As semi-automated vehicles (SAVs) become more common, ensuring effective human-vehicle interaction during control handovers remains a critical safety challenge. Existing studies often rely on single-session simulator experiments or naturalistic driving datasets, which often lack temporal context on drivers' cognitive and physiological states before takeover events. This study introduces a hybrid framework combining longitudinal mobile sensing with high-fidelity driving simulation to examine driver readiness in semi-automated contexts. In a pilot study with 38 participants, we collected 7 days of wearable physiological data and daily surveys on stress, arousal, valence, and sleep quality, followed by an in-lab simulation with scripted takeover events under varying secondary task conditions. Multimodal sensing, including eye tracking, fNIRS, and physiological measures, captured real-time responses. Preliminary analysis shows the framework's feasibility and individual variability in baseline and in-task measures; for example, fixation duration and takeover control time differed by task type, and RMSSD showed high inter-individual stability. This proof-of-concept supports the development of personalized, context-aware driver monitoring by linking temporally layered data with real-time performance.

翻译：随着半自动化车辆（SAVs）日益普及，确保控制权交接过程中人机交互的有效性仍是关键安全挑战。现有研究多依赖单次模拟器实验或自然驾驶数据集，这些数据往往缺乏对驾驶员接管事件前认知与生理状态的时间维度信息。本研究提出一种融合纵向移动感知与高保真驾驶仿真的混合框架，以评估半自动化情境下的驾驶员准备状态。在包含38名参与者的预实验中，我们收集了连续7天的可穿戴生理数据及关于压力、唤醒水平、效价和睡眠质量的日常问卷，随后在实验室模拟环境下设置脚本化接管事件，并引入不同次级任务条件。多模态感知技术（包括眼动追踪、功能性近红外光谱fNIRS和生理指标测量）实现了实时响应捕捉。初步分析表明该框架的可行性，并揭示了基线状态与任务状态测量的个体差异：例如，注视时长与接管控制时间因任务类型而异，而RMSSD显示出较高的个体间稳定性。这一概念验证通过将时间分层数据与实时表现相联结，为开发个性化、情境感知的驾驶员监控系统提供了理论支撑。