混合车队的知识导向自学控制策略与延迟的知识导向自学控制策略
Jingyao Wang1, Huinian Wang2, Jian Song1
1School of Aerospace Engineering, Xiamen University, Xiamen, P. R. China.
Nature communications
|August 19, 2025
概括
本研究介绍了混合交通控制的知识导向的自我学习策略,提高了自动驾驶车辆的排队,尽管通信延迟和传统车辆的不可预测性. 它提高了交通稳定性,舒适性和能源效率,没有碰撞.
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
- 人工智能的人工智能
背景情况:
- 自动驾驶和传统车辆将在几十年内共存,这给混合交通管理带来了挑战.
- 连接自动驾驶汽车的通信延迟会降低车队控制性能.
- 传统车辆的异质性和随机性使交通流程复杂化.
研究的目的:
- 提出一个以知识为导向的自我学习混合排队控制策略.
- 在混合交通环境中提高道路吞吐量,燃料消耗和交通稳定性.
- 为应对通信延迟和传统车辆行为所带来的挑战.
主要方法:
- 整合动力波和纽埃尔的汽车追踪模型来预测传统车辆的行为.
- 从传统车辆中提取时间间隔和停车间隔等关键特征.
- 将以前的控制指令纳入软演员-关键算法的状态表示中,以处理延迟的信息.
主要成果:
- 在交通稳定性,乘客舒适性和能源消耗方面表现优于现有的方法.
- 证明了交通振荡的显著抑制.
- 在车辆合并和分离的场景中实现零碰撞率.
结论:
- 拟议的战略为互联自动驾驶汽车系统提供了可通用和可扩展的解决方案.
- 通过预测传统车辆轨迹并补偿通信延迟,有效地管理混合交通.
- 显著提高整体交通效率和安全.
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