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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous vehicles require sophisticated control systems for safe navigation.
  • Current physics-based models have limitations in adapting to diverse operating conditions and utilizing vehicle-generated data.

Purpose of the Study:

  • To develop and evaluate a neural network-based control model for automated vehicles.
  • To improve trajectory tracking performance and adaptability compared to traditional physics-based models.

Main Methods:

  • A feedforward-feedback control structure was employed.
  • A physics-inspired neural network model processed sequences of past states and inputs.
  • The model was trained on data from various road conditions (dry and snow).

Main Results:

  • The neural network model demonstrated superior performance over the physics-based model on an experimental vehicle.
  • The model successfully adapted to different road surfaces without explicit friction estimation.
  • Performance was comparable to a champion race car driver in path tracking.

Conclusions:

  • Neural network models offer a promising approach for robust model-based control in automated vehicles.
  • This data-driven method overcomes limitations of traditional physics-based models.
  • Further investigation is warranted for controlling automated vehicles across their full operating range.