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Flow Field and Neural Network Guided Steering Control for Rigid Autonomous Vehicles

  • Mengxuan Song
  • Timothy Gordon
  • Yinqi Liu
  • Jun WangEmail author
Conference paper
  • 6 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

This paper studies the steering control for low-speed manoeuvring of autonomous ground vehicles. A guidance method combining flow analogy and a neural network model is proposed to produce the proper angular velocity for the vehicle, which can be used as a reference for the control of the steering wheel. In a previous study, fluid flow itself has shown outstanding global search capabilities in guiding the vehicle through complicated environments. But the vehicle is not always able to follow the motion of the flow due to the difference of their nature. In this paper, the heat flow analogy is used instead of fluid flow, and a neural network model is added upon the flow layer in order to produce a steering reference more suitable for a rigid vehicle. Simulated results demonstrate that, except for the branching situations, the proposed method is able to guide the vehicle towards its desired destination.

Keywords

Autonomous vehicles Vehicle dynamics Neural network Path planning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mengxuan Song
    • 1
  • Timothy Gordon
    • 2
  • Yinqi Liu
    • 1
  • Jun Wang
    • 1
    Email author
  1. 1.Department of Control Science and EngineeringTongji UniversityShanghaiChina
  2. 2.University of LincolnLincolnUK

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