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From the Simulator to the Road—Realization of an In-Vehicle Interface to Support Fuel-Efficient Eco-Driving

  • Craig AllisonEmail author
  • James Fleming
  • Xingda Yan
  • Neville Stanton
  • Roberto Lot
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

Abstract

Motivated by the observation that modifying driver behavior can significantly reduce fuel usage and CO2 emissions, this paper documents the development of a dedicated in-vehicle interface to support eco-driving. This visual interface has been tested in simulator conditions, demonstrating an 8.5% reduction in fuel use, and will soon be deployed on-road. Transitioning from simulator testing to on-road testing presents significant challenges to ensure driver safety and system effectiveness in the presence of changing road conditions and imperfect information about the current driving scenario.

Keywords

Human factors Interface development User testing Eco-driving 

Notes

Acknowledgements

This work was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/N022262/1 “Green Adaptive Control for Future Interconnected Vehicles” (www.g-active.uk).

References

  1. 1.
    International Energy Agency (IEA): Key World Energy Statistics. https://www.iea.org/publications/freepublications/publication/KeyWorld2017.pdf
  2. 2.
    Skeie, R.B., Fuglestvedt, J., Berntsen, T., Lund, M.T., Myhre, G., Rypdal, K.: Global temperature change from the transport sectors: historical development and future scenarios. Atmos. Environ. 43, 6260–6270 (2009)CrossRefGoogle Scholar
  3. 3.
    Rose, G., Marfurt, H.: Travel behaviour change impacts of a major ride to work day event. Trans. Res. Part A Pol. Prac. 41, 351–364 (2007)CrossRefGoogle Scholar
  4. 4.
    Lorf, C., Martı́nez-Botas, R.F., Howey, D.A., Lytton, L., Cussons, B.: Comparative analysis of the energy consumption and CO2 emissions of 40 electric, plug-in hybrid electric, hybrid electric and internal combustion engine vehicles. Tran. Res. D: Trans. Env. 23, 12–19 (2013)CrossRefGoogle Scholar
  5. 5.
    Yilmaz, M., Krein, P.T.: Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Trans. Power. Elec. 28, 2151–2169 (2013)CrossRefGoogle Scholar
  6. 6.
    Barkenbus, J.N.: Eco-driving: an overlooked climate change initiative. Energy Policy 38, 762–769 (2010)CrossRefGoogle Scholar
  7. 7.
    Birol, F.: CO2 emissions from fuel combustion-highlights. Int. Energy Agency (2011)Google Scholar
  8. 8.
    Delhomme, P., Cristea, M., Paran, F.: Self-reported frequency and perceived difficulty of adopting eco-friendly driving behavior according to gender, age, and environmental concern. Trans. Res. D Trans. Environ. 20, 55–58 (2013)CrossRefGoogle Scholar
  9. 9.
    Lai, W.T.: The effects of eco-driving motivation, knowledge and reward intervention on fuel efficiency. Trans. Res. D Trans. Environ. 34, 155–160 (2015)CrossRefGoogle Scholar
  10. 10.
    Meschtscherjakov, A., Wilfinger, D., Scherndl, T., Tscheligi, M.: Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. In: Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 81–88. ACM (2009)Google Scholar
  11. 11.
    Yan, X., Fleming, J., Allison, C., Lot, R.: Portable automobile data acquisition module (ADAM) for naturalistic driving study. In: 15th European Automotive Congress (2017)Google Scholar
  12. 12.
    Fleming, J.M., Allison, C.K., Yan, X., Stanton, N.A., Lot, R.: Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data. Safety Sci. (2018)Google Scholar
  13. 13.
    Fleming, J.M., Yan, X., Allison, C.K., Stanton, N.A., Lot, R.: Driver Modeling and Implementation of a Fuel-saving ADAS. IEEE Conference on Systems, Man and Cybernetics (SMC) (2018)Google Scholar
  14. 14.
    Allison, C.K., Stanton, N.A., Fleming, J.M., Yan, X., Goudarzi, F., Lot, R.: Inception, ideation and implementation; developing interfaces to improve drivers’ fuel efficiency. Paper to be presented at Chartered Institute of Ergonomics and Human Factors (CIHEF) Ergonomics & Human Factors, Birmingham, UK (2018)Google Scholar
  15. 15.
    Matthews, G., Joyner, L., Gilliland, K., Campbell, S., Falconer, S., Huggins, J.: Validation of a comprehensive stress state questionnaire: towards a state big three. Pers. Psych. Eur. 7, 335–350 (1999)Google Scholar
  16. 16.
    Matthews, G., Campbell, S.E., Falconer, S., Joyner, L.A., Huggins, J., Gilliland, K., et al.: Fundamental dimensions of subjective state in performance settings: task engagement, distress, and worry. Emotion 2(4), 315 (2002)CrossRefGoogle Scholar
  17. 17.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Advances in psychology, vol. 52, pp. 139–183. Elsevier (1988)Google Scholar
  18. 18.
    Brooke, J.: SUS: a “quick and dirty usability scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClelland, I.L. (eds.) Usability evaluation in industry (189–194). Taylor and Francis, London (1996)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Craig Allison
    • 1
    Email author
  • James Fleming
    • 1
  • Xingda Yan
    • 1
  • Neville Stanton
    • 1
  • Roberto Lot
    • 1
  1. 1.Faculty of Engineering and Physical SciencesUniversity of SouthamptonSouthamptonUK

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