Autonomous tracking control is one of the most important part among the vehicle autonomous navigation, it is related to the safety and comfort of vehicles and traffic efficiency. In this paper, the model predictive control method is designed to solve the problem of path planning and tracking controller in the process of overtaking. Based on the analysis of lane changing model, the kinematics of the vehicle is established to research the kinematics regularly of the vehicle from the perspective of geometry. In addition, the minimum safe distance model of overtaking lane change is constructed. By comparing several typical lane change models, the mixed function model is selected as the path planning model of overtaking lane change. The experimental results demonstrate that the improved model predictive control method has the advantages of small steady-state error, fast response and strong robustness, which can effectively improve the accuracy of path tracking.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A MPC-BASED Mix function controller on path planning and path tracking of autonomous vehicles


    Contributors:
    Ma, Ning (author) / Kang, Shun (author) / Zhuang, Hao (author) / Ge, Pinghai (author) / He, Tao (author) / Cui, Zhen (author)


    Publication date :

    2024-05-24


    Size :

    1922719 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Path following controller for autonomous vehicles

    Domina, Adam / Tihanyi, Viktor | IEEE | 2019


    A 3D path-following velocity-tracking controller for autonomous vehicles

    Cuncha, Rita / Silvestre, Carlos | Tema Archive | 2005


    Explicit path tracking by autonomous vehicles

    Dong Hun Shin / Sanjiv Singh / Lee, Ju-Jang | Tema Archive | 1992


    PATH PLANNING FOR AUTONOMOUS AND SEMI-AUTONOMOUS VEHICLES

    MATSUDA TAKURO / ZHANG XINGZHONG | European Patent Office | 2021

    Free access