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

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 (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

[1]  Koji Zettsu,et al.  Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5 , 2015, Neural Computing and Applications.

[2]  Jinglu Hu,et al.  Air Quality Forecasting Using SVR with Quasi-Linear Kernel , 2019, 2019 International Conference on Computer, Information and Telecommunication Systems (CITS).

[3]  Tingli Su,et al.  Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy , 2021, Entropy.

[4]  T. Matton.  Simulation and Analysis of Air Recirculation Control Strategies to Control Carbon Dioxide Build-up Inside a Vehicle Cabin , 2015 .

[5]  I Alameddine,et al.  Operational and environmental determinants of in-vehicle CO and PM2.5 exposure. , 2016, The Science of the total environment.

[6]  W. Fisk,et al.  Is CO2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2 Concentrations on Human Decision-Making Performance , 2012, Environmental health perspectives.

[7]  F. S. A. Saad,et al.  Chicken Farm Malodour Monitoring Using Portable Electronic Nose System , 2012 .

[8]  Ming-Hung Chen,et al.  Carbon Dioxide Concentrations and Temperatures within Tour Buses under Real-Time Traffic Conditions , 2015, PloS one.

[9]  S. Fruin,et al.  Carbon dioxide accumulation inside vehicles: The effect of ventilation and driving conditions. , 2018, The Science of the total environment.

[10]  Joseph G. Allen,et al.  Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments , 2015, Environmental health perspectives.

[11]  T. Cecil Gray,et al.  Carbon Dioxide Retention , 1954, New York state journal of medicine.

[12]  E. Sazakli,et al.  Ozone long-range transport in the Balkans , 2011 .

[13]  Aranildo R. Lima,et al.  Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods , 2017, Air Quality, Atmosphere & Health.

[14]  P. Thirumal,et al.  Optimization of IAQ characteristics of an air-conditioned car using GRA and RSM , 2014 .

[15]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[16]  N HusseinW,et al.  Technology Elements that Influence the Implementation Success for Big Data Analytics and IoT- Oriented Transportation System , 2019, International Journal of Advanced Trends in Computer Science and Engineering.

[17]  Pravin Srinath,et al.  Machine Learning Techniques for Air Quality Forecasting and Study on Real-Time Air Quality Monitoring , 2017, 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA).

[18]  Ammar Zakaria,et al.  Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization , 2018, Adv. Robotics.

[19]  L. M. Kamarudin,et al.  Hand-Held Electronic Nose Sensor Selection System for Basal Stamp Rot (BSR) Disease Detection , 2012, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.

[20]  Omar Makke,et al.  The effectiveness of cloud-based smart in-vehicle air quality management , 2016, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC).

[21]  Bock Cheol Lee,et al.  Vehicle Cabin Air Quality with Fractional Air Recirculation , 2013 .

[22]  B. Tefft,et al.  Prevalence of motor vehicle crashes involving drowsy drivers, United States, 1999-2008. , 2012, Accident; analysis and prevention.

[23]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  Rossitza Setchi,et al.  A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes , 2019, J. Intell. Fuzzy Syst..

[26]  Jian-Lei Kong,et al.  The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods , 2021, Sensors.

[27]  Mikael Olsson.  Table , 2019, CSS3 Quick Syntax Reference.

[28]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[29]  K. Persaud,et al.  A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS , 2018, BMC Cancer.

[30]  Shijin Yuan,et al.  Air Quality Forecast Based on Principal Component Analysis-Genetic Algorithm and Back Propagation Model , 2017 .

[31]  A. Elkamel,et al.  A review of standards and guidelines set by international bodies for the parameters of indoor air quality , 2015 .

[32]  Tanvi Banerjee,et al.  Investigation of an Indoor Air Quality Sensor for Asthma Management in Children , 2017, IEEE Sensors Letters.

[33]  L. M. Kamarudin,et al.  Monitoring of carbon dioxide (CO2) accumulation in vehicle cabin , 2016, 2016 3rd International Conference on Electronic Design (ICED).

[34]  Jennifer C. Dela Cruz,et al.  Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[35]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[36]  Mohd Yasin,et al.  In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology , 2015, BMC Bioinformatics.

[37]  François Clemens,et al.  Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment , 2017 .

[38]  Gregory J. Pottie,et al.  Sensor network data fault types , 2007, TOSN.

[39]  Michael Grady,et al.  On-Road Air Quality and the Effect of Partial Recirculation on In-Cabin Air Quality for Vehicles , 2013 .

[40]  A. Zakaria,et al.  Activity recognition using accelerometer sensor and machine learning classifiers , 2018, 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA).

[41]  Debopam Acharya,et al.  Real time in-vehicle air quality monitoring using mobile sensing , 2016, 2016 IEEE Annual India Conference (INDICON).

[42]  Bin Xu,et al.  Air quality inside motor vehicles' cabins: A review , 2016 .

[43]  S. S. Jain,et al.  Prediction of Bus Travel Time Using ANN: A Case Study in Delhi , 2016 .

[44]  X. Querol,et al.  Vehicle interior air quality conditions when travelling by taxi. , 2019, Environmental research.

[45]  Anca Draghici,et al.  Perception of Cabin Air Quality among Drivers and Passengers , 2016, Sustainability.