Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Francesco Rundo; Sabrina Conoci; Concetto Spampinato; Roberto Leotta; Francesca Trenta; Sebastiano Battiato

doi:10.3389/fninf.2021.667008

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Front Neuroinform. 2021 Jul 30:15:667008. doi: 10.3389/fninf.2021.667008. eCollection 2021.

Authors

Francesco Rundo¹, Sabrina Conoci², Concetto Spampinato³, Roberto Leotta⁴, Francesca Trenta⁴, Sebastiano Battiato⁴

Affiliations

¹ STMicroelectronics, ADG Central R&D Division, Catania, Italy.
² Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy.
³ PerCeiVe Lab, University of Catania, DIEEI, Catania, Italy.
⁴ IPLAB, Department of Mathematics and Computer Science, University of Catania, Catania, Italy.

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.

Keywords: adas; deep learning; driver drowsiness monitoring; pedestrian tracking; photoplethysmographic.