Deep Learning for Risk Assessment in Automotive Applications

被引：0

作者：

Rundo, Francesco ^{[1
]}

Calabretta, Michele ^{[1
]}

Sitta, Alessandro ^{[1
]}

Rundo, Michael S. ^{[2
]}

Battiato, Sebastiano ^{[3
]}

Messina, Angelo A. ^{[4
]}

机构：

[1] STMicroelectronics, QMT, R&D, P&D, Catania, Italy

[2] Univ Catania, Dipartimento Ingn Informat, Elettr & Elettron PeRCeiVe Lab, Catania, Italy

[3] Univ Catania, Dipartimento Matemat & Informat, Catania, Italy

[4] STMicroelectronics, Italy Publ Affairs, Catania, Italy

来源：

2024 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AUTOMOTIVE, METROAUTOMOTIVE 2024 | 2024年

关键词：

ADAS; Deep Learning; automotive; risk assessment;

D O I：

10.1109/METROAUTOMOTIVE61329.2024.10615341

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

Within the framework of the assisted systems for automotive applications, considerable research has been employed to monitoring the driver's attention level in order to assess the risk level of the driving scenario. In this context, physiological monitoring of the driver's condition has emerged as a relevant approach to enhance driving assistance without having an invasive approach. According to these premises, the authors have developed a driving assistance system capable to employ a dedicated bio-sensor for capture the driver's photoplethysmographic (PPG) signal, which is closely linked to their level of attention. This PPG signal is then processed by a dedicated deep learning architecture to reconstruct the driver's attention level. Meanwhile a separate automotive-grade intelligent vision-based system has been developed to quantify the risk level of the driving scenario by means of a video saliency analysis technique. The effectiveness of this comprehensive system has been validated through experimental results.

引用

页码：53 / 57

页数：5

共 25 条

[1] Einthoven's Triangle Transparency: A Practical Method to Explain Limb Lead Configuration Following Single Lead Misplacements [J].

Abi-Saleh, Bernard ;

Omar, Bassam .

REVIEWS IN CARDIOVASCULAR MEDICINE, 2010, 11 (01) :35-40

[2]

Amidei A., 2024, IEEE Sens. J.

[3] Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance [J].

Amidei, Andrea ;

Spinsante, Susanna ;

Iadarola, Grazia ;

Benatti, Simone ;

Tramarin, Federico ;

Pavan, Paolo ;

Rovati, Luigi .

SENSORS, 2023, 23 (08)

[4]

Battiato S, 2007, IEEE ICCE, P209

[5]

Bhola G, 2020, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020), P1101, DOI [10.1109/iciccs48265.2020.9121061, 10.1109/ICICCS48265.2020.9121061]

[6]

Choi HT, 2018, I C INF COMM TECH CO, P1249, DOI 10.1109/ICTC.2018.8539478

[7] Characterization and evaluation of current transport properties of power SiC Schottky diode [J].