论文标题
通过连接和自动化的车辆抑制停下来的交通 - 一种深厚的增强学习方法
Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach
论文作者
论文摘要
停车场的交通构成了越野系统的许多挑战，但其形成和机制仍在勘探中。本研究没有使用分析模型，而是采用强化学习来控制CAV的行为，并将单个CAV放在车队的第二位置，目的是抑制车队领导者的速度振荡，并帮助跟随人类驾驶员采用更平稳的驾驶行为。结果表明，我们的控制器可以将CAV的Sppper振荡降低54％和8％-28％的人，而人类驱动的车辆的振荡。还可以观察到大量的燃油消耗。此外，结果表明，如果骑士选择略微极度地行为，他们可能会充当交通稳定器。
Stop-and-go traffic poses many challenges to tranportation system, but its formation and mechanism are still under exploration.however, it has been proved that by introducing Connected Automated Vehicles(CAVs) with carefully designed controllers one could dampen the stop-and-go waves in the vehicle fleet. Instead of using analytical model, this study adopts reinforcement learning to control the behavior of CAV and put a single CAV at the 2nd position of a vehicle fleet with the purpose to dampen the speed oscillation from the fleet leader and help following human drivers adopt more smooth driving behavior. The result show that our controller could decrease the spped oscillation of the CAV by 54% and 8%-28% for those following human-driven vehicles. Significant fuel consumption savings are also observed. Additionally, the result suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.
