基于遗传算法和故障树分析的多因素变化车道风险弹性评估模型的研究
Qiang Luo1, Haihui Wang1, Junheng Yang1
1School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China.
本研究引入了基于弹性的车道更换风险评估框架,提高了恶劣天气中的安全性. 新的指标和基因算法优化的模型改善了智能运输系统的实时风险预测.
科学领域:
- 智能运输系统 智能运输系统
- 道路安全工程 道路安全工程
- 弹性工程 弹性工程
背景情况:
- 现有的车道更换风险模型在动态天气适应和现实世界的验证方面扎.
- 需要一种弹性工程方法来评估系统在干扰下保持安全的能力.
研究的目的:
- 开发一个以天气为导向,以弹性为导向的车道更改风险评估框架.
- 引入新的指标,即风险暴露水平 (REL) 和风险严重程度水平 (RSL).
- 改善动态风险适应不利的天气条件.
主要方法:
- 集成了一个基因算法 (GA) 校准的停止视觉距离 (SSD) 模型与断层树分析 (FTA).
- 利用CitySim自然驾驶数据集在各种天气条件下提取车道更换事件.
- 采用统计测试来确定影响车道更换风险的关键因素.
主要成果:
- 优化制动参数使用GA用于在不同天气条件下自适应的风险值.
- 量化冲突概率 (REL) 和严重程度 (RSL) 用于全面的系统稳定性评估.
- 与基线相比,模型健康状况得到了42.38%的改善.
结论:
- 开发的框架有效地捕捉了实时变车道风险,即使在恶劣的天气中.
- 该模型为智能运输系统的积极安全管理提供了可靠的工具.
- 以弹性为导向的方法增强了对智能运输系统的决策支持.
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