Driving intention recognition and trajectory prediction of moving vehicles are two important requirements of future advanced driver assistance systems (ADAS) for urban intersections. In this paper, we present a consistent framework for solving these two problems. The key idea is to model the spatio-temporal dependencies of traffic situations with a two-dimensional Gaussian process regression. With this representation the driving intention can be recognized by evaluating the data likelihood for each individual regression model. For the trajectory prediction purpose, we transform these regression models into the corresponding dynamical models and combine them with Unscented Kalman Filters (UKF) to overcome the non-linear issue. We evaluate our framework with data collected from real traffic scenarios and show that our approach can be used for recognition of different driving intentions and for long-term trajectory prediction of traffic situations occurring at urban intersections.


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    Titel :

    Modelling of traffic situations at urban intersections with probabilistic non-parametric regression


    Beteiligte:
    Tran, Quan (Autor:in) / Firl, Jonas (Autor:in)


    Erscheinungsdatum :

    01.06.2013


    Format / Umfang :

    1307256 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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