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Related Experiment Video

Updated: May 9, 2025

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Machine learning vehicle fuel efficiency prediction.

So-Rin Yoo1, Jae-Woo Shin2, Seoung-Ho Choi3

  • 1Department of AI Application, Hansung University, 116, Samseongyo-ro 16-gil, Seongbuk-gu, Seoul, 02876, Republic of Korea.

Scientific Reports
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts vehicle fuel efficiency using key attributes like fuel type and engine size. This framework identifies critical factors for better decision-making in fuel consumption.

Keywords:
Fuel FrameworkFuel MarkerFuel consumptionMachine learningVehicle Fuel

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Area of Science:

  • Automotive Engineering
  • Data Science
  • Machine Learning

Background:

  • High fuel consumption in vehicles presents environmental and economic challenges.
  • Accurate prediction of fuel efficiency is essential for informed decision-making and vehicle design.

Purpose of the Study:

  • To develop a machine learning framework for predicting vehicle fuel efficiency.
  • To identify key vehicle attributes influencing fuel efficiency predictions.

Main Methods:

  • Utilized a comprehensive framework integrating various vehicle information for prediction.
  • Compared six machine learning models including Extra Trees Regressor and Random Forest Regressor.
  • Employed evaluation metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared score.
  • Applied SHAP, LIME, and odds ratio analysis to interpret model predictions and assess factor impact.

Main Results:

  • The proposed machine learning framework successfully predicted vehicle fuel efficiency.
  • Extra Trees Regressor and Random Forest Regressor exhibited high prediction accuracy, adept at capturing nonlinear relationships.
  • Identified key factors such as fuel type, engine displacement, and vehicle grade significantly impact fuel efficiency.

Conclusions:

  • Machine learning offers a robust approach to predicting vehicle fuel efficiency.
  • Understanding the impact of specific vehicle attributes is crucial for enhancing prediction accuracy and supporting decision-making.
  • The study highlights the importance of interpretable AI methods in automotive applications.