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Machine Learning-Based Approach For Detecting Driver Behavior Using Smartphone Sensors
| Content Provider | Semantic Scholar |
|---|---|
| Author | Abbadi, Mohammad Ali Abadleh, Ahmad Aljaafreh, Saqer Halhouli, Zaid |
| Copyright Year | 2019 |
| Abstract | This paper aims at combining machine learning techniques with Smartphone sensors (i.e. accelerometer sensor) to develop a smart model capable of classifying vehicle driving style into (Excellent, good or weak) categories. In this paper, we use several machine learning algorithms (Neural network, KNN, Naïve Bayes and Random forest tree) to train and test data extracted from Smartphone sensors. The results indicate the possibility to exploit Smartphone sensor readings in the design of a reliable model capable of identifying the driving style based on accelerometer readings. All examined machine learning algorithms maintain high accuracy in classifying the vehicle's driving class; however, the neural network classifiers achieve the highest accuracy ratio reaches (99.9%). |
| Starting Page | 1057 |
| Ending Page | 1060 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| Volume Number | 8 |
| Alternate Webpage(s) | http://www.ijstr.org/final-print/dec2019/Machine-Learning-based-Approach-For-Detecting-Driver-Behavior-Using-Smartphone-Sensors.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
