论文标题
通过边缘驱动的辅助驾驶方法
An Edge-powered Approach to Assisted Driving
论文作者
论文摘要
连接车辆的汽车服务是新产生移动网络以及边缘计算范式应用程序的主要领域之一。在本文中，我们研究了一种将分布式车辆网络与网络边缘集成的系统体系结构，目的是优化车辆旅行时间。然后，我们提出了一个基于队列的系统模型，该模型允许对车辆流进行优化，并将其适用于两个相关服务，即车道更改/合并（代表合作辅助驾驶）和导航。此外，我们引入了一种称为瓶颈狩猎（BH）的有效算法，能够在线性时间内制定高质量的流动策略。我们通过结合NS-3和SUMO的全面和现实的模拟框架来评估所提出的系统体系结构的性能和BH的性能。在实际情况下得出的结果表明，与单个车辆做出的决策相比，我们的解决方案提供的旅行时间短得多。
Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.
