Road collisions are a worldwide pandemic that can be addressed through the improvement of existing tools for safety analysis. A refined probabilistic framework is presented for the analysis of road-user interactions. In particular, the identification of potential collision points is used to estimate collision probabilities, and their spatial distribution can be visualized. A probabilistic time to collision is introduced, and interactions are grouped into four categories: head-on, rear-end, side, and parallel. The framework is applied to a large data set of video recordings collected in Kentucky that contains more than 300 severe interactions and collisions. The results demonstrate the usefulness of the approach for studying road-user behavior and mechanisms that may lead to collisions.


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    Titel :

    Large-Scale Automated Analysis of Vehicle Interactions and Collisions


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Saunier, Nicolas (Autor:in) / Sayed, Tarek (Autor:in) / Ismail, Karim (Autor:in)


    Erscheinungsdatum :

    2010-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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