计算机科学
移动边缘计算
虚拟化
强化学习
边缘计算
GSM演进的增强数据速率
建筑
计算机网络
智能交通系统
分布式计算
实时计算
服务器
云计算
人工智能
操作系统
运输工程
工程类
艺术
视觉艺术
作者
Bo Fan,Yuan Wu,Zhengbing He,Yanyan Chen,Tony Q. S. Quek,Xu Cheng
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2021-11-01
卷期号:35 (6): 194-201
被引量:41
标识
DOI:10.1109/mnet.201.2000768
摘要
With automated driving forthcoming, lane-changing for Connected and Automated Vehicles (CAVs) has received wide attention. The main challenge is that lane-changing requires not only local CAV control but also interactions with the surrounding traffic. Nevertheless, the Line-of-Sight (LoS) sensing range of the CAVs imposes severe limitations on lane-changing safety, and the lane-changing decision that is made based only on self-interest ignores its impact on the traffic flow efficiency. To overcome these difficulties, this article proposes a Digital Twin (DT) empowered mobile edge computing (MEC) architecture. With MEC, the sensing and computing capabilities of the CAVs can be strengthened to guarantee real-time safety. The virtualization and offline learning capabilities of the DT can be leveraged to enable the CAVs to learn from the experience of the physical MEC network and make lane-changing decisions via a ‘foresight intelligent’ approach. A case study of lane-changing is provided where the DT is constituted by a cellular automata based road traffic simulator coupled with a LTE-V based MEC network simulator. Deep reinforcement learning is adopted to train the lane-changing strategy and results validate the effectiveness of our proposed architecture.
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