# Fuzzy Logic based Autonomous Parking Systems -- Part III: A Fuzzy Decision Tree System

**Authors:** Yu Wang, Xiaoxi Zhu  
**arXiv:** [1912.07571](https://arxiv.org/abs/1912.07571)  
**Date:** 2019-12-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1912.07571v1/

## Key points

- Authors Yu Wang and Xiaoxi Zhu present a paper on the design of a Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS).
- The proposed controller combines a Base Fuzzy Controller (BFC) with a Supervisory Fuzzy Decision Tree Controller (SFDTC) to ensure optimal parking trajectory with minimal computational cost.
- The BFC provides efficient trajectory planning while the SFDTC enhances system robustness in noisy environments by integrating Decision Tree theory and fuzzy inference mechanisms.
- A data training process is implemented to improve control performance.
- Simulations conducted using MATLAB show that the IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates superior performance characterized by faster convergence and minimal deviation from the optimal parking trajectory.

## Summary

Authors Yu Wang and Xiaoxi Zhu present a paper on the design of a Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS). The proposed controller combines a Base Fuzzy Controller (BFC) with a Supervisory Fuzzy Decision Tree Controller (SFDTC) to ensure optimal parking trajectory with minimal computational cost. Building upon previous work in fuzzy logic control for unmanned parking, the BFC provides efficient trajectory planning while the SFDTC enhances system robustness in noisy environments by integrating Decision Tree theory and fuzzy inference mechanisms. A data training process is also implemented to improve control performance. Through simulations conducted using MATLAB, the IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates superior performance characterized by faster convergence and minimal deviation from the optimal parking trajectory. This research contributes to advancing autonomous parking systems through the detailed design and evaluation of the Supervisory Fuzzy Decision Tree Controller.