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Autonomous Driving Powered by 5G Enabled Mobile Edge Computing, LoPECS and Edge AI
Abstract
Autonomous driving technology demands high-performance computing and low-latency communication for real-time and reliable decision-making. Edge computing is a promising solution to meet these requirements by enabling autonomous vehicles to offload computation and communication tasks to nearby edge servers. This paper discusses the potential of edge computing for autonomous driving, particularly in the context of 5G enabled Mobile Edge Computing, low power edge computing systems, and edge AI. However, designing an efficient ecosystem for edge computing in autonomous vehicles poses challenges such as delivering enough computing power, processing heterogeneous data in real-time, and operating within strict energy consumption restrictions. To address these challenges, the paper presents various approaches done in past, including scheduling algorithms, security mechanisms, and deep learning techniques, to optimize the performance and reliability of edge computing. The challenges of efficient resource allocation, real-time communication, and security issues are also discussed. The paper concludes that edge computing, particularly 5G enabled Mobile Edge Computing, low power edge computing systems, and edge AI, has the potential to significantly enhance the performance of autonomous driving systems, improving computation and communication offloading, reducing network latency, and ensuring reliability and security.
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