING POINT OF DRIVING BEHAVIOR USING UNSUPERVISED BAYESIAN DOUBLE ARTICULATION ANALYZER

    Nagasaka, S. / Taniguchi, T. / Hitomi, K. et al. | British Library Conference Proceedings | 2014




    Determining Utterance Timing of a Driving Agent With Double Articulation Analyzer

    Taniguchi, Tadahiro / Furusawa, Kai / Liu, Hailong et al. | IEEE | 2016

    Freier Zugriff