Version 1
: Received: 22 April 2024 / Approved: 23 April 2024 / Online: 24 April 2024 (03:33:57 CEST)
Version 2
: Received: 2 July 2024 / Approved: 3 July 2024 / Online: 3 July 2024 (06:36:51 CEST)
How to cite:
Piaseczna, N.; Doniec, R.; Sieciński, S.; Barańska, K.; Jędrychowski, M.; Grzegorzek, M. Driving Reality vs Simulator: Data Distinctions. Preprints2024, 2024041490. https://doi.org/10.20944/preprints202404.1490.v2
Piaseczna, N.; Doniec, R.; Sieciński, S.; Barańska, K.; Jędrychowski, M.; Grzegorzek, M. Driving Reality vs Simulator: Data Distinctions. Preprints 2024, 2024041490. https://doi.org/10.20944/preprints202404.1490.v2
Piaseczna, N.; Doniec, R.; Sieciński, S.; Barańska, K.; Jędrychowski, M.; Grzegorzek, M. Driving Reality vs Simulator: Data Distinctions. Preprints2024, 2024041490. https://doi.org/10.20944/preprints202404.1490.v2
APA Style
Piaseczna, N., Doniec, R., Sieciński, S., Barańska, K., Jędrychowski, M., & Grzegorzek, M. (2024). Driving Reality vs Simulator: Data Distinctions. Preprints. https://doi.org/10.20944/preprints202404.1490.v2
Chicago/Turabian Style
Piaseczna, N., Marek Jędrychowski and Marcin Grzegorzek. 2024 "Driving Reality vs Simulator: Data Distinctions" Preprints. https://doi.org/10.20944/preprints202404.1490.v2
Abstract
As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The data set encompasses electrooculography signals, with a focus on standardizing and processing data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology.
Keywords
electrooculography; driving simulation; real-world driving; data distinctions; biomedical signal processing
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
