Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Driving behavior analysis for smartphone-based insurance telematics
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2718-0262
2015 (English)In: WPA 2015 - Proceedings of the 2nd Workshop on Physical Analytics, ACM Digital Library, 2015, 19-24 p.Conference paper, Published paper (Refereed)
Abstract [en]

Insurance telematics programs are continuously gaining market shares in the automotive insurance industry. By recording data on drivers' behavior, the information asymmetry between the policyholder and the insurer is reduced, enabling a granular risk differentiation based on the true risk levels of the drivers. However, the growth of the insurance telematics industry is being held up by large logistic costs associated with the process of acquiring data. As a result, several market participants have started looking towards smartphone-based solutions, which have the potential of easing and improving the data collection process for both policyholders and insurers. In this paper, we present a unified framework highlighting the challenges of smartphone-based driver behavior analysis. Since all driver behavior analysis relies on access to accurate navigation data, we first address the intermediate step of smartphone-based automotive navigation. The considered topics include estimation of the smartphone's orientation with respect to the vehicle, classification of the smartphoneowner as a passenger or driver, and navigation in GNSSchallenged areas. Once a driver-specific high-performance navigation solution has been obtained, it can be used to extract information on the driver's behavior. We review the most commonly employed driving events, and discuss some of the difficulties inherent in detecting these events.

Place, publisher, year, edition, pages
ACM Digital Library, 2015. 19-24 p.
Keyword [en]
Driver behavior analysis, Insurance telematics, Smartphones, Behavioral research, Commerce, Competition, Insurance, Navigation, Signal encoding, Wireless telecommunication systems, Data collection process, Driver behavior, Extract informations, Information asymmetry, Market participants, Navigation solution, Risk differentiation, Telematics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-196189DOI: 10.1145/2753497.2753535Scopus ID: 2-s2.0-84982082560ISBN: 9781450334983 (print)OAI: oai:DiVA.org:kth-196189DiVA: diva2:1046742
Conference
2nd Workshop on Physical Analytics, WPA 2015, 22 May 2015
Note

Conference Paper. QC 20161115

Available from: 2016-11-15 Created: 2016-11-14 Last updated: 2016-11-15Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Wahlström, JohanSkog, IsaacHändel, Peter
By organisation
ACCESS Linnaeus Centre
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf