A reinforcement learning-based reverse-parking system for autonomous vehicles

被引：0

作者：

Al-Mousa, Amjed ^{[1
]}

Arrabi, Ahmad ^{[1
]}

Daoud, Hamza ^{[1
]}

机构：

[1] PSUT, Comp Engn Dept, Amman, Jordan

来源：

IET INTELLIGENT TRANSPORT SYSTEMS | 2025年 / 19卷 / 01期

关键词：

Artificial Intelligence; Autonomous Driving; Autonomous Parking; Intelligent Vehicles; Proximal Policy Optimization; Reinforcement Learning; Reverse-Parking; Sim-to-real;

D O I：

10.1049/itr2.12614

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work presents the design and implementation of a reinforcement learning-based autonomous parking system where an agent is trained to reverse-park in a selected parking spot. The parking procedure is divided into three stages, and each stage has its corresponding surrogate objective that contributes to the overall parking process. The model solely depends on features extracted from a top-view image of the parking space. It has the advantage of potential deployment in smart parking buildings without refitting non-autonomous cars with modern sensors. The training was conducted offline on a simulation utilizing the proximal policy optimization algorithm. The model was then transferred and tested on a hardware prototype of the parking space. The results of the system were successful as the successful parking rate reached 100% with no collisions with any objects, and the fastest parking time reached 10 s. The testing was conducted on multiple samples and scenarios of the parking setup.

引用

页数：14

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