Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity

Stratis Kanarachos; Stavros Christopoulos; Alexander Chroneos

doi:10.1016/j.trc.2018.03.023

Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity

Stratis Kanarachos, Stavros Christopoulos, Alexander Chroneos

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Abstract

Nowadays, more than half of the world’s web traffic comes from mobile phones, and by 2020 approximately 70 percent of the world’s population will be using smartphones. The unprecedented market penetration of smartphones combined with the connectivity and embedded sensing capability of smartphones is an enabler for the large-scale deployment of Intelligent Transportation Systems (ITS). On the downside, smartphones have inherent limitations such as relatively limited energy capacity, processing power, and accuracy. These shortcomings may potentially limit their role as an integrated platform for monitoring driver behaviour in the context of ITS. This study examines this hypothesis by reviewing recent scientific contributions. The Cybernetics theoretical framework was employed to allow a systematic comparison. First, only a few studies consider the smartphone as an integrated platform. Second, a lack of consistency between the approaches and metrics used in the literature is noted. Last but not least, areas such as fusion of heterogeneous information sources, Deep Learning and sparse crowd-sensing are identified as relatively unexplored, and future research in these directions is suggested.

Original language	English
Pages (from-to)	867-882
Number of pages	16
Journal	Transportation Research Part C: Emerging Technologies
Volume	95
Early online date	5 Apr 2018
DOIs	https://doi.org/10.1016/j.trc.2018.03.023
Publication status	Published - Oct 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Transportation Research Part C: Emerging Technologies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Transportation Research Part C: Emerging Technologies, [95], (2018) DOI:10.1016/j.trc.2018.03.023

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

Smartphones
driver behaviour
sensor fusion
connectivity
cybernetics
crowd-sensing

Access to Document

10.1016/j.trc.2018.03.023

Post-PrintAccepted author manuscript, 1.42 MBLicence: CC BY-NC-ND

iVMS: Intelligent Variable Messaging System
haas, O., Perraki, G., Fofana, A., Esugo, M., Kanarachos, S., Christopoulos, S., Henry, N. & Jones, A.
1/06/16 → 30/03/18
Project: Research

Cite this

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title = "Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity",

abstract = "Nowadays, more than half of the world{\textquoteright}s web traffic comes from mobile phones, and by 2020 approximately 70 percent of the world{\textquoteright}s population will be using smartphones. The unprecedented market penetration of smartphones combined with the connectivity and embedded sensing capability of smartphones is an enabler for the large-scale deployment of Intelligent Transportation Systems (ITS). On the downside, smartphones have inherent limitations such as relatively limited energy capacity, processing power, and accuracy. These shortcomings may potentially limit their role as an integrated platform for monitoring driver behaviour in the context of ITS. This study examines this hypothesis by reviewing recent scientific contributions. The Cybernetics theoretical framework was employed to allow a systematic comparison. First, only a few studies consider the smartphone as an integrated platform. Second, a lack of consistency between the approaches and metrics used in the literature is noted. Last but not least, areas such as fusion of heterogeneous information sources, Deep Learning and sparse crowd-sensing are identified as relatively unexplored, and future research in these directions is suggested. ",

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author = "Stratis Kanarachos and Stavros Christopoulos and Alexander Chroneos",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Transportation Research Part C: Emerging Technologies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Transportation Research Part C: Emerging Technologies, [95], (2018) DOI:10.1016/j.trc.2018.03.023 {\textcopyright} 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ ",

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PY - 2018/10

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N2 - Nowadays, more than half of the world’s web traffic comes from mobile phones, and by 2020 approximately 70 percent of the world’s population will be using smartphones. The unprecedented market penetration of smartphones combined with the connectivity and embedded sensing capability of smartphones is an enabler for the large-scale deployment of Intelligent Transportation Systems (ITS). On the downside, smartphones have inherent limitations such as relatively limited energy capacity, processing power, and accuracy. These shortcomings may potentially limit their role as an integrated platform for monitoring driver behaviour in the context of ITS. This study examines this hypothesis by reviewing recent scientific contributions. The Cybernetics theoretical framework was employed to allow a systematic comparison. First, only a few studies consider the smartphone as an integrated platform. Second, a lack of consistency between the approaches and metrics used in the literature is noted. Last but not least, areas such as fusion of heterogeneous information sources, Deep Learning and sparse crowd-sensing are identified as relatively unexplored, and future research in these directions is suggested.

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Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity

Abstract

Bibliographical note

Keywords

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Projects

iVMS: Intelligent Variable Messaging System

Cite this