Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.


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    Titel :

    Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving


    Beteiligte:
    Zhang, Yihuan (Autor:in) / Lin, Qin (Autor:in) / Wang, Jun (Autor:in) / Verwer, Sicco (Autor:in) / Dolan, John M. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2018


    Format / Umfang :

    2045726 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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