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

doi:10.1109/TIV.2022.3197815

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving
	Tian, Yonglin1 ; Wang, Jiangong1 ; Wang, Yutong1 ; Zhao, Chen1 ; Yao, Fei 2; Wang, Xiao1,3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2022-09-01
卷号	7 期号:3 页码:456-465
通讯作者	Wang, Xiao(x.wang@ia.ac.cn)
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.
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
DOI	10.1109/TIV.2022.3197815
关键词[WOS]	INTELLIGENT ; VEHICLES ; NETWORK
收录类别	SCI
语种	英语
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]
项目资助者	Key-Area Research and Development Program of Guangdong Province ; Key Research and Development Program of Guangzhou ; National Natural Science Foundation of China
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:000873905600009
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：34[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/173211/50542
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wang, Xiao
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.North Automat Control Technol Inst, Taiyuan 030006, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.