Recently, the scientific progress of Advanced Driver Assistance System
solutions (ADAS) has played a key role in enhancing the overall safety of
driving. ADAS technology enables active control of vehicles to prevent
potentially risky situations. An important aspect that researchers have focused
on is the analysis of the driver attention level, as recent reports confirmed a
rising number of accidents caused by drowsiness or lack of attentiveness. To
address this issue, various studies have suggested monitoring the driver
physiological state, as there exists a well-established connection between the
Autonomic Nervous System (ANS) and the level of attention. For our study, we
designed an innovative bio-sensor comprising near-infrared LED emitters and
photo-detectors, specifically a Silicon PhotoMultiplier device. This allowed us
to assess the driver physiological status by analyzing the associated
PhotoPlethysmography (PPG) signal.Furthermore, we developed an embedded
time-domain hyper-filtering technique in conjunction with a 1D Temporal
Convolutional architecture that embdes a progressive dilation setup. This
integrated system enables near real-time classification of driver drowsiness,
yielding remarkable accuracy levels of approximately 96%.

近年来，先进驾驶辅助系统（ADAS）的科学进展在提高驾驶整体安全性方面扮演了关键角色。ADAS 技术能够主动控制车辆，以预防潜在的危险情况。研究人员关注的一个重要方面是分析驾驶员的注意力水平，因为最近的报告确认了由于困倦或注意力不集中导致的事故数量不断增加。为了解决这个问题，各种研究提出了通过监测驾驶员的生理状态来实现，因为自主神经系统（ANS）与注意力水平之间存在着确立的联系。为了进行我们的研究，我们设计了一种创新的生物传感器，包括近红外 LED 发射器和光电二极管，具体来说是一种硅光电倍增器装置。这使我们能够通过分析相关的光电容抗（PPG）信号评估驾驶员的生理状态。此外，我们开发了一种嵌入式时域超滤波技术，并结合了一种 1D 时域卷积架构，该架构中嵌入了渐进延展设置。这个集成系统能够实时分类驾驶员的困意，获得 96% 左右的显著准确度水平。