Bibliographic Details
| Title: |
AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles |
| Authors: |
Telma Esteves, Joao Ribeiro Pinto, Pedro M. Ferreira, Pedro Amaro Costa, Lourenco Abrunhosa Rodrigues, Ines Antunes, Gabriel Lopes, Pedro Gamito, Arnaldo J. Abrantes, Pedro M. Jorge, Andre Lourenco, Ana F. Sequeira, Jaime S. Cardoso, Ana Rebelo |
| Source: |
IEEE Access, Vol 9, Pp 153678-153700 (2021) |
| Publisher Information: |
IEEE, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Biometrics, biosignals, computer vision, data, driver, drowsiness, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
As technology and artificial intelligence conquer a place under the spotlight in the automotive world, driver drowsiness monitoring systems have sparked much interest as a way to increase safety and avoid sleepiness-related accidents. Such technologies, however, stumble upon the observation that each driver presents a distinct set of behavioral and physiological manifestations of drowsiness, thus rendering its objective assessment a non-trivial process. The AUTOMOTIVE project studied the application of signal processing and machine learning techniques for driver-specific drowsiness detection in smart vehicles, enabled by immersive driving simulators. More broadly, comprehensive research on biometrics using the electrocardiogram (ECG) and face enables the continuous learning of subject-specific models of drowsiness for more efficient monitoring. This paper aims to offer a holistic but comprehensive view of the research and development work conducted for the AUTOMOTIVE project across the various addressed topics and how it ultimately brings us closer to the target of improved driver drowsiness monitoring. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9614134/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3128016 |
| Access URL: |
https://doaj.org/article/9c060e043ea3414fb915dc5e1cded527 |
| Accession Number: |
edsdoj.9c060e043ea3414fb915dc5e1cded527 |
| Database: |
Directory of Open Access Journals |
