In real-world driving scenarios, multiple states occur simultaneously due to individual differences and environmental factors, complicating the analysis and estimation of driver states. Previous studies, limited by experimental design and analytical methods, may not be able to disentangle the relationships among multiple driver states and environmental factors. This paper introduces the Double Machine Learning (DML) analysis method to the field of driver state analysis to tackle this challenge. To train and test the DML model, a driving simulator experiment with 42 participants was conducted. All participants drove SAE level-3 vehicles and conducted three types of cognitive tasks in a 3-hour driving experiment. Drivers' subjective cognitive load and drowsiness levels were collected throughout the experiment. Then, we isolated individual and environmental factors affecting driver state variations and the factors affecting drivers' physiological and eye-tracking metrics when they are under specific states. The results show that our approach successfully decoupled and inferred the complex causal relationships between multiple types of drowsiness and cognitive load. Additionally, we identified key physiological and eye-tracking indicators in the presence of multiple driver states and under the influence of a single state, excluding the influence of other driver states, environmental factors, and individual characteristics. Our causal inference analytical framework can offer new insights for subsequent analysis of drivers' states. Further, the updated causal relation graph based on the DML analysis can provide theoretical bases for driver state monitoring based on physiological and eye-tracking measures.

翻译：在真实驾驶场景中，由于个体差异与环境因素，多种驾驶状态常同时发生，这使驾驶状态的分析与评估变得复杂。既往研究受限于实验设计与分析方法，往往难以厘清多重驾驶状态与环境因素间的关联。本文引入双重机器学习（DML）分析方法至驾驶状态分析领域以应对这一挑战。为训练与测试DML模型，我们开展了包含42名参与者的驾驶模拟器实验。所有参与者在3小时驾驶实验中操控SAE L3级自动驾驶车辆，并执行三类认知任务。实验全程采集了驾驶员的主观认知负荷与困倦水平。随后，我们分离了影响驾驶状态变化的个体与环境因素，以及驾驶员处于特定状态下影响其生理与眼动指标的因素。结果表明，我们的方法成功解耦并推断了多种类型困倦与认知负荷之间的复杂因果关系。此外，我们识别了在多重驾驶状态共存时以及受单一状态影响（排除其他驾驶状态、环境因素及个体特征干扰）下的关键生理与眼动指标。我们的因果推断分析框架可为后续驾驶状态分析提供新的见解。进一步地，基于DML分析更新的因果关系图谱可为基于生理与眼动测量的驾驶状态监测提供理论基础。