在线注释隐蔽危险驾驶场景：利用驾驶员的脑电（EEG）信号
Annotating Covert Hazardous Driving Scenarios Online: Utilizing Drivers' Electroencephalography (EEG) Signals
摘要：
随着自动驾驶系统的普及，系统从包含细粒度驾驶场景的数据库中学习变得越来越重要。目前可用的大多数数据库都是人工注释的;它们昂贵，耗时，并且受到行为偏见的影响。在本文中，我们提供了初步的证据，支持一种新的技术，利用司机的脑电图（EEG）信号，隐式标记危险的驾驶场景，同时被动地观看记录的真实道路驾驶，从而节省人工注释的需要，避免人类注释者的行为偏见，在明确的报告。我们进行了一项脑电图实验，使用真实和动画记录的驾驶场景，并要求参与者在必要时明确报告危险。行为结果显示，参与者倾向于报告危险，只有当明显的危险（例如，车辆或行人突然从遮挡物后面出现）。相比之下，他们的EEG信号在看到明显的危险和隐蔽的危险时都会增强（例如，遮挡信号表示车辆或行人可能从后面出现）。因此，脑电信号更敏感的驾驶危险比明确的报告。此外，时间序列AI（TSAI）成功地将EEG信号分类为显性和隐性危险。我们将讨论未来在现实生活中实现该技术所需的步骤。
英文摘要：
As autonomous driving systems prevail, it is becoming increasingly critical that the systems learn from databases containing fine-grained driving scenarios. Most databases currently available are human-annotated; they are expensive, time-consuming, and subject to behavioral biases. In this paper, we provide initial evidence supporting a novel technique utilizing drivers' electroencephalography (EEG) signals to implicitly label hazardous driving scenarios while passively viewing recordings of real-road driving, thus sparing the need for manual annotation and avoiding human annotators' behavioral biases during explicit report. We conducted an EEG experiment using real-life and animated recordings of driving scenarios and asked participants to report danger explicitly whenever necessary. Behavioral results showed the participants tended to report danger only when overt hazards (e.g., a vehicle or a pedestrian appearing unexpectedly from behind an occlusion) were in view. By contrast, their EEG signals were enhanced at the sight of both an overt hazard and a covert hazard (e.g., an occlusion signalling possible appearance of a vehicle or a pedestrian from behind). Thus, EEG signals were more sensitive to driving hazards than explicit reports. Further, the Time-Series AI (TSAI) successfully classified EEG signals corresponding to overt and covert hazards. We discuss future steps necessary to materialize the technique in real life.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
