Self-driving vehicles have attracted tremendous attention from all walks of life and parking prediction is essential for the vehicle to find a parking space. Many existing works focus on parking predictions in an sensor network equipped environment using large datasets. In this paper, we consider a campus-wide smart parking problem that requires no sensor installation and external large datasets. We assume that the parking availabilities of the garages are not known and the only information that a vehicle uses is its past recorded statistics. We get insights from the multi-armed bandits problem in reinforcement learning and come up with a framework for a vehicle to make a wise decision in each trial. We also propose several non-reinforcement learning algorithms for comparison. We conduct extensive simulation and discussion to evaluate the performance of these algorithms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Energy-efficient Smart Parking for Self-driving Vehicles


    Contributors:
    Chen, Xiao (author)


    Publication date :

    2021-05-01


    Size :

    181748 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Planning parking trajectory for self-driving vehicles

    LI DONG / LUO QI / ZHANG LIANGLIANG et al. | European Patent Office | 2021

    Free access

    MANAGING SELF-DRIVING VEHICLES WITH PARKING SUPPORT

    GAO SHENGLONG / YEN MARK | European Patent Office | 2021

    Free access

    PLANNING PARKING TRAJECTORY FOR SELF-DRIVING VEHICLES

    LI DONG / LUO QI / ZHANG LIANGLIANG et al. | European Patent Office | 2020

    Free access

    Smart parking system for vehicles

    Adhatarao, Sripriya Srikant / Alfandi, Omar / Bochem, Arne et al. | IEEE | 2014


    Parking management architecture for parking autonomous driving vehicles

    ZHOU JINYUN / HE RUNXIN / LUO QI et al. | European Patent Office | 2020

    Free access