For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.

对于 SAE 3 级或更低级别的自动驾驶，驾驶员响应接管请求 (TOR) 的表现对于提供系统安全至关重要。可以预测驾驶员在 TOR 事件中的表现的驾驶员状态监控系统将促进从车辆到驾驶员的更安全的控制转换。该实验研究调查了在 TOR 之前测量的驾驶员眼动是否可以预测后续 TOR 事件中的驾驶性能。我们招募了参与者 (N = 36) 以获得真实车辆研究中的真实结果。在实验中，驾驶员在测试轨道上乘坐自动驾驶汽车约 32 分钟，并在驾驶结束时发生了一个关键的 TOR 事件。眼球运动由基于摄像头的驾驶员监控系统测量，并从 TOR 事件之前的最后 2 分钟时间中提取了五个测量值。检查每个眼动测量与驾驶员反应时间之间的相关性，并使用逐步程序建立多元回归模型。结果表明，较长的反应时间可以通过较少数量的大扫视、较多数量的中等扫视和较低的扫视速度来显着预测。这些关系的含义与以前的研究一致。目前的实车研究可以为汽车行业提供见解，帮助他们在自动驾驶汽车和驾驶员之间寻找更安全、更灵活的接口。更多的中等扫视次数和较低的扫视速度。这些关系的含义与以前的研究一致。目前的实车研究可以为汽车行业提供见解，帮助他们在自动驾驶汽车和驾驶员之间寻找更安全、更灵活的接口。更多的中等扫视次数和较低的扫视速度。这些关系的含义与以前的研究一致。目前的实车研究可以为汽车行业提供见解，帮助他们在自动驾驶汽车和驾驶员之间寻找更安全、更灵活的接口。