Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique
Abdul Syafiq Abdull Sukor, Goh Chew Cheik, Latifah Munirah Kamarudin, Xiaoyang Mao, Hiromitsu Nishizaki, Ammar Zakaria, Syed Muhammad Mamduh Syed Zakaria
Abstract
In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.