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

• Main Authors: Chew Cheik Goh, Latifah Munirah Kamarudin, Ammar Zakaria, Hiromitsu Nishizaki, Nuraminah Ramli, Xiaoyang Mao, Syed Muhammad Mamduh Syed Zakaria, Ericson Kanagaraj, Abdul Syafiq Abdull Sukor, Md. Fauzan Elham
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Sensors
• Subjects: internet of things (IoT); machine learning prediction; in-vehicle air quality; smart mobility; smart city
• Online Access: https://www.mdpi.com/1424-8220/21/15/4956

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO₂, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (<i>R</i>²). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an <i>R</i>² of 0.9981.