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

doi:10.1109/TIV.2023.3285592

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving
	Zhang, Haobo 2; Yang, Ziang 2; Tian, Yonglin 1; Zhang, Hongliang 2; Di, Boya 2; Song, Lingyang 2,3
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-08-01
卷号	8 期号:8 页码:4031-4046
关键词	Autonomous driving federated learning metasurface reconfigurable holographic surface simultaneous localization and mapping
ISSN号	2379-8858
DOI	10.1109/TIV.2023.3285592
通讯作者	Song, Lingyang(lingyang.song@pku.edu.cn)
英文摘要	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.
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]
WOS关键词	SIMULTANEOUS LOCALIZATION ; RADAR
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001075333800006
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53038]
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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, 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.