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Edge Computing for Autonomous Driving: Opportunities and Challenges

文献类型：期刊论文

作者Liu, Shaoshan1; Liu, Liangkai2; Tang, Jie3; Yu, Bo1; Wang, Yifan4,5; Shi, Weisong2
刊名PROCEEDINGS OF THE IEEE
出版日期2019-08-01
卷号107期号:8页码:1697-1716
关键词Connected and autonomous vehicles (CAVs) edge computing heterogeneous computing security vehicle-to-everything (V2X) vehicular operating system
ISSN号0018-9219
DOI10.1109/JPROC.2019.2915983
英文摘要Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.
WOS研究方向Engineering
语种英语
WOS记录号WOS:000497973300013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/14964]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tang, Jie
作者单位1.PerceptIn, Fremont, CA 94539 USA
2.Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA
3.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510330, Guangdong, Peoples R China
4.Wayne State Univ, Detroit, MI 48202 USA
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shaoshan,Liu, Liangkai,Tang, Jie,et al. Edge Computing for Autonomous Driving: Opportunities and Challenges[J]. PROCEEDINGS OF THE IEEE,2019,107(8):1697-1716.
APA Liu, Shaoshan,Liu, Liangkai,Tang, Jie,Yu, Bo,Wang, Yifan,&Shi, Weisong.(2019).Edge Computing for Autonomous Driving: Opportunities and Challenges.PROCEEDINGS OF THE IEEE,107(8),1697-1716.
MLA Liu, Shaoshan,et al."Edge Computing for Autonomous Driving: Opportunities and Challenges".PROCEEDINGS OF THE IEEE 107.8(2019):1697-1716.

入库方式： OAI收割

来源：计算技术研究所

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