In highway driving scenarios it is important for highly automated driving systems to be able to recognize and predict the intended maneuvers of other drivers in order to make robust and informed decisions. Many methods utilize the current kinematics of vehicles to make these predictions, but it is possible to examine the relations between vehicles as well to gain more information about the traffic scene and make more accurate predictions. The work presented in this paper proposes a novel method of predicting lane change maneuvers in highway scenarios using deep learning and a generic visual representation of the traffic scene. Experimental results suggest that by operating on the visual representation, the spacial relations between arbitrary vehicles can be captured by our method and used for more informed predictions without the need for explicit dynamic or driver interaction models. The proposed method is evaluated on highway driving scenarios using the Interstate-80 dataset and compared to a kinematics based prediction model, with results showing that the proposed method produces more robust predictions across the prediction horizon than the comparison model.


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    Title :

    Learning to Predict Lane Changes in Highway Scenarios Using Dynamic Filters On a Generic Traffic Representation


    Contributors:


    Publication date :

    2018-06-01


    Size :

    968487 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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