A systematic methodology to evaluate prediction models for driving style classification

Silva Feraud, Iván and Naranjo Hernández, José Eugenio (2020). A systematic methodology to evaluate prediction models for driving style classification. "Sensors", v. 20(6) (n. 1692); pp. 1-21. ISSN 1424-8220. https://doi.org/10.3390/s20061692.

Description

Title: A systematic methodology to evaluate prediction models for driving style classification
Author/s:
  • Silva Feraud, Iván
  • Naranjo Hernández, José Eugenio
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 18 March 2020
ISSN: 1424-8220
Volume: 20(6)
Subjects:
Freetext Keywords: Driving styles; Driving styles classification; Driving styles methodology; Machine learning; Intelligent vehicle control; Driving safety
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 65576
DC Identifier: http://oa.upm.es/65576/
OAI Identifier: oai:oa.upm.es:65576
DOI: 10.3390/s20061692
Official URL: https://www.mdpi.com/1424-8220/20/6/1692/htm
Deposited by: Memoria Investigacion
Deposited on: 14 Jan 2021 15:46
Last Modified: 14 Jan 2021 15:46
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