Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Retrospective and Future Automotive Infotainment Systems—100 Years of User Interface Evolution Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

2. Engaged Drivers–Safe Drivers: Gathering Real-Time Data from Mobile and Wearable Devices for Safe-Driving Apps

verfasst von : Fabius Steinberger, Ronald Schroeter, Diana Babiac

Erschienen in: Automotive User Interfaces

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Regardless of punitive strategies such as fines and demerit points, drivers continue to bring their own devices into cars and use them while driving. In this chapter, we explore the opportunities for gamified safe-driving apps provided by real-time data gathered from mobile and wearable devices. The study is grounded in our interest in providing engaging experiences for drives that otherwise lack engagement, both in manual and semi-automated vehicles. We developed BrakeMaster, a smartphone app built around vehicle and road data, and evaluated it in a simulator study looking at system performance, usability, and affect. We found the app to perform responsively and accurately, and self-reported data indicate good usability and increased pleasure. Besides exploring vehicle and road data, we investigated wearable activity monitors for gathering driver data such as arousal. Consumer wearables are more cost and size effective than advanced biofeedback systems and are capable of revealing heart rate patterns and trends across drives. We conclude that road and particularly vehicle data can be leveraged to develop novel driving experiences, whereas driver data is more challenging to exploit in this unique design context.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Retrospective and Future Automotive Infotainment Systems—100 Years of User Interface Evolution
Nächstes Kapitel Driver and Driving Experience in Cars
Fußnoten
Literatur
Aguilar, S. R., Merino, J. L. M., Sánchez, A. M., & Valdivieso, Á. S. (2015). Variation of the heartbeat and activity as an indicator of drowsiness at the wheel using a smartwatch. International Journal of Artificial Intelligence and Interactive Multimedia 3.
Andreone, L., Amditis, A., Deregibus, E., & Damiani, S. (2005). Beyond context-awareness: Driver-vehicle-environment adaptivity. From the COMUNICAR Project to the AIDE concept. In 16th IFAC World.
Baguley, T., & Andrews, M. (2016). Handling missing data. In J. Robertson & M. Kaptein (Eds.), Modern statistical methods for HCI (pp. 57–82). Springer International Publishing.
Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. International Journal of Human Computer Interaction, 24, 574–594.CrossRef
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (sandy). (2014). Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the 22Nd ACM International Conference on Multimedia (pp. 477–486). New York, NY, USA: ACM.
Brooke, J. (1996). Others: SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189, 4–7.
Casner, S. M., Hutchins, E. L., & Norman, D. (2016). The challenges of partially automated driving. Communications of the ACM, 59, 70–77.CrossRef
Coben, J. H., & Zhu, M. (2013). Keeping an eye on distracted driving. JAMA, 309, 877–878.CrossRef
Dai, J., Teng, J., Bai, X., Shen, Z., & Xuan, D. (2010). Mobile phone based drunk driving detection. Pervasive Health, 2010, 1–8.
Eren, H., Makinist, S., Akin, E., & Yilmaz, A. (2012). Estimating driving behavior by a smartphone. In Intelligent Vehicles Symposium (IV), 2012 IEEE (pp. 234–239).
Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., & Balakrishnan, H. (2008). The pothole patrol: Using a mobile sensor network for road surface monitoring. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services (pp. 29–39). New York, NY, USA: ACM.
Heslop, S. (2014). Driver boredom: Its individual difference predictors and behavioural effects. Transportation Research Part F: Traffic Psychology and Behaviour, 22, 159–169.
Hong, J.-H., Margines, B., & Dey, A. K. (2014). A smartphone-based sensing platform to model aggressive driving behaviors. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 4047–4056). New York, NY, USA: ACM.
Koukoumidis, E., Martonosi, M., & Peh, L.-S. (2012). Leveraging smartphone cameras for collaborative road advisories. IEEE Transactions on Mobile Computing, 11, 707–723.CrossRef
Lewis, J. R., & Sauro, J. (2009). The factor structure of the system usability scale. In M. Kurosu (Ed.), Human centered design (pp. 94–103). Berlin, Heidelberg: Springer.CrossRef
Liang, Y., Reyes, M. L., & Lee, J. D. (2007). Real-time detection of driver cognitive distraction using support vector machines. IEEE Transactions on Intelligent Transportation Systems, 8, 340–350.CrossRef
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013). MoodScope: Building a mood sensor from smartphone usage patterns. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (pp. 389–402). New York, NY, USA: ACM.
Litman, T. (2015). Autonomous vehicle implementation predictions: Implications for transport planning. In Transportation Research Board 94th Annual Meeting.
McCall, R., & Koenig, V. (2012). Gaming concepts and incentives to change driver behaviour. In Ad Hoc Networking Workshop (Med-Hoc-Net), 2012 The 11th Annual Mediterranean (pp. 146–151).
Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008). Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (pp. 323–336). New York, NY, USA: ACM.
Nelson, M. B., Kaminsky, L. A., Dickin, D. C., & Montoye, A. H. K. (2016). Validity of consumer-based physical activity monitors for specific activity types. Medicine and Science in Sports and Exercise, 48, 1619–1628.CrossRef
NHTSA. (2016). Distracted Driving 2014.
Normark, C. J. (2015). Vehicle interaction tailored to you. Interactions, 22, 32–36.CrossRef
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178.CrossRef
SAE International. (2014). J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems.
Schroeter, R., Soro, A., & Rakotonirainy, A. (2013). Social cars: sensing, gathering, sharing and conveying social cues to road users. In B. Guo, D. Riboni & P. Hu (Eds.) Creating personal, social, and urban awareness through pervasive computing (pp. 176–200). IGI Global.
Sivak, M. (1996). The information that drivers use: Is it indeed 90% visual? Perception, 25, 1081–1089.CrossRef
Steinberger, F., Schroeter, R., Lindner, V., Fitz-Walter, Z., Hall, J., & Johnson, D. (2015). Zombies on the road: A holistic design approach to balancing gamification and safe driving. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 320–327). New York, NY, USA: ACM.
Steinberger, F., Schroeter, R., & Lindner, V. (2015). From gearstick to joystick—challenges in designing new interventions for the safety-critical driving context. Presented at the OzCHI 2015 Workshop, Melbourne, VIC December 7 (2015).
Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological recording. Oxford University Press.
Tijs, T. J. W., Brokken, D., & IJsselsteijn, W. A. (2008). Dynamic game balancing by recognizing affect. In: P. Markopoulos, B. de Ruyter, W. IJsselsteijn & D. Rowland (Eds.), Fun and games (pp. 88–93). Berlin, Heidelberg: Springer.
Vollrath, M., Huemer, A. K., Teller, C., Likhacheva, A., & Fricke, J. (2016). Do German drivers use their smartphones safely?—Not really! Accident Analysis and Prevention, 96, 29–38.CrossRef
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit‐formation. The Journal of Comparative Neurology.
You, C.-W., Montes-de-Oca, M., Bao, T. J., Lane, N. D., Lu, H., Cardone, G., Torresani, L., & Campbell, A. T. (2012). CarSafe: A driver safety app that detects dangerous driving behavior using dual-cameras on smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 671–672). New York, NY, USA: ACM.
Young, J., Wessnitzer, J. (2016). Descriptive statistics, graphs, and visualisation. In J. Robertson & M. Kaptein (eds.), Modern statistical methods for HCI (pp. 37–56). Springer International Publishing.
Zhang, Y., Lin, W. C., & Chin, Y. K. S. (2010). A pattern-recognition approach for driving skill characterization. IEEE Transactions on Intelligent Transportation Systems, 11, 905–916.CrossRef
Metadaten
Titel
Engaged Drivers–Safe Drivers: Gathering Real-Time Data from Mobile and Wearable Devices for Safe-Driving Apps
verfasst von
Fabius Steinberger
Ronald Schroeter
Diana Babiac
Copyright-Jahr
2017
Buch
Automotive User Interfaces

Print ISBN: 978-3-319-49447-0

Electronic ISBN: 978-3-319-49448-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-49448-7_2

Neuer Inhalt

    Bildnachweise