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通过认知计算模型在两阶段的接管系统中建模注意力分配和接管性能:考虑多个监控请求的作用

Lie Guo¹, Xu Wang¹, Linli Xu¹

¹School of Mechanical Engineering, Dalian University of Technology, Dalian, China.

|April 9, 2024

概括

这项研究开发了两阶段接管系统的认知模型,模拟不同监控请求 (MR) 下的驾驶员注意力和性能. 该模型准确地预测了驾驶员的行为,在自动驾驶中推进了人类因素建模.

科学领域:

认知科学认知科学
人与计算机的交互
汽车工程汽车工程

背景情况:

现有的认知模型在两个阶段的接管系统中不足以解决驾驶员的表现.
对于监控请求 (MR) 对这些系统中的驾驶员认知的影响的理解有限.

研究的目的:

使用QN-ACTR架构开发用于双阶段接管系统的认知计算模型.
模拟和分析由于不同的MR体验导致的驾驶员注意力分配和接管性能变化.

主要方法:

基于排队网络-思维理性的自适应控制 (QN-ACTR) 架构构建了一个新的认知模型.
整合了五个组成部分,代表着与驾驶员行为保持一致的独特认知过程.
通过实验验证该模型,测量了道路中心的时间和接管时间等指标.

主要成果:

该模型与实验数据显著一致 (R平方≥0.76,RMSE≤0.41,MAPE≤15%).
模型预测准确地捕获了驾驶员注意力分配和接管性能的变化.
该研究成功模拟了不同监控请求经验的影响.

结论:

开发的基于QN-ACTR的认知模型有效地模拟了两阶段接管系统中的驾驶员性能.
这些发现提供了对自动驾驶汽车转型中人类因素的宝贵见解.
这项研究为超越特定系统应用的更广泛的人类因素建模领域做出了贡献.

关键词:

认知计算模型认知计算模型在 QN-ACTR 架构中.驾驶员性能建模驾驶员性能建模监控请求监控请求接管系统的接管系统

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