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Summary

This study introduces LiDAR-driven neuromorphic control for autonomous vehicles, enhancing speed and steering. Proportional learning demonstrated superior performance in both static and dynamic environments.

Keywords:
PID controlautonomous drivingneural engineering frameworkneuromorphic controlneuromorphic engineeringonline learning

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Area of Science:

  • Robotics and Artificial Intelligence
  • Neuroscience and Control Systems

Background:

  • Autonomous vehicles leverage advances in sensing, control, and machine learning.
  • Neuromorphic (brain-inspired) control offers advantages in energy efficiency, robustness, and adaptability over conventional methods.

Purpose of the Study:

  • To propose and evaluate LiDAR-driven neuromorphic control for autonomous vehicle speed and steering.
  • To compare neuromorphic PID control with online learning approaches for autonomous driving.

Main Methods:

  • Implemented neuromorphic control using biologically plausible basal ganglia and thalamus neural models.
  • Evaluated control schemes in static and dynamic environments using LiDAR data.
  • Extended neural models to include null controllers and target-reaching optimization.

Main Results:

  • Neuromorphic control, particularly proportional learning, outperformed conventional methods.
  • Biologically inspired models effectively managed steering and collision avoidance.
  • Controller extensions significantly improved overall autonomous vehicle performance.

Conclusions:

  • LiDAR-driven neuromorphic control presents a promising, efficient, and robust paradigm for autonomous vehicles.
  • Proportional learning is suggested as a preferred control scheme for its superior performance.
  • Biologically inspired neural models offer a powerful framework for advanced autonomous systems.