对山地道入口的智能连接车辆的交通流稳定性的模拟分析,考虑非线性合效应
Mengmeng Duan1,2,3
1College of Civil Engineering, Hefei University of Technology, Hefei, 230009, China. ypssb0@163.com.
Scientific reports
|October 24, 2025
概括
一个新的框架通过管理灯光和斜率变化来提高道的交通安全. 它减少了40%的速度变化和碰撞风险,同时保护智能互联汽车 (ICV) 混合交通中的数据隐私.
科学领域:
- 智能运输系统 智能运输系统
- 控制工程 控制工程 控制工程
- 机器学习 机器学习
背景情况:
- 由于灯光和斜率的突然变化,道带来了独特的交通挑战.
- 现有的控制方法与动态环境,数据隐私和适应性作斗争.
- 混合交通流,包括智能互联汽车 (ICV),加剧了这些问题.
研究的目的:
- 开发一个综合框架,用于环境感知和车辆控制道.
- 为应对动态环境,数据隐私和道交通控制中的适应性所带来的挑战.
- 在复杂的道场景中提高交通流动稳定性和安全性.
主要方法:
- 开发了MF3DQN-TF控制框架,整合了环境感知和车辆控制.
- 建立了可量化的风险信号的光梯度和斜率阻力之间的相关模型.
- 实施了智能重量分配机制和分布式培训架构,用于保护隐私的知识共享.
主要成果:
- 与传统方法相比,速度波动幅度减少约40%.
- 与集中式DQN相比,降低了62.5%的高风险时间到碰撞 (TTC) 事件.
- 与FedProx相比,通信量减少了46.2%,提高了数据隐私.
结论:
- MF3DQN-TF框架显著提高了道的交通流动稳定性和安全性.
- 该框架通过协同处理风险和车辆状态,为复杂的道环境提供了新的技术解决方案.
- 这种方法为混合交通道 (包括ICV) 的安全控制提供了新的途径.
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