Profiles of Professional Drivers Based on Drowsiness and Distraction Alerts

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Soares, S (Autor) Outra; Zafeiris Kokkinogenis (Autor) Outra; Sara Ferreira (Autor) FEUP; António Fidalgo Couto (Autor) FEUP

Ata de Conferência Internacional

Título: Human Interaction, Emerging Technologies and Future Applications II - Proceedings of the 2nd International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET - AI 2020), April 23-25, 2020, Lausanne, Switzerland

Páginas: 272-278

2nd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET-AI 2020

23 April 2020 through 25 April 2020

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00S-5DE

DOI: 10.1007/978-3-030-44267-5_41

Abstract (EN): Driver inattention has been a major contributor to road crashes through the last years. Additionally, professional drivers are more expose to this risk due to the long work journey. The present study aims to explore the data collected by a commercial driver-monitoring system (DMS) in order to identify profiles of professional drivers. The DMS emitted an alert when distraction or drowsiness were detected, generating additional information such as timestamp, GPS and instant speed. The Hierarchical Clustering Approach and the K-means method were used to identify distinct profiles. The results showed a clear distinction among the clusters with respect to the exposition variables (time and distance related) as well as the number of inattention events. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Idioma: Inglês

Tipo (Avaliação Docente): Científica