Autonomous driving vehicles have recently gained a lot of attention and are still a work in progress. The goal of this research is to improve the behavior of the autonomous vehicle for path tracking problems in which the steering performance degrades when travelling on curved paths. The pure pursuit controller is a good choice for solving this problem because of its simplicity, accurately, and speed of computation, but tuning its parameters to produce customized proper behavior requires a significant amount of effort. The autonomous computation of a curve identification procedure to manage the change in road curvature was offered as a novel idea for self-tuning of a look-ahead distance. I proposed a technique that splits the path into lines and circular-arcs portions first, and then extracts curve information that would be utilized to calculate the proper look-ahead distances later. Furthermore, the PI controller is used in conjunction with the pure pursuit controller to reduce the existing cross track error. To demonstrate the efficiency of the suggested method, the entire algorithms were implemented in the MATLAB/ Simulink environment for various path shapes. The results show that the novel strategy may greatly minimize lateral errors on curving roadways while maintaining acceptable tracking accuracy.


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    Title :

    Self-tuning Look-ahead Distance of Pure-pursuit Path-following Control for Autonomous Vehicles Using an Automated Curve Information Extraction Method


    Additional title:

    Int. J. ITS Res.


    Contributors:


    Publication date :

    2022-12-01


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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