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基于增强图形强化学习与车辆协作图形的混合交通的合作合并方法
Haifeng Guo1, Hongda Fu1, Dongwei Xu1
1Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了用于连接和自动驾驶汽车 (CAV) 的增强图形强化学习算法,以改善混合交通道合并中的合作决策. 通过有效地建模车辆相互作用,VCG-EGRL方法提高了安全性和交通流量.
科学领域:
- 智能运输系统 智能运输系统
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 互联和自动驾驶汽车 (CAV) 在混合交通环境中,合作感知和决策至关重要.
- 现有的方法不足以模拟异质代理之间的动态相互作用,风险拥堵和事故在坡道合并.
- 需要一个强大的系统来管理复杂的相互作用,以确保安全和高效的自动驾驶.
研究的目的:
- 提出一个增强图形强化学习算法 (VCG-EGRL),用于混合流量道场景中的合作合并决策.
- 开发一个车辆协作强度 (VCI) 模型来量化车辆交互动态.
- 创建一个局部-全球合作图,整合车辆到车辆和车辆到基础设施的通信.
主要方法:
- 设计了一个车辆协作强度 (VCI) 模型来捕捉交互动态.
- 使用VCI,V2V和V2I通信关系构建了局部-全球合作图.
- 使用图形卷积网络与科尔莫戈罗夫-阿诺德网络 (KANs) 进行特征提取 (GKAN).
- 优化了使用图形相互信息最大化的图形强化学习策略.
主要成果:
- VCG-EGRL算法在混合交通道合并场景中表现出卓越的性能.
- 实验结果显示,与基线模型相比,合并成功率,效率和稳定性显著改善.
- 该方法有效地模拟复杂的车辆相互作用和驾驶行为.
结论:
- 拟议的VCG-EGRL算法允许CAV在具有挑战性的混合交通环境中进行有效的合作合并.
- 集成VCI,图形神经网络和强化学习为智能交通控制提供了一个有前途的方法.
- 这项研究有助于在复杂的交通场景中实现更安全,更有效的自主导航.
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