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Related Experiment Video

Updated: Jan 19, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Published on: January 20, 2023

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Reinforcement Learning-Based End-to-End Parking for Automatic Parking System.

Peizhi Zhang1, Lu Xiong2, Zhuoping Yu3

  • 1School of Automotive Studies, Tongji University, Shanghai 201804, China. zhangpeizhi@tongji.edu.cn.

Sensors (Basel, Switzerland)
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel reinforcement learning algorithm for end-to-end automatic parking, overcoming path tracking errors common in traditional systems. Real-world tests confirm superior parking performance compared to existing methods.

Keywords:
automatic parking system (APS)end-to-end parkingparking slot trackingreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Current automatic parking systems (APS) rely on path planning and tracking.
  • Vehicle dynamics introduce path tracking errors, causing parking deviations.

Purpose of the Study:

  • To propose a reinforcement learning-based end-to-end algorithm for more accurate automatic parking.
  • To address limitations in convergence and local optima in reinforcement learning for parking.

Main Methods:

  • An end-to-end reinforcement learning algorithm directly outputs steering commands.
  • A vision and chassis-integrated algorithm ensures continuous parking slot tracking.
  • Specialized reinforcement learning training methods are developed for parking scenarios.

Main Results:

  • The proposed algorithm avoids path tracking errors inherent in traditional APS.
  • Real-vehicle tests demonstrated significantly improved parking attitude.
  • The system achieved better parking outcomes than path planning and tracking methods.

Conclusions:

  • Reinforcement learning offers a robust solution for end-to-end automatic parking.
  • The integrated approach enhances parking accuracy and stability.
  • This method represents an advancement over conventional automatic parking techniques.