Are you a Good Driver? A Data-driven Approach to Estimate Driving Style

Silva Feraud, Iván and Naranjo Hernández, José Eugenio (2019). Are you a Good Driver? A Data-driven Approach to Estimate Driving Style. In: "ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation", 16/01/2019-19/01/2019, Melbourne, Australia. ISBN 978-1-4503-6619-9. pp. 3-7. https://doi.org/10.1145/3307363.3307375.

Description

Title: Are you a Good Driver? A Data-driven Approach to Estimate Driving Style
Author/s:
  • Silva Feraud, Iván
  • Naranjo Hernández, José Eugenio
Item Type: Presentation at Congress or Conference (Article)
Event Title: ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation
Event Dates: 16/01/2019-19/01/2019
Event Location: Melbourne, Australia
Title of Book: ICCMS 2019: Proceedings of the 11th International Conference on Computer Modeling and Simulation
Date: January 2019
ISBN: 978-1-4503-6619-9
Subjects:
Freetext Keywords: Fuzzy logic; Driver behaviors; Aggressive driving style; Traffic violations
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

In recent years, the study of aggressive driving behavior has reached interest due to its correlation with traffic accidents. Traffic accidents are considered the third cause of deaths in the United States. Prior research has reported the possibility to estimate aggressive driving style using in-vehicle data (e.g., acceleration, speeding, lane changes, among others). However, traffic violations have not yet been considered in the analysis of aggressive driving style. This paper proposes a model to estimate driver's aggressive driving style by considering aggressive events from in-vehicle data, and traffic violations data using a fuzzy logic model. In-vehicle data and GPS data from twenty-five drivers in different routes were collected, to generate a fuzzy logic model that captures aggressive events and traffic violations. We validate these results by comparing the results between the fuzzy logic model and human experts scores, showing an accuracy of 0.84 and a recall of 0.8974. Future work should consider to revise the rules and membership values to improve misclassification errors.

More information

Item ID: 65580
DC Identifier: http://oa.upm.es/65580/
OAI Identifier: oai:oa.upm.es:65580
DOI: 10.1145/3307363.3307375
Official URL: http://iccms.org/2019.html
Deposited by: Memoria Investigacion
Deposited on: 27 Jan 2021 14:14
Last Modified: 27 Jan 2021 14:14
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