Edge Computing for Autonomous Driving: Opportunities and Challenges

doi:10.1109/JPROC.2019.2915983

	Edge Computing for Autonomous Driving: Opportunities and Challenges
	Liu, Shaoshan 1; Liu, Liangkai 2; Tang, Jie 3; Yu, Bo 1; Wang, Yifan 4,5; Shi, Weisong 2
	2019-08-01
发表期刊	PROCEEDINGS OF THE IEEE
ISSN	0018-9219
卷号	107 期号:8 页码:1697-1716
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.
关键词	Connected and autonomous vehicles (CAVs) edge computing heterogeneous computing security vehicle-to-everything (V2X) vehicular operating system
DOI	10.1109/JPROC.2019.2915983
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000497973300013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：361[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14965
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.