Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks
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 c...
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| Published in | Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5551 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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13.06.2023
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Piet, Artur Hasan, Md Abid Irshad, Muhammad Tausif Piaseczna, Natalia Nisar, Muhammad Adeel Doniec, Rafał Konior, Justyna Sieciński, Szymon Li, Frédéric Grzegorzek, Marcin |
| AuthorAffiliation | 4 Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland 2 Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; ar.piet@uni-luebeck.de (A.P.); m.irshad@uni-luebeck.de (M.T.I.); md.hasan@student.uni-luebeck.de (M.A.H.); fr.li@uni-luebeck.de (F.L.); marcin.grzegorzek@uni-luebeck.de (M.G.) 3 Department of Information Technology, University of the Punjab, Lahore 54000, Pakistan; adeel.nisar@pucit.edu.pk 1 Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; szymon.siecinski@polsl.pl (S.S.); natalia.piaseczna@polsl.pl (N.P.) |
| AuthorAffiliation_xml | – name: 4 Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland – name: 1 Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; szymon.siecinski@polsl.pl (S.S.); natalia.piaseczna@polsl.pl (N.P.) – name: 3 Department of Information Technology, University of the Punjab, Lahore 54000, Pakistan; adeel.nisar@pucit.edu.pk – name: 2 Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; ar.piet@uni-luebeck.de (A.P.); m.irshad@uni-luebeck.de (M.T.I.); md.hasan@student.uni-luebeck.de (M.A.H.); fr.li@uni-luebeck.de (F.L.); marcin.grzegorzek@uni-luebeck.de (M.G.) |
| Author_xml | – sequence: 1 givenname: Rafał orcidid: 0000-0002-2535-3932 surname: Doniec fullname: Doniec, Rafał – sequence: 2 givenname: Justyna surname: Konior fullname: Konior, Justyna – sequence: 3 givenname: Szymon orcidid: 0000-0002-3801-3144 surname: Sieciński fullname: Sieciński, Szymon – sequence: 4 givenname: Artur orcidid: 0000-0003-2137-8363 surname: Piet fullname: Piet, Artur – sequence: 5 givenname: Muhammad Tausif orcidid: 0000-0003-4581-4107 surname: Irshad fullname: Irshad, Muhammad Tausif – sequence: 6 givenname: Natalia orcidid: 0000-0001-9652-157X surname: Piaseczna fullname: Piaseczna, Natalia – sequence: 7 givenname: Md Abid orcidid: 0000-0003-0240-1708 surname: Hasan fullname: Hasan, Md Abid – sequence: 8 givenname: Frédéric orcidid: 0000-0003-2110-4207 surname: Li fullname: Li, Frédéric – sequence: 9 givenname: Muhammad Adeel orcidid: 0000-0003-3288-750X surname: Nisar fullname: Nisar, Muhammad Adeel – sequence: 10 givenname: Marcin orcidid: 0000-0003-4877-8287 surname: Grzegorzek fullname: Grzegorzek, Marcin |
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| SubjectTerms | Accidents, Traffic - prevention & control Algorithms Automobile Driving - psychology Automobiles Behavior Classification convolutional neural networks Deep learning driving a car driving behavior electrooculography Eye movements Fatigue Internet of Things Neural networks Neural Networks, Computer Safety and security measures Sensors Traffic accidents & safety |
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| Title | Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks |
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