一种基于交叉重要性抽样的可靠性分析方法,用于在横风下对准错误的自动卡车车队的横向安全评估
Jilong Chen1, Feng Chen1, Suiyang Zhao1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao'an Road, Jiading, Shanghai 201804, China.
Accident; analysis and prevention
|April 16, 2025
概括
错位的自动驾驶卡车车队提高了道路的可持续性,但可能会带来安全风险. 交叉度重要抽样 (CE-IS) 有效地评估横向安全,使智能货运系统能够更好地管理风险.
科学领域:
- 运输工程 运输工程
- 智能运输系统 智能运输系统
- 道路铺路的可持续性 道路铺路的可持续性
背景情况:
- 自动卡车排队提供了效率,但传统的直线形状会造成道路损坏.
- 不对齐的队伍旨在通过横向分配负载来提高路面的可持续性.
- 随着排队的错位,特别是在横风下,出现了侧面安全问题.
研究的目的:
- 在横风条件下调查错位的自动卡车车队的横向安全性.
- 引入一种高效的计算方法来评估横向风险概率.
- 将拟议的方法与传统的蒙特卡洛模拟 (MCS) 进行比较.
主要方法:
- 使用交叉的重要性抽样 (CE-IS) 进行横向风险估计.在三辆卡车的排.
- 在风险评估中采用多个指标的随机分布.
- 将CE-IS性能与蒙特卡洛模拟 (MCS) 进行比较,以获得准确性和效率.
主要成果:
- 与MCS相比,CE-IS显著减少了计算时间,同时保持了高精度.
- 在一致的控制下,不对齐的排队比对齐的排队更容易发生车道侵占.
- 重组中间卡车的位置可以减少横向风险,尽管它可能仍然高于对齐的排队.
结论:
- CE-IS提供了一种高效准确的方法来量化车辆排队系统中的横向风险.
- 结果为改善横向安全和减轻协调的卡车车队对道路表面的影响提供了洞察力.
- CE-IS 方法支持智能货运系统的实时操作策略.
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