This paper presents a neural network developed for predicting the path of a remote vehicle using post facto created vehicle-to-vehicle (V2V) data and uses that prediction to determine whether it is safe to change lanes. The data was collected in a 2013 experiment involving various drivers traveling on public roads in Ann Arbor, MI. The trips were on suburban roads, city roads and divided highways over a two day period. The vehicular satellite global positioning system (GPS) data from movement over this period was gathered and post-processed to find vehicle paths within 10 meters of one another. The path traces of the two vehicles were combined to simulate what a V2V network would have provided to properly equipped vehicles if such a network and vehicles existed on real road networks demonstrating natural driving behavior.This research harnesses this data to determine the increased effectiveness of a neural network predicting the future path of surrounding vehicles and lane change safety when a V2V network is available. The most studied maneuver is overtaking. To a lesser extent, this paper also provides a view into how a neural network predicts remote vehicle behaviors using a host vehicle equipped with only perceptive hardware and no given information from the remote vehicle.


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    Titel :

    Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data


    Beteiligte:
    Breg, Alexander (Autor:in) / Murphey, Yi Lu (Autor:in) / Yu, Takchoi (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    268866 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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