基于模糊逻辑的速度指导策略,用于交叉路口的混合排队,具有通信延迟补偿和驾驶员反应时间建模的交叉路口
Xiaofeng Liu1,2, Menghua Yan3,4, Runjia Dai1,2
1School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin, 300222, China.
Scientific reports
|December 23, 2025
概括
本研究介绍了混合自动驾驶和人驾驶车辆在十字路口的速度指导策略,以改善交通流量和安全. 该系统有效地弥补通信延迟,并模拟驾驶员的反应,提高合作车辆基础设施系统的效率.
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.
- 控制系统 控制系统
背景情况:
- 合作车辆基础设施系统 (CVIS) 面临着与异质车辆行为和通信不确定性的挑战.
- 信号交叉点的高效交通管理对于整体网络性能至关重要.
- 连接和自动驾驶汽车 (CAV) 和人驾驶汽车 (HV) 的混合车队需要先进的协调策略.
研究的目的:
- 制定一个全面的速度指导战略,用于在信号交叉点的混合CAV-HV排队.
- 解决通信延迟和模拟驾驶员反应时间,以提高交通效率.
- 为了提高安全性和减少在混合自主的人类交通环境中的旅行时间.
主要方法:
- 开发了一个四维速度指导框架 (加速,减速,恒定速度,停止).
- 一个模糊的逻辑增强的史密斯预测器被用于动态V2X (车辆到一切) 延迟补偿.
- 创建了一个多因素驾驶员反应时间模型,考虑年龄,疲劳和认知负载.
主要成果:
- 在通信延迟 (0.5-2.0秒) 的情况下,最大加速度和速度误差降低了80.9%和62%.
- 驾驶员反应时间模型提高了轨迹预测准确度.
- 与固定参数方法相比,十字路口的吞吐量增加了约36%.
结论:
- 拟议的战略大大提高了混合交通中排队协调和交叉路口的效率.
- 综合方法通过共同模拟和实地实验证明了实际的有效性和安全性.
- 该系统减少了旅行时间,并在复杂的CVIS环境中改善了整体流量.
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