AI 中文总结
研究在驾驶模拟器实验中探究危险类型、次要任务等因素对驾驶员接管行为的影响，采用多模态传感框架评估，发现危险情境是主要决定因素，为半自动车辆人机协作策略奠定基础。
AI 中文摘要
半自动驾驶系统虽有望减少碰撞，但也带来人类因素挑战，如驾驶员需监控自动化并在故障时迅速恢复控制。长时间被动监控会降低警惕性、延迟反应并增加接管风险。本研究通过控制的、受试者内驾驶模拟器实验，交叉两种危险类型（动态行人与静态碰撞事件）和三个次要任务参与水平（无任务、对话、工作记忆负荷）来探究这些相互作用因素。使用多模态传感框架评估驾驶员反应，包括车辆动力学测量、主观工作量评级、自主生理（皮肤电活动和心率变异性）以及功能性近红外光谱测量的前额叶皮层激活。结果表明危险情境是接管行为的主要决定因素，次要任务对客观车辆控制影响较小，内部状态测量显示出更多与任务相关的可变模式。这些发现凸显了在评估接管准备和设计驾驶员监控系统时联合考虑环境情境和人类状态的重要性，为支持半自动车辆中更安全的人机协作的自适应、情境感知策略奠定了基础。
英文摘要
Semi-automated driving systems promise to reduce crashes by assisting with perception and control, yet they simultaneously introduce additional human factors challenges by requiring drivers to monitor automation and rapidly resume control when failures occur. Prolonged passive monitoring can degrade vigilance, delay reactions, and increase takeover risk, but the extent to which distraction, hazard context, and drivers' underlying cognitive and physiological states jointly shape takeover performance remains insufficiently understood. This study investigates these interacting factors using a controlled, within-subjects driving simulator experiment that crosses two hazard types (dynamic pedestrian and static crash events) with three levels of secondary task engagement (no task, conversation, and working memory load). Driver responses were assessed using a multimodal sensing framework that integrates vehicle-dynamics measures, subjective workload ratings, autonomic physiology (electrodermal activity and heart rate variability), and prefrontal cortical activation measured with functional near-infrared spectroscopy. Results show that hazard context is the primary determinant of takeover behavior, with pedestrian events producing longer and more variable maneuvers and crash events yielding faster and more stable responses. Secondary tasks exerted smaller effects on objective vehicle control, while internal-state measures showed more variable task-related patterns. These findings highlight the importance of jointly considering environmental context and human state when evaluating takeover readiness and designing driver monitoring systems. This study lays the groundwork for adaptive, context-aware strategies that support safer human-automation collaboration in semi-automated vehicles.
