Deep reinforcement learning (DRL) has been successfully adopted in many tasks, such as autonomous driving and gaming, to achieve or surpass human-level performance. This paper proposes a DRL-based trajectory planner for automated parking systems (APS). A thorough review of literature in this field is presented. A simulation study is conducted to investigate the trajectory planning performance of the parking agent for: (i) different neural-network architectures; (ii) different training set-ups; (iii) efficacy of human-demonstration. Real-time capability of the proposed planner on various embedded hardware platforms is also discussed by the paper, showing promising performance. Insights of the use of DRL for APS are concluded at the end of the paper.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Trajectory Planning for Automated Parking Systems Using Deep Reinforcement Learning


    Additional title:

    Int.J Automot. Technol.


    Contributors:
    Du, Zhuo (author) / Miao, Qiheng (author) / Zong, Changfu (author)

    Published in:

    Publication date :

    2020-08-01


    Size :

    7 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    An Automated Parking Trajectory Planning Method Based on Safe Parking Corridors

    Liu, Honghui / Xie, Xiaomei / Wei, Mingzhu | IEEE | 2023


    Parking task allocation and trajectory planning system based on multi-agent reinforcement learning

    WANG MEILING / CHEN SIYUAN / SONG WENJIE et al. | European Patent Office | 2023

    Free access

    Trajectory Folding-based Path Planning for Automated Parking System

    Lee, Sanghyuk / Kang, Dong Hee / Kang, Chang Mook et al. | IEEE | 2018



    RRT Trajectory Planning Approach For Automated Semi-trailer truck Parking

    Lattarulo, Ray / Perez, Joshue / Murgoitio, Jesus | IEEE | 2022