Szczegóły publikacji
Opis bibliograficzny
Sensor-based classification of primary and secondary car driver activities using convolutional neural networks / Rafał Doniec, Justyna Konior, Szymon Sieciński, Artur Piet, Muhammad Tausif Irshad, Natalia Piaseczna, Md Abid Hasan, Frédéric Li, Muhammad Adeel Nisar, Marcin Grzegorzek // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220 . — 2023 — vol. 23 iss. 12 art. no. 5551, s. 1–19. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 17–19, Abstr. — Publikacja dostępna online od: 2023-06-13. — Sz. Sieciński – afiliacje: Silesian University of Technology, Zabrze ; University of Lübeck, Germany
Autorzy (10)
- Doniec Rafał
- Konior Justyna
- Sieciński Szymon
- Piet Artur
- Irshad Muhammad Tausif
- Piaseczna Natalia
- Hasan Md Abid
- Li Frédéric
- Nisar Muhammad Adeel
- Grzegorzek Marcin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164544 |
|---|---|
| Data dodania do BaDAP | 2026-01-27 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s23125551 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.
