中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

文献类型：期刊论文

作者Tian, Yonglin3; Wang, Jiangong3; Wang, Yutong3; Zhao, Chen3; Yao, Fei2; Wang, Xiao1,3
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期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
DOI10.1109/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.
WOS关键词INTELLIGENT ; VEHICLES ; NETWORK
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研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:000873905600009
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。