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

Yiang Wu; Qiong Wu; Pingyi Fan; Kezhi Wang; Wen Chen; Guoqiang Mao; Khaled B. Letaief

cs.LGMar 5, 2026

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

Organizations: Jiangnan University · School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University · Zhuhai Fudan Innovation Institute · Tsinghua University · Brunel University of London · Shanghai Jiao Tong University · Southeast University · The Hong Kong University of Science and Technology

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.

Figures & tables

Fig. 1 : The system architecture of U-Parking.

Panel of Figure 2 — Fig. 2 : Demonstration.

Method	Euclidean Error (m)		DTW (m)
Method	Max	Mean	Normalized Mean
UWB	2.354	0.240	0.284
UWB+EKF	1.692	0.190	0.212
UWB+IAEKF	0.653	0.138	0.169
Integrated Improvement (ours)	0.517	0.118	0.133

TABLE I : Comparison of Tracking Accuracy

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End-to-End Autonomous Driving Autonomous Parking Autonomous Driving Autonomous Driving Planning

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End-to-end autonomous parking has emerged as a critical task within the realm of autonomous driving. However, existing methods suffer from black-box characteristics, lacking high-level semantic understanding and interpretability, which impedes the realization of seamless long-distance autonomous parking from the road to the target spot. To address these limitations, we propose ParkingTransformer, a novel framework that leverages multi-view perception and the scene understanding capability of Large Language Models (LLMs). By combining trajectory queries with LLMs implicit state features, our method interacts directly with historical information and raw sensor data to output planning trajectories, eliminating the need for dense Bird's-View (BEV) representations. To compensate for the inadequate spatial reasoning ability of LLMs, we introduce 3D positional encoding to explicitly inject spatial geometric awareness. Furthermore, a fixed-window streaming mechanism is designed for historical information processing, significantly improving long-term temporal processing efficiency and inference speed. Additionally, a coarse-to-fine decoding strategy is employed to progressively enhance trajectory precision. Extensive closed-loop experiments are conducted on the CARLA simulator and real-world vehicle platforms. The results demonstrate that our method achieves a driving score of 61.32 in CARLA simulator and an average success rate of 88.70% in real-world experiments, validating the feasibility and effectiveness of the proposed algorithms.

Abstract

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