Driver behavior model for healthy driving style using machine learning methods
1 Department of Computer Science, Nnamdi Azikiwe University Awka, Anambra State, Nigeria.
2 Department of Electronic and Computer Engineering, Nnamdi Azikiwe University Awka, Anambra State, Nigeria.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2022, 07(01), 137–148.
Article DOI: 10.30574/wjaets.2022.7.1.0103
Publication history:
Received on 14 September 2022; revised on 18 October 2022; accepted on 21 October 2022
Abstract:
Driving is a complex and dynamic task requiring drivers not only to make accurate perceptions and cognitions about the information on the driver’s driving skill but also to process this information at a high speed. This paper compared three major image processing/machine learning algorithms viz; Single Shot Multibox Detection (SSD), Convolutional Neural Networks (CNN), and support vector machine (SVM) to find the fastest and most efficient of the three with regards to the dataset from driving events (braking, speeding and safe driving) collected from Nigeria. The results analyzed showed that in an identical testing environment, Support Vector Machine outperformed Single Shot Detection and Convolutional Neural Networks.
Keywords:
Machine learning; Driving events; Convolutional Neural Network; Support Vector Machine; Single Shot Multibox Detection.
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0