Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving

doi:10.1109/TIV.2023.3285592

	Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving
	Zhang, Haobo 1; Yang, Ziang 1; Tian, Yonglin2 ; Zhang, Hongliang 1; Di, Boya 1; Song, Lingyang 1,3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2023-08-01
卷号	8 期号:8 页码:4031-4046
通讯作者	Song, Lingyang(lingyang.song@pku.edu.cn)
摘要	Simultaneous Localization and Mapping (SLAM) utilizing millimeter-wave (mmWave) radars is widely recognized as an essential component for autonomous driving applications. In this article, we present a Reconfigurable Holographic Surface (RHS)-aided SLAM system, incorporating federated learning. The hardware cost of autonomous driving systems can be significantly reduced by replacing the expensive phased array antennas, traditionally used in mmWave radars, with the low-cost RHS metasurface antenna. Furthermore, multiple vehicles can collaborate through the federated learning framework, obtaining additional sensed data to enhance SLAM performance. However, the distinctive radiation structure of the RHS and the information exchange within the federated learning framework introduce complexities to the overall SLAM system design. To address these challenges, we propose a multi-vehicle SLAM protocol that regulates RHS-based radar sensing and data processing across multiple vehicles. Additionally, we design algorithms for RHS radiation optimization and federated learning-based localization and mapping. Simulation results demonstrate the efficacy of the proposed approach when compared to existing phased array-based and non-cooperative schemes.
关键词	Autonomous driving federated learning metasurface reconfigurable holographic surface simultaneous localization and mapping
DOI	10.1109/TIV.2023.3285592
关键词[WOS]	SIMULTANEOUS LOCALIZATION ; RADAR
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFE0111900] ; National Natural Science Foundation of China[62271012] ; National Natural Science Foundation of China[61941101] ; Beijing Natural Science Foundation[L212027] ; Beijing Natural Science Foundation[4222005] ; State Key Laboratory of Advanced Optical Communication Systems Networks, China ; Science and Technology Innovation Program of Hunan Provionce[2022RC4024]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; State Key Laboratory of Advanced Optical Communication Systems Networks, China ; Science and Technology Innovation Program of Hunan Provionce
WOS研究方向	Computer Science ; Engineering ; Transportation
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001075333800006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53039
专题	多模态人工智能系统全国重点实验室
通讯作者	Song, Lingyang
作者单位	1.Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Haobo,Yang, Ziang,Tian, Yonglin,et al. Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(8):4031-4046.
APA	Zhang, Haobo,Yang, Ziang,Tian, Yonglin,Zhang, Hongliang,Di, Boya,&Song, Lingyang.(2023).Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(8),4031-4046.
MLA	Zhang, Haobo,et al."Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.8(2023):4031-4046.