连接和自动化车辆的驾驶循环的有效性评估:节点重量和驾驶可靠性
Junlian Yan1,2, Daowen Zhang1,2,3, Qirui Luo4
1School of Automobile and Transportation, Xihua University, Chengdu, China.
PloS one
|June 23, 2025
概括
本研究介绍了连接和自动驾驶汽车 (CAV) 驾驶循环的网络模型,集成了观察,定向,决策和行动 (OODA) 概念. 拟议的评估方法提高了CAV的系统优化和驾驶安全.
科学领域:
- 汽车工程 汽车工程
- 网络科学 网络科学
- 系统工程 系统工程
背景情况:
- 连接和自动驾驶汽车 (CAV) 是汽车行业的一个重大趋势.
- 有效的网络建模和驱动循环评估对于CAV开发至关重要.
- 现有的模型可能无法完全捕捉到CAV驾驶过程的复杂性.
研究的目的:
- 为CAV的驱动循环构建一个网络模型.
- 开发一种方法来评估这种驾驶循环模型的有效性.
- 为了优化CAV系统的性能,驾驶安全和可靠性.
主要方法:
- 驾驶过程与观察,定向,决策和行动 (OODA) 循环概念的整合.
- 解释性结构模型 (ISM) 和复杂网络理论的应用来衡量节点的重要性.
- 利用模糊评估来确定驾驶可靠性和全面的节点重量.
- 使用确定节点权重进行有效性评估,增强信息模型.
主要成果:
- 成功建立了CAV驱动循环的新型网络模型.
- 提出了一种强大的方法来评估驱动循环的有效性,考虑到节点的重要性和可靠性.
- 拟议的方法在优化CAV网络模型方面表现出有效性,可用于各种场景.
结论:
- 开发的网络模型和评估方法为CAV驾驶循环分析提供了科学基础.
- 这种方法促进了CAV系统的有效规划,建模,评估和优化.
- 这些发现有助于提高自动驾驶技术的安全性和可靠性.
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