AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Esteves, T (Author) Other; Pinto, JR (Author) Other; Ferreira, PM (Author) Other; Costa, PA (Author) Other; Rodrigues, LA (Author) Other; Antunes, I (Author) Other; Lopes, G (Author) Other; Gamito, P (Author) Other; Abrantes, AJ (Author) Other; Jorge, PM (Author) Other; Lourenco, A (Author) Other; Sequeira, AF (Author) Other; Jaime S Cardoso (Author) FEUP; Rebelo, A (Author) Other

Journal

Title: IEEE Access

Vol. 9

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00V-N8F

DOI: 10.1109/access.2021.3128016

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23