Ubiquitous Sensorization for Multimodal Assessment of Driving Patterns

  • Fábio Silva
  • Cesar Analide
  • Celestino Gonçalves
  • João Sarmento
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 291)


Sustainability issues and sustainable behaviours are becoming concerns of increasing significance in our society. In the case of transportation systems, it would be important to know the impact of a given driving behaviour over sustainability factors. This paper describes a system that integrates ubiquitous mobile sensors available on devices such as smartphones, intelligent wristbands and smartwatches, in order to determine and classify driving patterns and to assess driving efficiency and driver’s moods. It first identifies the main attributes for contextual information, with relevance to driving analysis. Next, it describes how to obtain that information from ubiquitous mobile sensors, usually carried by drivers. Finally, it addresses the multimodal assessment process which produces the analysis of driving patterns and the classification of driving moods, promoting the identification of either regular or aggressive driving patterns, and the classification of mood types between aggressive and relaxed. Such an approach enables ubiquitous sensing of personal driving patterns across different vehicles, which can be used in sustainability frameworks, driving alerts and recommendation systems.


Driving Profile Mobile Sensors Sustainability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    André, M.: Driving Cycles Development: Characterization of the Methods. Tech. rep., INRETS (May 1996)Google Scholar
  2. 2.
    Aztiria, A., Izaguirre, A., Augusto, J.C.: Learning patterns in ambient intelligence environments: a survey. Artif. Intell. Rev. 34(1), 35–51 (2010)CrossRefGoogle Scholar
  3. 3.
    Bosse, T., Hoogendoorn, M., Klein, M.C.A., Treur, J.: A Component-Based Ambient Agent Model for Assessment of Driving Behaviour. In: Sandnes, F.E., Zhang, Y., Rong, C., Yang, L.T., Ma, J. (eds.) UIC 2008. LNCS, vol. 5061, pp. 229–243. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behavior by a smartphone. In: 2012 IEEE Intelligent Vehicles Symposium, vol. (254), pp. 234–239. IEEE (June 2012)Google Scholar
  5. 5.
    Ericsson, E.: Variability in exhaust emission and fuel consumption in urban driving. In: Urban Transport Systems, Proceedings from the 2nd kfb Research Conference, pp. 1–16 (1980)Google Scholar
  6. 6.
    Ericsson, E.: Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment 6(5), 325–345 (2001)CrossRefGoogle Scholar
  7. 7.
    Flach, T., Mishra, N., Pedrosa, L., Riesz, C., Govindan, R.: CarMA. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 2011, p. 135. ACM Press, New York (2011)Google Scholar
  8. 8.
    Gebhard, P.: ALMA: a layered model of affect. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 29–36 (2005)Google Scholar
  9. 9.
    Healey, J., Picard, R.: Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Transactions on Intelligent Transportation Systems 6(2), 156–166 (2005)CrossRefGoogle Scholar
  10. 10.
    Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1609–1615. IEEE (October 2011)Google Scholar
  11. 11.
    Kharrazi, A., Kraines, S., Hoang, L., Yarime, M.: Advancing quantification methods of sustainability: A critical examination emergy, exergy, ecological footprint, and ecological information-based approaches. Ecological Indicators, Part A 37, 81–89 (2014)CrossRefGoogle Scholar
  12. 12.
    Kuhler, M., Karstens, D.: Improved Driving Cycle for Testing Automotive Exhaust Emissions. Tech. rep., Volkswagenwerk AG (February 1978)Google Scholar
  13. 13.
    Li, K., Lu, M., Lu, F., Lv, Q., Shang, L., Maksimovic, D.: Personalized Driving Behavior Monitoring and Analysis for Emerging Hybrid Vehicles. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 1–19. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Mehrabian, A.: Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14(4), 261–292 (1996)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys 2008, p. 323. ACM Press, New York (2008)Google Scholar
  16. 16.
    Nettle, D.: Personality: What makes you the way you are. OUP Oxford (2007)Google Scholar
  17. 17.
    Oliveira, T., Novais, P., Jose, N.: Guideline Formalization and Knowledge Representation for Clinical Decision Support. Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ) I(2), 1–12 (2012)Google Scholar
  18. 18.
    Paefgen, J., Kehr, F., Zhai, Y., Michahelles, F.: Driving Behavior Analysis with Smartphones: Insights from a Controlled Field Study. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, pp. 36:1–36:8. ACM, USA (2012)Google Scholar
  19. 19.
    Rakotonirainy, A., Tay, R.: In-vehicle ambient intelligent transport systems (I-VAITS): towards an integrated research. In: Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749), pp. 648–651. IEEE (2004)Google Scholar
  20. 20.
    Sadri, F.: Ambient intelligence. ACM Computing Surveys 43(4), 1–66 (2011)CrossRefGoogle Scholar
  21. 21.
    Silva, F., Analide, C., Rosa, L., Felgueiras, G., Pimenta, C.: Ambient Sensorization for the Furtherance of Sustainability. In: van Berlo, A., Hallenborg, K., Rodríguez, J.M.C., Tapia, D.I., Novais, P. (eds.) Ambient Intelligence & Software & Applications. AISC, vol. 219, pp. 179–186. Springer, Heidelberg (2013)Google Scholar
  22. 22.
    Sun, J., Wu, Z.H., Pan, G.: Context-aware smart car: from model to prototype. Journal of Zhejiang University Science A 10(7), 1049–1059 (2009)CrossRefGoogle Scholar
  23. 23.
    Todorov, V., Marinova, D.: Modelling sustainability. Mathematics and Computers in Simulation 81(7), 1397–1408 (2011)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fábio Silva
    • 1
  • Cesar Analide
    • 1
  • Celestino Gonçalves
    • 1
  • João Sarmento
    • 1
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations