skip to main content
10.1145/3274192.3274203acmotherconferencesArticle/Chapter ViewAbstractPublication PagesihcConference Proceedingsconference-collections
research-article

CDNA: A Context-Aware Notification System for Driver Interruption

Authors Info & Claims
Published:22 October 2018Publication History

ABSTRACT

Inopportune driver notifications are a real problem that may cause distractions and interruptions in traffic, and hence accidents. Notifications on mobile devices are one of the ways by which drivers are interrupted, reaching extremely high amounts in a normal day. Despite that, notifications are valued by users and they are part of the common use of smartphones. Therefore, how to lessen the interruptive potential of notifications without eliminating them completely? To mitigate this problem, the present work proposes a context-aware notification system with the identification of opportune and inopportune moments for drivers notification. The proposed system uses smartphone sensors (gyroscope and GPS) to infer if the driver may be interrupted in a specific moment to receive a notification. Preliminary experiments were performed with people in real driving situations to verify if the system could identify opportune and inopportune moments, and found results indicate that is possible to identify these moments with a general accuracy of 88%.

References

  1. Erik M. Altmann, J. Gregory Trafton, and David Z. Hambrick. 2014. Momentary interruptions can derail the train of thought. Journal of Experimental Psychology: General 143, 1 (2014), 215--226.Google ScholarGoogle ScholarCross RefCross Ref
  2. Brian P. Bailey and Joseph A. Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior 22, 4 (2006), 685--708.Google ScholarGoogle ScholarCross RefCross Ref
  3. Matthias Baldauf, Schahram Dustdar, and Florian Rosenberg. 2007. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing 2, 4 (2007), 263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Dongyao Chen, Kyong-tak Cho, Sihui Han, Zhizhuo Jin, and Kang G Shin. 2015. Invisible Sensing of Vehicle Steering with Smartphones. In Mobile Systems, Application and Services. Florence, Italy, 1--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Richard Chirgwin. 2012. Sweden: talk, text and drive? OK. http://www.theregister.co.uk/2012/04/12/sweden{_}not{_}banning{_}mobiles{_}in{_}cars/Google ScholarGoogle Scholar
  6. ANIND K. DEY. 2001. Understanding and using context. Personal and ubiquitous computing 5, 1 (2001), 4--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H Eren, S Makinist, E Akin, and A Yilmaz. 2012. Estimating Driving Behavior by a Smartphone. In IEEE Intelligent Vehicles Symposium. Alcalá de Henares, Spain, 234--239.Google ScholarGoogle ScholarCross RefCross Ref
  8. James Fogarty, Scott E. Hudson, Christopher G. Atkeson, Daniel Avrahami, Jodi Forlizzi, Sara Kiesler, Johnny C. Lee, and Jie Yang. 2005. Predicting human interruptibility with sensors. ACM Transactions on Computer-Human Interaction 12, 1 (2005), 119--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Tony Gillie and Donald Broadbent. 1989. What makes interruptions disruptive? A study of length, similarity, and complexity. Psychological Research 50, 4 (1989), 243--250.Google ScholarGoogle ScholarCross RefCross Ref
  10. Joyce Ho and Stephen S Intille. 2005. Using Context-Aware Computing to Reduce the Perceived Burden of Interruptions from Mobile Devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Portland, Oregon, USA, 909--918. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Scott E Hudson, James Fogarty, Christopher G Atkeson, Daniel Avrahami, Jodi Forlizzi, Sara Kiesler, Johnny C Lee, and Jie Yang. 2003. Predicting Human Interruptibility with Sensors: A Wizard of Oz Feasibility Study. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Ft. Lauderdale, Florida, USA, 257--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Insurance Institute for Highway Safety IIHS. 2017. Cellphones and texting. http://www.iihs.org/iihs/topics/laws/cellphonelaws/maphandheldcellbansGoogle ScholarGoogle Scholar
  13. Shamsi T. Iqbal and Eric Horvitz. 2010. Notifications and awareness: A field study of alert usage and preferences. In Proceedings of the 2010 ACM conference on Computer supported cooperative work - CSCW '10. Savannah, Georgia, 27--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Derick A. Johnson and Mohan M. Trivedi. 2011. Driving style recognition using a smartphone as a sensor platform. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. 1609--1615.Google ScholarGoogle Scholar
  15. SeungJun Kim, Jaemin Chun, and Anind K. Dey. 2015. Sensors Know When to Interrupt You in the Car: Detecting Driver Interruptibility Through Monitoring of Peripheral Interactions. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15. Seoul, 487--496. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Claire Laberge-Nadeau, Urs Maag, François Bellavance, Sophie D. Lapierre, Denise Desjardins, Stéphane Messier, and Abdelnasser Saïdi. 2003. Wireless telephones and the risk of road crashes. Accident Analysis and Prevention 35, 5 (2003), 649--660.Google ScholarGoogle ScholarCross RefCross Ref
  17. Daniel C. McFarlane. {n. d.}. Interruption of people in human-computer interaction: A general unifying definition of human interruption and taxonomy. Technical Report. Navy Center for Applied Research in Artificial Intelligence - Information Technology Division, Washington, DC.Google ScholarGoogle Scholar
  18. Christopher A. Monk, Deborah A. Boehm-Davis, George Mason, and J. Gregory Trafton. 2004. Recovering From Interruptions: Implications for Driver Distraction Research. Human Factors: The Journal of the Human Factors and Ergonomics Society 46, 4 (2004), 650--663.Google ScholarGoogle ScholarCross RefCross Ref
  19. National Highway Traffic Safety Administration NHTSA. 2016. Traffic Safety Facts Traffic Safety Facts - Distracted Driving 2014. Technical Report. United State Department of Transportation, Washington, DC.Google ScholarGoogle Scholar
  20. Antti Oulasvirta, Tye Rattenbury, Lingyi Ma, and Eeva Raita. 2012. Habits make smartphone use more pervasive. Personal and Ubiquitous Computing 16, 1 (2012), 105--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Jung Wook Park, Anind K. Dey, and SeungJun Kim. 2016. Integrated Driving Aware System in the Real-World: Sensing, Computing and Feedback. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. San Jose, California, USA, 1591--1597. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Martin Pielot, Karen Church, and Rodrigo de Oliveira. 2014. An in-situ study of mobile phone notifications. Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services - MobileHCI '14 (2014), 233--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Benjamin Poppinga, Wilko Heuten, and Susanne Boll. 2014. Sensor-based identification of opportune moments for triggering notifications. IEEE Pervasive Computing 13, 1 (2014), 22--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Alireza Sahami Shirazi, Niels Henze, Tilman Dingier, Martin Pielot, Dominik Weber, and Albrecht Schmidt. 2014. Large-scale assessment of mobile notifications. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14. Toronto, Ontario, Canada, 3055--3064. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Stefan Schneegass, Bastian Pfleging, Nora Broy, Albrecht Schmidt, and Frederik Heinrich. 2013. A data set of real world driving to assess driver workload. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '13. 150--157. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Cary Stothart, Ainsley Mitchum, and Courtney Yehnert. 2015. The attentional cost of receiving a cell phone notification. Journal of Experimental Psychology: Human Perception and Performance 41, 4 (2015), 893--897.Google ScholarGoogle ScholarCross RefCross Ref
  27. Fred R H Zijlstra, Robert A Roe, Anna B Leonora, and Irene Krediet. 1999. Temporal factors in mental work: Effects of interrupted activities. Journal of Occupational and Organizational Psychology 72 (1999), 163--185.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. CDNA: A Context-Aware Notification System for Driver Interruption

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Other conferences
                IHC '18: Proceedings of the 17th Brazilian Symposium on Human Factors in Computing Systems
                October 2018
                488 pages
                ISBN:9781450366014
                DOI:10.1145/3274192

                Copyright © 2018 ACM

                © 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 22 October 2018

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • research-article
                • Research
                • Refereed limited

                Acceptance Rates

                IHC '18 Paper Acceptance Rate42of166submissions,25%Overall Acceptance Rate331of973submissions,34%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader