This work presents an approach for capturing vehicle-following behavior on highways, based on Inverse Re-inforcement Learning (IRL) with control Lyapunov function. The idea is to describe the vehicle-following behavior as an optimal control problem with its underlying cost and constraints expressed in terms of the data. Using the highD dataset as case study, we identify vehicle-following dynamics and frame them in an IRL context. Using kernel regression, we show that such IRL boils down to a Quadratic Programming (QP), solvable using standard optimization routines.


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    Titel :

    Lyapunov-Based Inverse Reinforcement Learning for Vehicle-Following Traffic Scenarios


    Beteiligte:
    Zhao, Xinshi (Autor:in) / Liu, Di (Autor:in) / Yang, Kang (Autor:in) / Baldi, Simone (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1050507 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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