In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.


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    Title :

    Learning Behavior Models for Interpreting and Predicting Traffic Situations


    Contributors:

    Publication date :

    2014



    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English






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