在商业规模上优化自动驾驶汽车控制系统的可靠性,通过LIF dombi方法
Hanan Alolaiyan1, Misbah Hayat2, Umer Shuaib2
1Department of Mathematics, College of Science, King Saud University, Riyadh, Saudi Arabia.
Scientific reports
|October 31, 2024
概括
本研究引入了使用语言直觉模糊集 (LIFS) 处理自动驾驶汽车安全不确定性的新方法. 研究开发了新的聚合运算符和决策算法,提高了自主系统的可信度.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 自动驾驶汽车通过减少交通碰撞,提供了显著的安全和效率改进.
- 管理决策中的不确定性对于自主系统的可靠性至关重要.
- 语言直觉模糊集 (LIFS) 提供了一个处理语言不确定性的框架.
研究的目的:
- 为语言直觉模糊集 (LIFS) 开发新的聚合运算符.
- 为多个属性决策 (MADM) 问题创建高级分数和准确性函数.
- 通过改进的决策算法,提高自动驾驶汽车控制系统的可信度.
主要方法:
- 设计了语言直觉模糊的多比有序加权平均 (LIFDOWA) 和几何 (LIFDOWG) 运算符.
- 为基于LIF的MADM开发了创新的评分和准确性功能.
- 为含糊数据的MADM问题创建了一个专门的算法.
主要成果:
- 拟议的LIFDOWA和LIFDOWG运营商表现出基本的结构性质.
- 开发的分数和准确性功能有效地解决了MADM的复杂性.
- 该算法在选择自动驾驶汽车控制系统的最佳策略方面展示了实际应用.
结论:
- 基于LIF的新型聚合运算符和MADM算法增强了不确定性下的决策.
- 这些方法提供了一个强大的方法来提高自动驾驶汽车系统的可信度.
- 该研究验证了与现有方法相比拟的技术的有效性和实用性.
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