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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

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

Revista

Título: IEEE Access

Vol. 9

ISSN: 2169-3536

Editora: IEEE

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23