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Naturalistic use of cell phones in driving and context-based user assistance

Published: 09 September 2007 Publication History

Abstract

A field study has been conducted to investigate the naturalistic use of cell phone applications in driving, home, work, and school and during daytime and nighttime. GPS coordinates are used to determine whether cell phone users are driving. The frequency and duration of use of various cell phone applications such as incoming or outgoing voice calls, music player, calendar, SMS, camera, and the Internet are analyzed separately for driving and non-driving. The present results provide fundamental data for adequately assessing the distraction potential of mobile devices and guiding the design of context-based assistance systems.

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Cited By

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  • (2014)Subjective Traffic Safety Experts' Knowledge for Driving-Risk DefinitionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2014.233057615:4(1823-1834)Online publication date: Aug-2014
  • (2014)Driver’s Hand Detection and Tracking Based on Address Event RepresentationComputational Modeling of Objects Presented in Images10.1007/978-3-319-04039-4_8(131-144)Online publication date: 2-Feb-2014
  • (2012)Wireless In-Vehicle Compliant Black Box: For Accident AnalysisIEEE Vehicular Technology Magazine10.1109/MVT.2012.22036927:3(80-89)Online publication date: Sep-2012
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    cover image ACM Other conferences
    MobileHCI '07: Proceedings of the 9th international conference on Human computer interaction with mobile devices and services
    September 2007
    352 pages
    ISBN:9781595938626
    DOI:10.1145/1377999
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 09 September 2007

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    Author Tags

    1. GPS
    2. driving
    3. mobile devices
    4. user assistance

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    Cited By

    View all
    • (2014)Subjective Traffic Safety Experts' Knowledge for Driving-Risk DefinitionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2014.233057615:4(1823-1834)Online publication date: Aug-2014
    • (2014)Driver’s Hand Detection and Tracking Based on Address Event RepresentationComputational Modeling of Objects Presented in Images10.1007/978-3-319-04039-4_8(131-144)Online publication date: 2-Feb-2014
    • (2012)Wireless In-Vehicle Compliant Black Box: For Accident AnalysisIEEE Vehicular Technology Magazine10.1109/MVT.2012.22036927:3(80-89)Online publication date: Sep-2012
    • (2011)Section-Wise similarities for classification of subjective-data on time seriesProceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-642-25085-9_43(363-371)Online publication date: 15-Nov-2011
    • (2010)Detection and tracking of driver's hands in real timeProceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications10.5555/1948207.1948246(212-219)Online publication date: 8-Nov-2010
    • (2010)An exploratory study of unsupervised mobile learning in rural IndiaProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/1753326.1753435(743-752)Online publication date: 10-Apr-2010
    • (2010)Driving risk classification based on experts evaluation2010 IEEE Intelligent Vehicles Symposium10.1109/IVS.2010.5548130(1098-1103)Online publication date: Jun-2010

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