Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia.
Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia.
Sensors (Basel). 2021 Jul 21;21(15):4956. doi: 10.3390/s21154956.
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 CO, 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 (). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an of 0.9981.
本文提出了一种实时的基于云的车载空气质量监测系统，该系统能够预测当前和未来的车内空气质量。所设计的系统使用机器学习算法提供预测分析，可根据车内空气质量测量驾驶员的困倦和疲劳程度。该系统由五个传感器组成，可测量 CO 水平、颗粒物、车辆速度、温度和湿度。来自这些传感器的数据实时从车辆座舱中收集并存储在云数据库中。使用多层感知器、支持向量回归和线性回归开发了预测模型来分析数据并预测车内空气质量的未来状况。使用均方根误差、均方误差、平均绝对误差和决定系数（）来评估这些模型的性能。结果表明，支持向量回归表现出色，预测数据与实际数据之间的线性度最高，为 0.9981。
