[2603.04898] U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning

[2603.04898] U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning

arXiv - Machine Learning 3 min read

About this article

Abstract page for arXiv paper 2603.04898: U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning

Computer Science > Machine Learning arXiv:2603.04898 (cs) [Submitted on 5 Mar 2026] Title:U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning Authors:Yiang Wu, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen, Guoqiang Mao, Khaled B. Letaief View a PDF of the paper titled U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning, by Yiang Wu and 5 other authors View PDF HTML (experimental) Abstract:This demonstration presents U-Parking, a distributed Ultra-Wideband (UWB)-assisted autonomous parking system. By integrating Large Language Models (LLMs)-assisted planning with robust fusion localization and trajectory tracking, it enables reliable automated parking in challenging indoor environments, as validated through real-vehicle demonstrations. Comments: Subjects: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2603.04898 [cs.LG]   (or arXiv:2603.04898v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.04898 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qiong Wu [view email] [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB) Full-text links: Access Paper: View a PDF of the paper titled U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning, by Yiang Wu and 5 other authorsView PDFHTML (experimental)TeX Source view li...

Originally published on March 06, 2026. Curated by AI News.

Related Articles

Llms

[R] GPT-5.4-mini regressed 22pp on vanilla prompting vs GPT-5-mini. Nobody noticed because benchmarks don't test this. Recursive Language Models solved it.

GPT-5.4-mini produces shorter, terser outputs by default. Vanilla accuracy dropped from 69.5% to 47.2% across 12 tasks (1,800 evals). The...

Reddit - Machine Learning · 1 min ·
Llms

built an open source CLI that auto generates AI setup files for your projects just hit 150 stars

hey everyone, been working on this side project called ai-setup and just hit a milestone i wanted to share 150 github stars, 90 PRs merge...

Reddit - Artificial Intelligence · 1 min ·
Llms

built an open source tool that auto generates AI context files for any codebase, 150 stars in

one of the most tedious parts of working with AI coding tools is having to manually write context files every single time. CLAUDE.md, .cu...

Reddit - Artificial Intelligence · 1 min ·
Find out what’s new in the Gemini app in March's Gemini Drop.
Llms

Find out what’s new in the Gemini app in March's Gemini Drop.

Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.

AI Tools & Products · 1 min ·
More in Llms: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime