In the area of autonomous driving, it typically brings great difficulty in solving the motion planning problem since the vehicle model is nonlinear and the driving scenarios are complex. Particularly, most of the existing methods cannot be generalized to dynamically changing scenarios with varying surrounding vehicles. To address this problem, this development here investigates the framework of integrated decision and control. As part of the modules, static path planning determines the reference candidates ahead, and then the optimal path-tracking controller realizes the specific autonomous driving task. An innovative and effective constrained finite-horizon approximate dynamic programming (ADP) algorithm is herein presented to generate the desired control policy for effective path tracking. With the generalized policy neural network that maps from the state to the control input, the proposed algorithm preserves the high effectiveness for the motion planning problem towards changing driving environments with varying surrounding vehicles. Moreover, the algorithm attains the noteworthy advantage of alleviating the typically heavy computational loads with the mode of offline training and online execution. As a result of the utilization of multi-layer neural networks in conjunction with the actor-critic framework, the constrained ADP method is capable of handling complex and multidimensional scenarios. Finally, various simulations have been carried out to show that the constrained ADP algorithm is effective.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Policy Iteration Based Approximate Dynamic Programming Toward Autonomous Driving in Constrained Dynamic Environment


    Contributors:
    Lin, Ziyu (author) / Ma, Jun (author) / Duan, Jingliang (author) / Li, Shengbo Eben (author) / Ma, Haitong (author) / Cheng, Bo (author) / Lee, Tong Heng (author)


    Publication date :

    2023-05-01


    Size :

    2055020 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Kernel-based Approximate Dynamic Programming for Autonomous Vehicle Stability Control

    Guo, Hongyan / Tan, Zhongqiu / Liu, Jun et al. | IEEE | 2022


    Dynamic Airspace Configuration Using Approximate Dynamic Programming

    Kulkarni, Sameer / Ganesan, Rajesh / Sherry, Lance | Transportation Research Record | 2012


    Differential dynamic programming (DDP) based planning architecture for autonomous driving vehicles

    LUO QI / ZHOU JINYUN / JIANG SHU et al. | European Patent Office | 2023

    Free access

    Trajectory optimization of autonomous driving by differential dynamic programming

    Huang, Weiwei / Wu, Xiaojun / Zhang, Qun et al. | IEEE | 2014


    Dynamic programming and quadratic programming based decision and planning for autonomous driving vehicles

    ZHU FAN / KONG QI / ZHU WEICHENG et al. | European Patent Office | 2020

    Free access