Ding, Huizhe and Raja Ghazilla, Raja Ariffin and Kuldip Singh, Ramesh Singh and Wei, Lina (2022) Deep learning method for risk identification under multiple physiological signals and PAD model. Microprocessors and Microsystems, 88. ISSN 0141-9331, DOI https://doi.org/10.1016/j.micpro.2021.104393.
Full text not available from this repository.Abstract
The number of vehicle ownership keeps increasing with the fast development of China's society and economy. Consequently, there are surging road traffic accidents, which seriously endanger the safety of people's lives and property. Hence, the present work aims to reduce the probability of traffic accidents and improve the efficiency of driver risk identification. The driver is taken as the research sample. Driver's physiological and Pleasure Arousal Dominance (PAD) data are collected using virtual driving equipment. Recurrent Neural Network (RNN) is applied to identify multiple physiological signals. Support Vector Machine (SVM) is utilized to classify the data. The performance of the proposed model is validated through data analysis of multiple volunteers. Results demonstrate that the PAD physiological signals can improve the rate of drivers recognizing risks. In the meantime, these signals help to understand the driver's emotions, thereby identifying the risks faced by the driver. By combining SVM and RNN, the accuracy of risk identification reaches 91.44%, which to some extent proves the effectiveness of the proposed model. The results have vital reference value for reducing traffic accidents and ensuring driving safety.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Multiple physiological signals; PAD model; Risk identification; Support vector machine; Recurrent neural network |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Oct 2023 10:20 |
| Last Modified: | 17 Oct 2023 10:20 |
| URI: | http://eprints.um.edu.my/id/eprint/42067 |
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