Millions of traffic accidents take place every year on roads around the world. Some advanced assistance systems have been released in commercial vehicles in the past few years, contributing to the transition towards semiautonomous vehicles. Some of the best known are the adaptive cruise control and the lane keeping systems. These systems keep a desired distance with respect to the preceding vehicle or a fixed speed on the center of the lane. It is very useful for these systems to know what the surrounding vehicles trajectories will be or if they will perform a lane change manoeuvre. This paper evaluates two kinds of artificial neural networks over two different datasets to predict its trajectories. A Support Vector Machine classifier is used to classify the action that will be carried out. The proposed trajectory prediction systems are 30% better than the vehicle motion model in a time horizon of 4 seconds and are able to predict a lane change action 3 seconds before it happens.


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    Titel :

    Vehicle trajectory and lane change prediction using ANN and SVM classifiers


    Beteiligte:
    Izquierdo, R. (Autor:in) / Parra, I. (Autor:in) / Munoz-Bulnes, J. (Autor:in) / Fernandez-Llorca, D. (Autor:in) / Sotelo, M. A. (Autor:in)


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    194970 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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