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Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm.

Chew Cheik Goh1,2, Latifah Munirah Kamarudin1,2, Ammar Zakaria2,3

  • 1Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia.

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Summary

This study developed a cloud-based system to monitor and predict in-vehicle air quality using machine learning. Support Vector Regression accurately predicted future air quality, enhancing driver safety.

Keywords:
in-vehicle air qualityinternet of things (IoT)machine learning predictionsmart citysmart mobility

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Area of Science:

  • Environmental Science
  • Automotive Engineering
  • Data Science

Background:

  • In-vehicle air quality significantly impacts driver health and safety.
  • Real-time monitoring and prediction are crucial for proactive management of cabin air.
  • Existing systems often lack predictive capabilities for future air quality conditions.

Purpose of the Study:

  • To develop a real-time, cloud-based system for monitoring and predicting in-vehicle air quality.
  • To utilize machine learning algorithms for analyzing sensor data and forecasting cabin air conditions.
  • To assess the potential of air quality data in predicting driver drowsiness and fatigue.

Main Methods:

  • Integration of five sensors (CO2, particulate matter, speed, temperature, humidity) into a cloud-based system.
  • Real-time data collection and cloud storage from the vehicle cabin.
  • Development and evaluation of machine learning models (Multilayer Perceptron, Support Vector Regression, Linear Regression) for air quality prediction.

Main Results:

  • Support Vector Regression demonstrated superior performance in predicting in-vehicle air quality.
  • The SVR model achieved a high coefficient of determination (R^2) of 0.9981, indicating excellent linearity.
  • The system successfully correlated air quality parameters with potential driver fatigue indicators.

Conclusions:

  • The developed cloud-based system provides accurate real-time and predictive in-vehicle air quality monitoring.
  • Machine learning, particularly Support Vector Regression, is effective for forecasting cabin air conditions.
  • This technology has the potential to enhance driver safety by monitoring air quality and predicting fatigue.