Journal ArticleDOI
Edge Computing for Autonomous Driving: Opportunities and Challenges
Shaoshan Liu,Liangkai Liu,Jie Tang,Bo Yu,Yifan Wang,Weisong Shi +5 more
- Vol. 107, Iss: 8, pp 1697-1716
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TLDR
In this paper, the authors review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges, including providing enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles.Abstract:
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.read more
Citations
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Journal ArticleDOI
Multi-Sensor Fusion in Automated Driving: A Survey
TL;DR: The current situation of multi-sensor fusion in the automated driving process is analyzed to provide more efficient and reliable fusion strategies and provide some suggestions for further improvement in the future.
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Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
TL;DR: This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations.
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Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications.
TL;DR: In this article, a review of recent advances in Snapshot compressive imaging hardware, theory and algorithms, including both optimization-based and deep learning-based algorithms, is presented.
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Service Offloading With Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing
Xiaolong Xu,Bowen Shen,Sheng Ding,Gautam Srivastava,Muhammad Bilal,Mohammad Reza Khosravi,Varun G. Menon,Mian Ahmad Jan,Maoli Wang +8 more
TL;DR: A service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing, which leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning.
Journal ArticleDOI
Snapshot Compressive Imaging: Theory, Algorithms, and Applications
TL;DR: In this paper, the authors review recent advances in Snapshot Compressive Imaging (SCI) hardware, theory, and algorithms, including both optimization-based and deep learning-based algorithms.
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