基于最优监管控制理论，预测驾驶中人机协同下的多任务行为。
Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control
- 构建认知计算模型，模拟不同自动化水平下驾驶者多任务分配。
- 预测直线路段注视时间更长，弯道时更短，且辅助系统延长注视时长。
- 适用于智能座舱设计与自动驾驶人机协同研究，适合交通人因领域读者。
现代驾驶融合交互技术，易分散注意力并增加事故风险。本文提出一种基于最优监管控制理论的计算认知模型，可模拟驾驶过程中的人类多任务行为。该模型能预测在不同驾驶需求、交互任务及自动化水平下，多任务如何动态调整。与以往模型不同，它考虑了不同自动化程度下的情境依赖性多任务特征。模型预测：在直线路段车内注视时间更长，在弯道时更短；车道居中辅助等驾驶辅助系统会延长注视时长，且其影响受环境需求调节。模型在两个实证数据集上验证有效，为理解车内技术演进中的驾驶员多任务行为提供了新视角。
原文摘要 · Abstract (English)
Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.
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