Abstract
In recent years, the automotive field has been changed by the accelerated rise of new technologies. Specifically, autonomous driving has revolutionized the car manufacturer's approach to design the advanced systems compliant to vehicle environments. As a result, there is a growing demand for the development of intelligent technology in order to make modern vehicles safer and smarter. The impact of such technologies has led to the development of the so-called Advanced Driver Assistance Systems (ADAS), suitable to maintain control of the vehicle in order to avoid potentially dangerous situations while driving. Several studies confirmed that an inadequate driver's physiological condition could compromise the ability to drive safely. For this reason, assessing the car driver's physiological status has become one of the primary targets of the automotive research and development. Although a large number of efforts has been made by researchers to design safety-assessment applications based on the detection of physiological signals, embedding them into a car environment represents a challenging task. These mentioned implications triggered the development of this study in which we proposed an innovative pipeline, that through a combined less invasive Neuro-Visual approach, is able to reconstruct the car driver's physiological status. Specifically, the proposed contribution refers to the sampling and processing of the driver PhotoPlethysmoGraphic (PPG) signal. A parallel enhanced low frame-rate motion magnification algorithm is used to reconstruct such features of the driver's PhotoPlethysmoGraphic (PPG) data when that signal is no longer available from the native embedded sensor platform. A parallel monitoring of the driver's blood pressure levels from the PPG signal as well as the driver's eyes dynamics completes the reconstruction of the driver's physiological status. The proposed pipeline has been tested in one of the major investigated automotive scenarios i.e., the detection and monitoring of pedestrians while driving (pedestrian tracking). The collected performance results confirmed the effectiveness of the proposed approach.
Highlights
Drowsiness is a symptom related to a lack of awareness which affects concentration, reaction time, and most seriously, safety (Schmidt, 1989; Rundo et al, 2019c)
In order to validate the proposed composed approach we have tested each of the proposed blocks i.e., the PPG-based Heart Rate Variability (HRV) driver drowsiness detection as well as the driver blood pressure level estimation and the visual drowsiness estimation based on D-CNN processing and the pedestrian detection, segmentation and tracking
We addressed the mentioned issue regarding the estimation of driving safety by using a non-invasive and robust methodology
Summary
Drowsiness is a symptom related to a lack of awareness which affects concentration, reaction time, and most seriously, safety (Schmidt, 1989; Rundo et al, 2019c). The growing proliferation of non-invasive medical devices to collect physiological parameters has led to the development of advanced new tools to be integrated into vehicle-environment. In this context, PhotoPlethysmoGraphic (PPG) signal has been proposed as a valid solution to analyze a subject’s physiological status (Kurian et al, 2014; Dastjerdi et al, 2017; Rundo et al, 2019c,d). Several studies have pointed out that a robust driving risk assessment system leverages the so-called “driver fatigue condition” (which combines drowsiness and blood pressure monitoring) to perform a robust risk estimation (Husodo et al, 2018)
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