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Road-Adaptive Precise Path Tracking Based on Reinforcement Learning Method.

Bingheng Han1, Jinhong Sun2

  • 1School of Information Science and Engineering, Fudan University, Shanghai 200433, China.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

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Lightweight Road Adaptive Path Tracking Based on Soft Actor-Critic RL Method.

Sensors (Basel, Switzerland)·2025
See all related articles

This study introduces the SACPP controller, a novel speed-adaptive autonomous driving framework. It achieves efficient path tracking and obstacle avoidance by integrating soft actor-critic (SAC) and pure pursuit (PP) methods for real-time speed optimization and control.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous driving systems require robust path-tracking capabilities.
  • Real-time adaptation to vehicle dynamics and energy efficiency is crucial.
  • Obstacle avoidance must be integrated with path-following for safety.

Purpose of the Study:

  • To propose a speed-adaptive autonomous driving path-tracking framework named SACPP controller.
  • To enhance path tracking accuracy, obstacle avoidance, and energy efficiency.
  • To reduce computational load for real-time applications.

Main Methods:

  • Utilizing hybrid A* algorithm for obstacle-free path planning.
  • Employing soft actor-critic (SAC) for real-time optimal speed and preview point prediction.
Keywords:
motor efficiency mappath trackingpure pursuit controlsoft actor–critic

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  • Implementing pure pursuit (PP) for front wheel angle control based on predicted preview point.
  • Designing a specialized evaluation function for robust tracking under uncertainties.

    • Main Results:

    • The SACPP controller effectively tracks paths while avoiding obstacles.
    • The framework optimizes vehicle speed for high motor energy efficiency.
    • A lightweight network structure and geometry-based control reduce computational load.
    • The control framework achieves a control cycle exceeding 100 Hz on an i7 CPU.

    Conclusions:

    • The SACPP controller offers a computationally efficient and effective solution for speed-adaptive autonomous driving.
    • The integration of SAC and PP methods enhances both performance and energy efficiency.
    • The framework demonstrates practical feasibility for real-time autonomous navigation.