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Data-Driven Enhancements for MPC-Based Path Tracking Controller in Autonomous Vehicles.

Jianhua Guo1, Zhihao Xie1, Ming Liu2

  • 1National Key Laboratory of Automotive Chassis Integration and Bionics, Changchun 130022, China.

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|December 17, 2024
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Summary
This summary is machine-generated.

This study enhances autonomous vehicle path tracking using data-driven methods. The new approach improves accuracy, outperforming standard controllers in challenging conditions.

Keywords:
data-drivenmodel predictive controlpath tracking

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Area of Science:

  • Autonomous Systems
  • Control Theory
  • Machine Learning

Background:

  • Accurate control models are vital for model-based control in autonomous vehicles.
  • Model inaccuracies due to simplification, parameter variation, and noise degrade path tracking precision.

Purpose of the Study:

  • To introduce data-driven enhancements for a Model Predictive Control (MPC)-based path tracking controller (DD-PTC) for autonomous vehicles.
  • To improve the precision of vehicle path tracking by enhancing the control model accuracy.

Main Methods:

  • Utilizing Kolmogorov-Arnold Networks (KANs) to estimate tire lateral forces and correct cornering stiffness for a dynamic predictive model.
  • Employing Gaussian Process Regression (GPR) to capture unmodeled vehicle dynamics for a comprehensive control model.
  • Implementing the enhanced model within an MPC framework for steering control.

Main Results:

  • The DD-PTC approach demonstrated superior performance compared to standard MPC and Linear Quadratic Regulator (LQR) strategies.
  • Significant reduction in lateral distance errors was observed, particularly under challenging driving scenarios.
  • Validation was performed using the Simulink-CarSim simulation platform.

Conclusions:

  • The proposed data-driven enhancements effectively improve the accuracy of the control model for autonomous vehicle path tracking.
  • DD-PTC offers a robust solution for precise steering control, outperforming conventional methods.
  • The integration of KANs and GPR provides a powerful framework for addressing model uncertainties in autonomous driving.