Advertisement

Towards a Reality-Enhanced Serious Game to Promote Eco-Driving in the Wild

  • Rana MassoudEmail author
  • Francesco BellottiEmail author
  • Stefan PosladEmail author
  • Riccardo BertaEmail author
  • Alessandro De GloriaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11899)

Abstract

Reality-enhanced serious games (RESGs) incorporate data from the real world to enact training in the wild. This – with the proper cautions due to safety - can be done also for daily activities, such as driving. We have developed two modules that may be integrated as field user performance evaluators in third-party RESGs, aimed at improving driver’s fuel efficiency. They exploit vehicular signals (throttle position, engine revolutions per minute and car speed), which are easily accessible through the common On-Board Diagnostics-II (OBD-II) interface. The first module detects inefficient and risky driving manoeuvres while driving, in order to suggest improvement actions based upon fuzzy rules, derived from analyzing naturalistic driving data. The second module provides an eco-driving categorization for a drive via two indicators, fuel efficiency and throttle position values. The estimation of fuel efficiency for the whole trip relies on the mentioned signals, plus the OBD-II calculated engine load. Data from ‘enviroCar’ project’s, a naturalistic driving archive, was used in a simulation. The results are promising in terms of accuracy and encourage further steps towards more effective modules to support a better driving performance, for RESGs.

Keywords

Eco-driving Gamification Serious game (SG) Reality-enhanced serious game (RESG) Driving pattern Fuel consumption (FC) Fuel efficiency 

Notes

Acknowledgements

This research was partially funded as part of a Joint Doctorate Interactive and Cognitive Environments (JD-ICE) between the University of Genova, Elios Lab, in agreement with Queen Mary University of London. We also acknowledge technical support given by the enviroCar open Citizen Science Platform (in from 52 North).

References

  1. 1.
    Massoud, R., Poslad, S., Bellotti, F., Berta, R., Mehran, K., De Gloria, A.: A fuzzy logic module to estimate a driver’s fuel consumption for reality-enhanced serious games. Int. J. Serious Games 5(4), 45–62 (2018)CrossRefGoogle Scholar
  2. 2.
    Massoud, R., Bellotti, F., Poslad, S., Berta, R., De Gloria, A.: Exploring fuzzy logic and random forest for car drivers’ fuel consumption estimation in IoT-enabled serious games. In: IEEE International Symposium on Autonomous Decentralized Systems (ISADS) (2019)Google Scholar
  3. 3.
    Van Mierlo, J., Maggetto, G., Van de Burgwal, E., Gense, R.: Driving style and traffic measures-influence on vehicle emissions and fuel consumption. Proc. Inst. Mech. Eng. D J. Automob. Eng. 218, 43–50 (2004)CrossRefGoogle Scholar
  4. 4.
    Magana, V.C., Munoz-Organero, M.: GAFU: using a gamification tool to save fuel. IEEE Intell. Transp. Syst. Mag. 7(2), 58–70 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhao, X., Wu, Y., Rong, J., Zhang, Y.: Development of a driving simulator based eco-driving support system. Transp. Res. Part C Emerg. Technol. 58, 631–641 (2015)CrossRefGoogle Scholar
  6. 6.
    Tulusan, J., Soi, L., Paefgen, J., Brogle, M., Staake, T.: Eco-efficient feedback technologies: Which eco-feedback types prefer drivers most? In: 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1–8. IEEE (2011)Google Scholar
  7. 7.
    Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification. using game-design elements in non-gaming contexts. In: CHI 2011 Extended Abstracts on Human Factors in Computing Systems, 2425–2428. ACM (2011)Google Scholar
  8. 8.
    Bellotti, F., Berta, R., De Gloria, A.: Designing effective serious games: opportunities and challenges for research. Int. J. Emerg. Technol. Learn. (iJET) 5(2010) (2010)Google Scholar
  9. 9.
    Ritterfeld, U., Cody, M., Vorderer, P.: Serious Games: Mechanisms and Effects. Routledge, Abingdon (2009)CrossRefGoogle Scholar
  10. 10.
    Bellotti, F., et al.: TEAM applications for collaborative road mobility. IEEE Trans. Ind. Inf. 15(2), 1105–1119 (2018)CrossRefGoogle Scholar
  11. 11.
    Drakoulis, R., Bellotti, F., Bakas, I., Berta, R., Paranthaman, P.K., et al.: A gamified flexible transportation service for on-demand public transport. IEEE Trans. Intell. Transp. Syst. 19(3), 921–933 (2018)CrossRefGoogle Scholar
  12. 12.
    Diewald, S., Möller, A., Roalter, L., Stockinger, T., Kranz, M.: Gameful design in the automotive domain: review, outlook and challenges. In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 262–265. ACM (2013)Google Scholar
  13. 13.
    Poslad, S., Hamdi, M., Abie, H.: Adaptive security and privacy management for the internet of things (ASPI). In: Proceedings of the ACM conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 373–378 (2013)Google Scholar
  14. 14.
    Djordjevic, I., Dimitrakos, T.: Towards dynamic security perimeters for virtual collaborative networks. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 191–205. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24747-0_15CrossRefGoogle Scholar
  15. 15.
    Fijnheer, J.D., van Oostendorp, H.: Steps to design a household energy game. In: de De Gloria, A., Veltkamp, R. (eds.) GALA 2015. LNCS, vol. 9599, pp. 12–22. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40216-1_2CrossRefGoogle Scholar
  16. 16.
    Bellotti, F., Berta, R., Ferretti, E., DeGloria, A., Margarone, M.: VeGame: exploring art and history in venice. IEEE Comput. Spec. Issue Handheld Comput. 36(9), 48–55 (2003)Google Scholar
  17. 17.
    Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1609–1615 (2011)Google Scholar
  18. 18.
    Godavarty, S., Broyles, S., Parten, M.: Interfacing to the on-board diagnostic system. In: 52nd IEEE Vehicular Technology Conference (IEEE-VTS), vol. 4 (2000)Google Scholar
  19. 19.
    Bröring, A., Remke, A., Stasch, C., Autermann, C., Rieke, M., Möllers, J.: Envirocar: a citizen science platform for analyzing and mapping crowd-sourced car sensor data. Trans. GIS 19(3), 362–376 (2015)CrossRefGoogle Scholar
  20. 20.
    Saboohi, Y., Farzaneh, H.: Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Appl. Energy 86(10), 1925–1932 (2009)CrossRefGoogle Scholar
  21. 21.
    Khedkar, S., Oswal, A., Setty, M., Ravi, S.: Driver evaluation system using mobile phone and OBD-II system. Int. J. Comput. Sci. Inf. Technol. 6(3), 2738–2745 (2015)Google Scholar
  22. 22.
    Araújo, R., Igreja, A., De Castro, R., Araujo, R.E.: Driving coach: a smartphone application to evaluate driving efficient patterns. In: Intelligent Vehicles Symposium. IEEE, pp. 1005–1010 (2012)Google Scholar
  23. 23.
    Mei, H., Poslad, S., Du, S.: A game-theory based incentive framework for an intelligent traffic system as part of a smart city initiative. Sensors 17(12), 2874 (2017)CrossRefGoogle Scholar
  24. 24.
    Poslad, S., Ma, A., Wang, Z., Mei, H.: Using a smart city IoT to incentivise and target shifts in mobility behaviour – is it a piece of pie? Sensors 15(6), 13069–13096 (2015)CrossRefGoogle Scholar
  25. 25.
    Law, F.L., Kasirun, Z.M., Wang, Z., Mei, H.: Gamification towards sustainable mobile application. In: Malaysian Conference in Software Engineering, pp. 349–353. IEEE (2011)Google Scholar
  26. 26.
    Liimatainen, H.: Utilization of fuel consumption data in an ecodriving incentive system for heavy-duty vehicle drivers. IEEE Trans. Intell. Transp. Syst. 12(4), 1087–1095 (2011)CrossRefGoogle Scholar
  27. 27.
    Ando, R., Nishihori, Y., Ochi, D.: Development of a system to promote eco-driving and safe-driving. In: Balandin, S., Dunaytsev, R., Koucheryavy, Y. (eds.) NEW2AN/ruSMART -2010. LNCS, vol. 6294, pp. 207–218. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14891-0_19CrossRefGoogle Scholar
  28. 28.
    OpenStreetMap. http://www.openstreetmap.org. Accessed 26 June 2019
  29. 29.
    Massoud, R., Bellotti, F., Poslad, S., Berta, R., De Gloria, A.: Eco-driving profiling and behavioral shifts using IoT Vehicular sensors combined with serious games eco-driving profiling and behavioral shifts using IoT vehicular sensors combined with serious games. In: IEEE Conference On Games (COG) (2019)Google Scholar
  30. 30.
    Faiz, A., Weaver, C.S., Walsh, M.P.: Air pollution from motor vehicles: standards and technologies for controlling emissions. The World Bank (1996)Google Scholar
  31. 31.
  32. 32.
    Carvalho, M.B., et al.: An activity theory-based model for serious games analysis and conceptual design. Comput. Educ. 87, 166–181 (2015)CrossRefGoogle Scholar
  33. 33.
    Carvalho, M.B.: A case study on service-oriented architecture for serious games. Entertain. Comput. 6, 1–10 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Elios LabUniversity of GenoaGenoaItaly
  2. 2.IoT2US LabQueen Mary University of LondonLondonUK

Personalised recommendations