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)


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.


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



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).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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