基于深度强化学习的多车道混合交通道融合战略.
Tong Zhou1,2, Yuzhao Huang1, Yudan Tian1
1College of Big Data and Internet of Things, Chongqing Vocational Institute of Engineering, Chongqing, China.
PloS one
|September 18, 2025
概括
本研究介绍了一种用于多车道交通的改进的对决双DQN (D3QN) 上坡道合并策略 (IDS). 新的车辆协调系统 (VCS) + IDS显著提高了上坡道合并的效率和安全性.
科学领域:
- 智能运输系统 智能运输系统
- 交通工程是交通工程.
- 在交通控制中进行强化学习.
背景情况:
- 在路面上合并对于混合交通控制至关重要,但当前的方法往往忽视了多车道协调.
- 现有的研究主要优化单车道坡道合并,忽视复杂的多车道交通动态.
研究的目的:
- 为多车道交通流量制定先进的上坡路合并策略.
- 为了提高车辆协调和安全,在合并机动.
主要方法:
- 提议了一种改进的决斗双DQN (D3QN) 在坡道上的合并策略 (IDS),与正弦函数集成.
- 建立了车辆协调系统 (VCS) 以指导多车道合并.
- 实施了一种行动掩盖机制,以确保战略开发期间的安全勘探.
- 在不同的交通密度下使用SUMO模拟验证了战略.
主要成果:
- 在VCS+IDS战略下,实现了98.62%的合并完成率和98.11%的任务完成率,达到1200辆车辆/车道/小时.
- 与传统的D3QN相比,完成率显著提高了11.08%和10.79%.
- 有效引导车辆在交通流程中找到合适的空隙,提高安全性和效率.
结论:
- 拟议的VCS + IDS战略对于优化在多车道交通中路面上的合并是有效的.
- 整合D3QN和正弦函数为交通协调提供了一个强大的方法.
- 这种方法在处理复杂的合并场景方面比传统的D3QN有了显著的进步.
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