Next Article in Journal
Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network
Previous Article in Journal
A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
Article

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

1
Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
2
Facultad de Ingeniería, Universidad Espíritu Santo, Samborondón 092301, Ecuador
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1692; https://doi.org/10.3390/s20061692
Received: 7 February 2020 / Revised: 10 March 2020 / Accepted: 13 March 2020 / Published: 18 March 2020
(This article belongs to the Section Intelligent Sensors)
Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification. View Full-Text
Keywords: driving styles; driving styles classification; driving styles methodology; machine learning; intelligent vehicle control; driving safety driving styles; driving styles classification; driving styles methodology; machine learning; intelligent vehicle control; driving safety
Show Figures

Figure 1

MDPI and ACS Style

Silva, I.; Eugenio Naranjo, J. A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification. Sensors 2020, 20, 1692. https://doi.org/10.3390/s20061692

AMA Style

Silva I, Eugenio Naranjo J. A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification. Sensors. 2020; 20(6):1692. https://doi.org/10.3390/s20061692

Chicago/Turabian Style

Silva, Iván, and José Eugenio Naranjo. 2020. "A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification" Sensors 20, no. 6: 1692. https://doi.org/10.3390/s20061692

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop