Prediction of Emergency Braking Intention using Machine Learning Models | ||
| Journal of Advanced Engineering Trends | ||
| Volume 43, Issue 2, June 2024, Pages 73-77 PDF (545.1 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jaet.2022.157948.1222 | ||
| Authors | ||
| Samar Medhat Soliman* 1; Abdallah Hassan2; Hassan A. Youness2; Mohammed Moness3 | ||
| 1Computer and Systems Engineering, Faculty of Engineering, El-Minia university, El-Minia, Egypt. | ||
| 2Computer and Systems Engineering, Faculty of Engineering, El-Minia University, El-Minia, Egypt. | ||
| 3Computers and Systems Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt | ||
| Abstract | ||
| Since 2000, road accidents are on the rise, being a leading cause of death worldwide. Approximately 94% of all traffic crashes are due to human mistakes. These mistakes include speeding, reckless driving, or driving under the influence. A significant proportion of automobile accidents could be avoided with emergency braking support. Driver’s status monitoring and human mistake detection are some of the most successful applications of electroencephalogram (EEG) signals. This paper proposes a prediction model for predicting the intention of the driver to use emergency braking using the driver’s electroencephalogram (EEG) signals coupled with electromyography (EMG) data from leg muscles. The dataset utilized in this investigation was obtained from eighteen subjects while driving a simulated car by using an electrode cap with 64 scalp sites. The electroencephalogram (EEG) data signals are segmented to a 150 ms window and applied to five different machine learning classifiers (k-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and Naïve Bayes) for prediction. The proposed model can successfully predict the driver’s emergency braking intention 150 ms before the moment of the brake with an accuracy of 99.6%; that is, at 100 km/h driving speed, our model can anticipate emergency braking intention 4.22 m earlier. Furthermore, the model increased the driver’s prediction of emergency brake intention by 15.2% compared to other models. | ||
| Keywords | ||
| Electroencephalogram; Emergency braking; Machine learning; Prediction | ||
|
Supplementary Files
|
||
|
Statistics Article View: 102 PDF Download: 125 |
||
