Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently stop the vehicle to prevent accidents, but detection of symptoms of diseases and providing support before the driver loses consciousness is more reasonable. It is challenging to earlier detect symptoms with high confidence. Toward solving these problems, we propose a new method with a multi-sensor based driver monitoring system to detect cues of symptoms quickly and a verbal interaction system to confirm the internal state of the driver based on the monitoring results to reduce false positives. There is almost no data that records abnormal conditions while driving and tests with unhealthy participants are dangerous and ethically unacceptable, so we developed a system with pseudo-symptom data and did outlier detection only with normal driving data. From data collection experiments, we defined the confidence level derived from cue signs. The results of evaluation experiments showed that the proposed system worked well in pseudo headache and drowsiness detection scenarios. We found that signs of drowsiness varied with individual drivers, so the multi-sensor based driver monitoring system was proved to be effective. Moreover, we found that there were individual differences in how the cue signs appeared, so we can propose an online re-training method to make the system adapt to individual drivers.

中文翻译：

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走向健康——掩盖事故预防：基于驾驶员监控和言语互动的症状检测和干预


专业司机需要将乘客和/或客户的财产安全地运送到目的地，因此他们必须保持身心健康。健康问题会很大程度上影响驾驶表现，有时会导致意识丧失，从而导致受伤、死亡和巨额赔偿。传统系统可以检测意识丧失或紧急停车以防止事故发生，但在驾驶员失去意识之前检测疾病症状并提供支持更为合理。以高置信度及早发现症状具有挑战性。为了解决这些问题，我们提出了一种新方法，采用基于多传感器的驾驶员监控系统来快速检测症状线索，并使用语言交互系统根据监控结果确认驾驶员的内部状态，以减少误报。几乎没有记录驾驶时异常情况的数据，而对不健康的参与者进行测试是危险的，在道德上是不可接受的，因此我们开发了一个带有伪症状数据的系统，仅使用正常驾驶数据进行异常检测。通过数据收集实验，我们定义了源自提示信号的置信水平。评估实验结果表明，该系统在伪头痛和困倦检测场景中效果良好。我们发现，每个驾驶员的困倦迹象都不同，因此基于多传感器的驾驶员监控系统被证明是有效的。此外，我们发现提示信号的出现方式存在个体差异，因此我们可以提出一种在线重新训练方法，使系统适应个体驾驶员。