Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

doi:10.1109/TIV.2022.3197815

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving
	Tian, Yonglin 3; Wang, Jiangong 3; Wang, Yutong 3; Zhao, Chen 3; Yao, Fei 2; Wang, Xiao 1,3
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2022-09-01
卷号	7 期号:3 页码:456-465
关键词	Transformers Autonomous vehicles Collaborative work Point cloud compression Trajectory Computational modeling Vehicle dynamics Cooperative autonomous driving Federated Vehicular Transformers Federation of Vehicular Transformers Vehicular Transformers
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3197815
通讯作者	Wang, Xiao(x.wang@ia.ac.cn)
英文摘要	Cooperative computing is promising to enhance the performance and safety of autonomous vehicles benefiting from the increase in the amount, diversity as well as scope of data resources. However, effective and privacy-preserving utilization of multi-modal and multi-source data remains an open challenge during the construction of cooperative mechanisms. Recently, Transformers have demonstrated their potential in the unified representation of multi-modal features, which provides a new perspective for effective representation and fusion of diverse inputs of intelligent vehicles. Federated learning proposes a distributed learning scheme and is hopeful to achieve privacy-secure sharing of data resources among different vehicles. Towards privacy-preserving computing and cooperation in autonomous driving, this paper reviews recent progress of Transformers, federated learning as well as cooperative perception, and proposes a hierarchical structure of Transformers for intelligent vehicles which is comprised of Vehicular Transformers, Federated Vehicular Transformers and the Federation of Vehicular Transformers to exploit their potential in privacy-preserving collaboration.
资助项目	Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; Key Research and Development Program of Guangzhou[202007050002] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[62173329]
WOS关键词	INTELLIGENT ; VEHICLES ; NETWORK
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:000873905600009
资助机构	Key-Area Research and Development Program of Guangdong Province ; Key Research and Development Program of Guangzhou ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50541]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Wang, Xiao
作者单位	1.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 2.North Automat Control Technol Inst, Taiyuan 030006, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Tian, Yonglin,Wang, Jiangong,Wang, Yutong,et al. Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2022,7(3):456-465.
APA	Tian, Yonglin,Wang, Jiangong,Wang, Yutong,Zhao, Chen,Yao, Fei,&Wang, Xiao.(2022).Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,7(3),456-465.
MLA	Tian, Yonglin,et al."Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 7.3(2022):456-465.