基于毕达哥拉斯的哈马赫聚合的车载增强现实头顶显示器的层次特征评估和决策
Hongru Chen1,2, Weixing Wang1,2, Jiaqi Gao1,2
1School of Mechanical Engineering, Guizhou University, Guiyang, China.
Annals of the New York Academy of Sciences
|January 27, 2026
概括
本研究引入了一种新的决策方法,用于评估汽车增强现实头顶显示器 (AR-HUD),提高智能驾驶系统的性能评估准确性和稳定性.
科学领域:
- 工程 工程师 工程师 工程师
- 人与计算机的交互
- 模糊的集合理论 模糊的集合理论
背景情况:
- 汽车增强现实头顶显示器 (AR-HUD) 在智能驾驶系统中正在迅速发展.
- 由于数据异质性和认知不确定性,评估AR-HUD性能具有挑战性.
研究的目的:
- 开发一个全面的决策方法,用于多维AR-HUD性能评估.
- 在绩效评估中解决数据异质性和认知不确定性.
主要方法:
- 一个新的等级评估框架,将静态基本信息层 (SBIL) 和动态AR融合层 (DAFL) 分开.
- 毕达哥拉斯模糊集合和哈马赫信心加权聚合运算符 (PyHa-CWAO) 的集成.
- 应用DEMATEL用于因果关系和VIKOR用于多标准排名,形成PyHa-CWAO-DEMATEL-VIKOR模型.
- 建议使用超标趋势-信心融合得分模型来捕捉犹和信心.
主要成果:
- 与既有方法相比,拟议的模型表明排名一致性提高了14%.
- 一个驾驶模拟和眼睛跟踪实验验证了使用10个AR-HUD原型和12个指示器的模型.
- 该模型被证明是稳固的,并且在AR-HUD评估中具有实际价值.
结论:
- 开发的决策工具对于AR-HUD和其他扩展现实接口来说是准确的,有弹性的和可扩展的.
- 这项工作丰富了模糊决策理论,并为虚拟人机交互优化提供了方法论基础.
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