强化学习
计算机科学
能源消耗
温室气体
流量(计算机网络)
车辆动力学
交通拥挤
智能交通系统
运输工程
分布式计算
人工智能
汽车工程
工程类
计算机网络
生物
电气工程
生态学
作者
Sai Krishna Sumanth Nakka,Behdad Chalaki,Andreas A. Malikopoulos
标识
DOI:10.23919/acc53348.2022.9867314
摘要
The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and automated vehicles (CAVs), have the potential to directly address these issues and improve transportation network efficiency and safety. In this paper, we consider a highway merging scenario and propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We use a decentralized form of the actor-critic approach to deep reinforcement learning—multi-agent deep deterministic policy gradient. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving.
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