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Edge Computing Assisted Autonomous Driving Using Artificial Intelligence

机译：边缘计算辅助自动驾驶使用人工智能

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The emergence of new vehicles generation such as connected and autonomous vehicles led to new challenges in the vehicular networking and computing managements to provide efficient services and guarantee the quality of service. The edge computing facility allows the decentralization of processing from the cloud to the edge of the network. In this paper, we design and propose an end-to-end, reliable and low latency communication architecture that allows the allocation of compute-intensive autonomous driving services, in particular autopilot, to shared resources on edge computing servers and improve the level of performance for autonomous vehicles. The reference architecture is used to design an Advanced Autonomous Driving (AAD) communication protocol between autonomous vehicles, edge computing servers, and the centralized cloud. Then, a mathematical programming approach using Integer Linear Programming (ILP) is formulated to model the autopilot chain resources Offloading at the network edge. Further, a deep reinforcement learning (DRL) approach is proposed to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Moreover, several scenarios are considered to quantify the behavior of the optimization approaches. We compare their efficiency in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated Edge Autopilots.
机译：新车辆的出现，如连通和自治车辆导致车辆网络和计算管理中的新挑战，以提供高效的服务，并保证服务质量。边缘计算设施允许将从云处理的处理到网络边缘。在本文中，我们设计并提出了端到端，可靠和低延迟的通信架构，允许分配计算密集型自主驾驶服务，特别是AutoPilot，以在边缘计算服务器上共享资源，并提高性能水平自治车辆。参考架构用于在自主车辆，边缘计算服务器和集中云之间设计高级自主驾驶（AAD）通信协议。然后，使用整数线性编程（ILP）的数学编程方法配制成在网络边缘卸载自动驾驶仪链资源。此外，提出了一种深度增强学习（DRL）方法来处理密集的自主车辆（IOAV）网络。此外，考虑了几种场景量化了优化方法的行为。我们在总边缘服务器利用率，总边缘服务器分配时间和成功分配的边沿自动驾驶仪方面进行比较它们的效率。

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