主动推理模型的AV收购:模型参数与信任,情境意识和疲劳相关
Ran Wei1, Anthony D McDonald2, Ranjana K Mehta2
1Texas A&M University, USA.
Human factors
|November 1, 2024
概括
积极推断模型准确地预测自动驾驶汽车中的驾驶员接管. 模型参数与认知疲劳,情境意识和信任有关,有助于更安全的系统设计.
科学领域:
- 认知科学 认知科学
- 人与计算机的交互
- 自动化系统自动化系统
背景情况:
- 人类驾驶员和自动化之间的控制过渡存在安全风险.
- 现有的驾驶员行为模型缺乏足够的整合人类因素.
- 积极推理理论为自动化中对人类认知进行建模提供了一个定量框架.
研究的目的:
- 评估主动推断模型来预测自动驾驶汽车的驾驶员接管情况.
- 评估模型参数与自我报告的认知疲劳,信任和情境意识之间的联系.
- 提高更安全,以人为中心的自动化系统的设计.
主要方法:
- 使用驾驶模拟数据开发了一个主动推理模型.
- 与观察到的收购业绩相比,验证的模型预测.
- 采用贝叶斯回归来将模型参数与心理状态相关联.
主要成果:
- 积极推断模型准确地预测了驾驶接管时间.
- 增加的认知疲劳与收购决策的不确定性更高相关.
- 增强的情境意识与更精确的环境理解有关.
- 更大的信任与环境条件的变化增加有关.
结论:
- 结果支持主动推理理论,并将驱动器状态与模型参数连接起来.
- 积极推断框架可以为自动驾驶汽车的设计和验证提供信息.
- 这种方法有助于对设计参数进行校准,以提高安全性.
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