  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%.
