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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    Title :

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


    Contributors:
    Zhao, Xinshi (author) / Liu, Di (author) / Yang, Kang (author) / Baldi, Simone (author)


    Publication date :

    2024-09-24


    Size :

    1050507 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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