通过大规模整合计算心理理论来解释人类在道路上的互动
Gustav Markkula1,2, Yi-Shin Lin1, Aravinda Ramakrishnan Srinivasan1
1Institute for Transport Studies, University of Leeds, LS2 9JT Leeds, UK.
PNAS nexus
|June 22, 2023
概括
这项研究将心理学理论整合到人类与道路用户互动的计算模型中. 该模型成功地解释和预测了复杂的驾驶员-行人行为,这对于自动化汽车开发至关重要.
科学领域:
- 认知科学 认知科学
- 计算心理学 计算心理学
- 人与计算机的交互
背景情况:
- 人类道路使用者使用复杂的沟通和互动能力.
- 现有的计算模型缺乏捕捉这些行为范围.
- 了解这些相互作用对于自动化车辆和人类的安全共存至关重要.
研究的目的:
- 开发一个用于建模人类道路用户互动的计算框架.
- 将各种心理理论整合到一个统一的模型中.
- 为了解释司机与行人互动中以前无法解释的现象.
主要方法:
- 心理学中的通用计算理论 (贝叶斯感知,思想理论,游戏理论,决策).
- 将这些理论集成到一个新的计算框架中.
- 将模型与自然主义的司机-行人交叉路口数据进行验证.
主要成果:
- 综合模型与更简单的版本不同,解释了以前无法解释的司机与行人交互现象.
- 该模型成功预测了未见数据集中的相互作用结果.
- 证明了通用心理理论的现实世界的适用性.
结论:
- 人类道路使用是心理学的累积理论构建的合适领域.
- 在道路交通中人类互动的复杂性对自动化汽车的开发和测试产生了重大影响.
- 开发的计算框架为理解和建模这些相互作用提供了一个强大的工具.
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