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This study introduces reality-enhanced serious games (RESGs) for field training, using machine learning to evaluate driver performance via fuel consumption. The system provides real-time feedback, showing high accuracy in assessing driving efficiency.

Keywords:
Internet of Things (IoT)eco-drivingfuel consumptionmachine learning (ML)on-board diagnostic-II (OBD-II)reality-enhanced serious games (RESGs)serious game (SG)

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Area of Science:

  • Computer Science
  • Machine Learning
  • Internet of Things

Background:

  • Internet of Things (IoT) technologies enable new instructional games called reality-enhanced serious games (RESGs) for field-based training.
  • RESGs can incorporate real-time user performance evaluation using sensor data.
  • Fuel-efficient driving is a key area where RESGs can be applied for performance monitoring.

Purpose of the Study:

  • To investigate user performance evaluation in RESGs using real-world data.
  • To develop and validate a smart sensing dataflow for real-time driver performance assessment and feedback.
  • To promote fuel-efficient driving through a novel RESG application.

Main Methods:

  • Proposed a reference model for a smart sensing dataflow combining two parallel, real-time machine learning modules.
  • Compared machine learning algorithms (support vector regression, random forest, artificial neural networks) for quantitative performance assessment.
  • Implemented an instant recommendation module using fuzzy logic for verbal feedback on driving patterns.
  • Utilized the enviroCar public dataset with On-Board Diagnostic II (OBD II) data for system testing.

Main Results:

  • Random forest showed slightly better performance assessment correlation (R² = 0.99) but higher inference time compared to other algorithms.
  • The fuzzy logic module provided timely verbal recommendations for inefficient driving.
  • The system demonstrated robustness across various driving environments and vehicle models.
  • Achieved high estimation correlation and punctual verbal feedback, confirming the approach's feasibility and effectiveness.

Conclusions:

  • The proposed machine learning-based dataflow is effective for real-time driver performance evaluation in RESGs.
  • RESGs can successfully promote fuel-efficient driving through data-driven feedback.
  • Privacy concerns regarding the use of sensitive personal driving data must be addressed.