混合流量流的建模和分析,考虑驱动器随机性和CAV连接不确定性
Qi Zeng1, Siyuan Hao2, Nale Zhao2
1College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
更高的互联和自动驾驶汽车 (CAV) 透率提高了交通流动的稳定性. 然而,人类驾驶员的变化显著影响稳定性,即使有CAV连接的不确定性,突出了对强大的系统的需求.
科学领域:
- 运输工程 运输工程
- 交通流动动态 交通流动动态
- 自主系统 自主系统
背景情况:
- 将互联和自动驾驶汽车 (CAV) 集成到运输系统中,需要了解不确定性下的混合流量流动.
- 人驾驶汽车 (HV) 具有固有的随机性,而CAV面临连接不确定性 (传感器噪音,通信延迟).
研究的目的:
- 开发一个汽车追踪模型框架,分析驾驶员随机性和CAV连接性不确定性对混合流量流量的综合影响.
- 评估CAV透率和连接不确定性对交通稳定性的影响.
主要方法:
- 将经典的智能驾驶模型 (IDM) 扩展为兰格温型随机微分方程,以模拟人类驾驶员的随机性.
- 开发了一种用于CAV的多预测控制汽车追踪模型,包括传感器噪音,通信延迟和动态连接.
- 进行了广泛的数值模拟,以分析各种场景下的交通流动动态.
主要成果:
- 增加的CAV透率导致更稳定的交通流.
- 即使连接不确定性,CAV也可以稳定交通,但人类驾驶员的随机性大大降低了整体稳定性.
- 在纯粹的CAV环境中,传感器噪音和通信问题会影响稳定性;在混合流量中,HV不稳定性占主导地位.
结论:
- 人类驾驶员的变化是影响混合交通稳定的关键因素.
- 强大的传感器融合和错误补偿对于实现CAV潜力至关重要.
- 在混合交通环境中,减轻HV对CAV性能的不利影响至关重要.
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