Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Rafał Doniec
* ORCID logo ,
Justyna Konior
,
Szymon Sieciński
ORCID logo ,
Artur Piet
ORCID logo ,
Muhammad Tausif Irshad
ORCID logo ,
Natalia Piaseczna
ORCID logo ,
Md Abid Hasan
,
Frédéric Li
ORCID logo ,
Muhammad Adeel Nisar
ORCID logo ,
Marcin Grzegorzek
Version 1 : Received: 27 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (08:14:38 CEST)

A peer-reviewed article of this Preprint also exists.

Doniec, R.; Konior, J.; Sieciński, S.; Piet, A.; Irshad, M.T.; Piaseczna, N.; Hasan, M.A.; Li, F.; Nisar, M.A.; Grzegorzek, M. Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks. Sensors 2023, 23, 5551. Doniec, R.; Konior, J.; Sieciński, S.; Piet, A.; Irshad, M.T.; Piaseczna, N.; Hasan, M.A.; Li, F.; Nisar, M.A.; Grzegorzek, M. Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks. Sensors 2023, 23, 5551.

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 the cognitive capabilities of 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 primary activities in driving, including crossroad, parking, roundabout 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 secondary activities related to activities of daily life and secondary when driving a car.

Keywords

driving a car; driving behavior; electrooculography; convolutional neural networks

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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.

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