Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks
Author:
Doniec Rafał1ORCID, Konior Justyna1, Sieciński Szymon12ORCID, Piet Artur2ORCID, Irshad Muhammad Tausif23ORCID, Piaseczna Natalia1ORCID, Hasan Md Abid2ORCID, Li Frédéric2ORCID, Nisar Muhammad Adeel3ORCID, Grzegorzek Marcin24ORCID
Affiliation:
1. Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland 2. Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany 3. Department of Information Technology, University of the Punjab, Lahore 54000, Pakistan 4. Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland
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.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference70 articles.
1. Kinnear, N., and Stevens, A. (2015). The Battle for Attention: Driver Distraction—A Review of Recent Research and Knowledge, IAM. Technical Report. 2. Doniec, R., Sieciński, S., Piaseczna, N., Mocny-Pachońska, K., Lang, M., and Szymczyk, J. (2020). Advances in Intelligent Systems and Computing, Springer International Publishing. 3. Doniec, R.J., Sieciński, S., Duraj, K.M., Piaseczna, N.J., Mocny-Pachońska, K., and Tkacz, E.J. (2020). Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network. Electronics, 9. 4. A systematic review on sensor-based driver behaviour studies: Coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions;Kiah;PeerJ Comput. Sci.,2021 5. Ping, P., Qin, W., Xu, Y., Miyajima, C., and Kazuya, T. (2018, January 14–17). Spectral clustering based approach for evaluating the effect of driving behavior on fuel economy. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|