Edge Computing for Autonomous Driving: Opportunities and Challenges

By Shaoshan Liu, Liangkai Liu, Jie Tang, Bo Yu, Yifan Wang, Weisong Shi. Proceedings of the IEEE, 2019

652 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/afe5932da0c9f9f54e506a154f40341b3cabc035

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Citations in the corpus

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “prediction [73], [74], planning [75], [76], [77], [78], [79], [80], E2E [81], [82], control [83], [84], [85], [86], [87], [88], [89], system [90], [91], [92], [93], hardware [94], [95], [96], soft-”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The data processing tasks for autonomous driving mainly include perception, high-deﬁnition (HD) mapping, as well as SLAM [117], [302].”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “aset. For example, in driving automation from SAE, one of the critical differences between level 2 and level 3/4/5 is whether the vehicle can make decisions like overtaking or lane changing by itself [250]. In some instances, interacting with other human drivers becomes a big challenge because human”
F-cooper: feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds (2019, neutral): “An autonomous vehicle edge computing system consists of three layers: vehicle, edge, and cloud [18].”
Multi-Sensor Fusion in Automated Driving: A Survey (2020, background): “Reference [86] further points out that although many studies have proposed different heterogeneous computing architectures for multi-sensor data processing, edge computing is still needed to improve the computing power in the case of limited resources.”
Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021, background): “Furthermore, these SCI systems can be deployed with edge/cloud computing [102] to lower the bandwidth and memory requirements.”