With the development of highly automated vehicles a situation recognition of the traffic environment is of considerable interest. In particular challenging traffic situations i.e. cut-in scenarios have to be detected early and reliably. Here an environment perception is presented as a base for behavior control, which offers a consistent environment sensing and a situation awareness regarding lane change maneuvers of the surrounding traffic. For this a probabilistic approach based on Bayesian networks is applied to predict lane change maneuvers considering human driving behavior and traffic rules as well as the measurable object and lane states. The approach is applied to the Temporary Autopilot in the EU funded project HAVEit.


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    Title :

    A probabilistic lane change prediction module for highly automated driving


    Additional title:

    Eine probabilistisches Modell zur Vorhersage des Spurwechsels bei hoch automatisiertem Fahren


    Contributors:


    Publication date :

    2010


    Size :

    6 Seiten, 9 Bilder, 11 Quellen


    Remarks:

    (nicht paginiert)


    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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