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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2019 Vol.9(3): 344-350 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.3.808

A Support Vector Clustering Based Approach for Driving Style Classification

Yuxiang Feng, Simon Pickering, Edward Chappell, Pejman Iravani, and Chris Brace
Abstract—All drivers have their own habitual choice of driving behavior, causing variations in fuel consumption. It would be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a novel technique to robustly classify driving style using the Support Vector Clustering approach, which attempts to differentiate the variations in individual’s driving pattern and provides an objective driver classification. It is part of a research program aiming to replicate some humans’ driving behaviors on chassis dynamometer using a robot driver. Moreover, it can potentially be used in developing more economical and personalized advanced driver assistance systems (ADAS) and humanized autonomous driving strategies. With the easily accessible on-board diagnostics (OBD) data on modern vehicles, both vehicle state and traffic information of three drivers were collected using an instrumented vehicle, which had external forward-looking radar and a monocular dashcam. For data processing, each trip data was first segmented into separate event groups. Prominent factors were then extracted by applying Principal Component Analysis (PCA) on both statistical and spectral features of all signals. Afterwards, Support Vector Clustering (SVC) was performed to classify driving style during the trip. The trained classifier was used to indicate the driving pattern variations in percentage. The validity of the proposed method was evaluated using the jerk profile, where a high correlation was found between the classification results and jerk distributions. Moreover, a positive relation between fuel consumption and driving aggressivity was also confirmed. Furthermore, it was found that weather condition, time of the day and ultimately, the driver’s eagerness, can cause significant variations in driving style.

Index Terms—Driving style analysis, fuel consumption, real-world driving data, support vector clustering.

The authors are with University of Bath, Bath, UK (e-mail: yf356@bath.ac.uk, S.G.Pickering@bath.ac.uk, E.C.Chappell@bath.ac.uk, P.Iravani@bath.ac.uk, and C.J.Brace@bath.ac.uk).

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Cite: Yuxiang Feng, Simon Pickering, Edward Chappell, Pejman Iravani, and Chris Brace, "A Support Vector Clustering Based Approach for Driving Style Classification," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 344-350, 2019.

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