The paper presents a probabilistic approach for online parameter estimation of an enhanced car-following model appropriate for multi-lane traffic, which is based on an extension of the well-known Intelligent Driver Model (IDM). The approach explicitly considers the simultaneous influence of several interacting vehicles on the longitudinal dynamics of the ego-vehicle. Therefore, a method to extract the relevant reference vehicles considered in the proposed multi-lane car-following model is developed. In order to calibrate the model parameters online, a particle filter approach, which is able to deal with the overdetermined model structure, is employed. Experimental studies using a real highway scenario observed by vehicle surroundings sensors show the need of the online-calibrated multi-lane architecture. To use the model for vehicle speed prediction, a further learning-based extension is suggested which enables the adaption of the model parameters over the prediction horizon.


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    Title :

    Interaction-Aware Approach for Online Parameter Estimation of a Multi-lane Intelligent Driver Model


    Contributors:


    Publication date :

    2019-10-01


    Size :

    723787 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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