In vehicles with partial or conditional driving automation (SAE Levels 2-3), the driver remains responsible for supervising the system and responding to take-over requests. Therefore, reliable driver monitoring is essential for safe human-automation collaboration. However, most existing Driver Monitoring Systems rely on generalized models that ignore individual physiological variability. In this study, we examine the feasibility of personalized driver state modeling using non-intrusive physiological sensing during real-world automated driving. We conducted experiments in an SAE Level 2 vehicle using an Empatica E4 wearable sensor to capture multimodal physiological signals, including electrodermal activity, heart rate, temperature, and motion data. To leverage deep learning architectures designed for images, we transformed the physiological signals into two-dimensional representations and processed them using a multimodal architecture based on pre-trained ResNet50 feature extractors. Experiments across four drivers demonstrate substantial interindividual variability in physiological patterns related to driver awareness. Personalized models achieved an average accuracy of 92.68%, whereas generalized models trained on multiple users dropped to an accuracy of 54%, revealing substantial limitations in cross-user generalization. These results underscore the necessity of adaptive, personalized driver monitoring systems for future automated vehicles and imply that autonomous systems should adapt to each driver's unique physiological profile.

翻译：在具备部分或条件驾驶自动化功能的车辆中（SAE L2-L3级），驾驶员仍需负责监督系统并响应接管请求。因此，可靠的驾驶员监测对于安全的人机协同至关重要。然而，现有驾驶员监测系统多采用忽略个体生理差异的通用模型。本研究探究了真实道路自动驾驶场景下，利用非侵入式生理传感实现个性化驾驶员状态建模的可行性。我们基于SAE L2级车辆开展实验，使用Empatica E4可穿戴传感器采集包括皮电反应、心率、体温和运动数据在内的多模态生理信号。为利用面向图像的深度学习架构，我们将生理信号转换为二维表征，并通过基于预训练ResNet50特征提取器的多模态架构进行处理。针对四位驾驶员的实验表明，与驾驶员警觉性相关的生理模式存在显著个体差异。个性化模型平均准确率达92.68%，而基于多用户训练的通用模型准确率骤降至54%，揭示了跨用户泛化的显著局限。这些结果凸显了未来自动驾驶汽车需要具备自适应能力的个性化驾驶员监测系统，并表明自动驾驶系统应适应每位驾驶员独特的生理特征。