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Edge Computing for Autonomous Driving: Opportunities and Challenges This Article Surveys the Designs of Autonomous Driving Edge Computing Systems

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Edge Computing for Autonomous Driving: Opportunities and Challenges This Article Surveys the Designs of Autonomous Driving Edge Computing Systems Edge Computing for Autonomous Driving: Opportunities and Challenges This article surveys the designs of autonomous driving edge computing systems. In addition, it presents the security issues in autonomous driving as well as how edge computing designs can address these issues. By SHAOSHAN LIU , Senior Member IEEE,LIANGKAI LIU,JIE TANG, Member IEEE, BO YU, Member IEEE,YIFAN WANG, AND WEISONG SHI, Fellow IEEE ABSTRACT | Safety is the most important requirement for gent performance and energy constraints on the edge side. autonomous vehicles; hence, the ultimate challenge of design- With V2X, more research is required to define how vehi- ing an edge computing ecosystem for autonomous vehicles is cles cooperate with each other and the infrastructure. Last, to deliver enough computing power, redundancy, and secu- safety cannot be guaranteed when security is compromised. rity so as to guarantee the safety of autonomous vehicles. Thus, protecting autonomous driving edge computing systems Specifically, autonomous driving systems are extremely com- against attacks at different layers of the sensing and comput- plex; they tightly integrate many technologies, including sens- ing stack is of paramount concern. In this paper, we review ing, localization, perception, decision making, as well as the state-of-the-art approaches in these areas as well as explore smooth interactions with cloud platforms for high-definition potential solutions to address these challenges. (HD) map generation and data storage. These complexities KEYWORDS | Connected and autonomous vehicles (CAVs); impose numerous challenges for the design of autonomous edge computing; heterogeneous computing; security; vehicle- driving edge computing systems. First, edge computing sys- to-everything (V2X); vehicular operating system. tems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from I. INTRODUCTION different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have The design goal of autonomous driving edge computing very strict energy consumption restrictions. Thus, it is imper- systems is to guarantee the safety of autonomous vehicles. ative to deliver sufficient computing power with reasonable This is extremely challenging, as autonomous vehicles energy consumption, to guarantee the safety of autonomous need to process an enormous amount of data in real vehicles, even at high speed. Second, in addition to the edge time (as high as 2 GB/s) with extremely tight latency system design, vehicle-to-everything (V2X) provides redun- constraints [1]. For instance, if an autonomous vehicle dancy for autonomous driving workloads and alleviates strin- travels at 60 miles per hour (mph), and thus about 30 m of braking distance, this requires the autonomous driving system to predict potential dangers up to a few seconds Manuscript received February 10, 2019; revised April 6, 2019; accepted before they occur. Therefore, the faster the autonomous May 5, 2019. Date of publication June 24, 2019; date of current version August 5, 2019. (Corresponding author: Jie Tang.) driving edge computing system performs these complex S. Liu and B. Yu are with PerceptIn, Fremont, CA 94539 USA. computations, the safer the autonomous vehicle is. L. Liu and W. Shi are with the Department of Computer Science, Wayne State University, Detroit, MI 48202 USA. Specifically, autonomous driving systems are extremely J. Tang is with the School of Computer Science & Engineering, South China complex; they tightly integrate many technologies, includ- University of Technology, Guangzhou 510330, China (e-mail: ing sensing, localization, perception, decision making, [email protected]). Y. Wa n g is with Wayne State University, Detroit, MI 48202 USA, and also with as well as the smooth interaction with cloud platforms for the Institute of Computing Technology, Chinese Academy of Sciences, Beijing high-definition (HD) map generation and data storage [2]. 100190, China. These complexities pose many challenges for the design Digital Object Identifier 10.1109/JPROC.2019.2915983 of autonomous driving edge computing systems, just to 0018-9219 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. Vol. 107, No. 8, August 2019 |PROCEEDINGS OF THE IEEE 1697 Liu et al.: Edge Computing for Autonomous Driving: Opportunities and Challenges name a few. First, they need to enable the interactions between the functional modules with low overheads, and this requires a lightweight operating system. Second, they need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. In addition, since they are mobile systems, they often have very strict energy consumption restrictions. This requires a high-performance and energy- efficient hardware system. In addition to the edge system design, vehicle-to- everything (V2X) technologies should also get involved in the communication of the edge system. V2X provides redundancy for autonomous driving workloads; it also alleviates stress on the edge computing system. We believe this is a promising approach, but the key is to identify Fig. 1. Autonomous driving edge computing architecture. a sweet spot between the tradeoffs of fully relying on the edge computing system versus fully relying on the V2X infrastructure. Hence, exploring how V2X enables the latest progress on the designs of autonomous driving autonomous vehicles to cooperate with each other and edge computing systems is presented and discussed. In with the infrastructure remains an open research topic. Section IV, we focus on how V2X alleviates the stress on Having a high-performance and energy-efficient edge edge computing systems. In Section V, we review security computing system is not enough, as attackers may turn an issues in autonomous driving and how edge computing autonomous vehicle into a dangerous weapon by hacking designs address these issues. We conclude in Section VI. into any layer of the autonomous driving sensing and computing stack. More research is required to identify all II. AUTONOMOUS DRIVING possible attack surfaces as well as protection mechanisms TECHNOLOGIES before autonomous vehicles are released on public roads. As detailed in [3], autonomous driving is not one tech- To summarize, the overarching challenge of nology but rather an integration of many technologies. autonomous driving edge computing systems design As is shown in Fig. 2, the autonomous driving technol- is to efficiently integrate the functional modules, including ogy stack consists of three major subsystems: algorithms, interactions with edge servers and V2X infrastructure, including sensing, perception, and decision; a vehicular to process a massive amount of heterogeneous data in edge subsystem, including the operating system and hard- real time, within a limited energy budget, and without ware platform; and a cloud platform, including data stor- sacrificing the security of the users. In this paper, age, simulation, HD mapping, and deep learning model we review state-of-the-art approaches in these areas training. as well as explore potential solutions to address these challenges. A. Algorithm Subsystem Before delving into the details of the autonomous The algorithm subsystem extracts meaningful informa- driving edge computing system designs, let us first define tion from sensor raw data to understand its environment the autonomous driving computing ecosystem. As illus- and dynamically make decisions about its actions. trated in Fig. 1, each autonomous vehicle is equipped with an edge computing system, which integrates all the real-time functional modules, such as localization, percep- tion, planning and control, and so on. Then, each vehicle communicates with edge servers, and eventually with the central cloud, through existing 3G/4G/5G communica- tion networks. In addition, the vehicles can communicate with the road side units (RSUs) through either the 5G networks or the dedicated short-range communications (DSRC) networks; this is a typical vehicle-to-infrastructure (V2I) scenario. Moreover, the vehicles can communicate with each other through the DSRC networks, and this is a typical vehicle-to-vehicle (V2V) scenario. The remainder of this paper is organized as follows. In Section II, we review the background of autonomous driving technologies and help readers understand the com- plexities of autonomous driving systems. In Section III, Fig. 2. Autonomous driving technology stack. 1698 PROCEEDINGS OF THE IEEE | Vol. 107, No. 8, August 2019 Liu et al.: Edge Computing for Autonomous Driving 1) Sensing: Normally, an autonomous vehicle consists of on this combination for localization for three reasons: several major sensors. Since each type of sensor presents 1) the accuracy is only about one meter; 2) the GNSS advantages and drawbacks, the data from multiple sensors signal has multipath problems, which means that the must be combined. The sensor types include the following. signal may bounce off buildings and introduce more a) GNSS/IMU: The global navigation satellite system noise; and 3) GNSS requires an unobstructed view of the and inertial measurement unit (GNSS/IMU) system helps sky so it fails to work in closed environments such as autonomous vehicles localize themselves by reporting both tunnels. inertial updates and a global
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