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Offering a machine learning based algorithm, with the purpose of emergency brake during simulated driving based on EEG signal
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M. Faridi Masouleh   , E. Ghiasi , A. Naghib |
| Department of Computer and Information Technology, Ahrar Institute of Technology and Higher Education, Iran |
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Abstract: (1032 Views) |
| Providing safe driving conditions has a great impact on reducing the amount of road accidents and deaths which are caused by them. The necessity of intelligent brake system for increasing the safety during driving has been taken into consideration in today’s cars. The automatic emergency braking system is responsible for informing the driver of impending accidents and using the ultimate potential of the vehicle's braking before a collision occurs. In this paper, the purpose is to predict the brake based on brain EEG signals. For this purpose, the standard bnci database which is defined in this field is used. The aim of the proposed method of this article, is to predict emergency brake during simulated driving, using after error propagation neural network algorithm. The innovative aspect of this paper is the combined use of the dimension reduction algorithm, after-error propagation neural network, and training by the means of K cross validation algorithm for reducing neural network learning error. The proposed method is trained with dataset feature vectors so that after feature vector entry, test recognizes that if emergency brake is necessary or not. Results obtained from proposed method show that the accuracy of this method is more than 90 percent, which has a better performance in comparison with other methods. |
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| Keywords: After Error Propagation Neural Network, EEG Signal, Emergency Brake |
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Full-Text [PDF 409 kb]
(764 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2023/01/25 | Accepted: 2023/06/28
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