On the prediction of air quality within vehicles using outdoor air pollution: sensors and machine learning algorithms

Abstract

Environmental conditions within vehicles represent a significant element of the driver's well-being and comfort. In particular, exposure to air pollution has been proven to affect human cognitive performances, hence it could represent a risk to driving safety. Monitoring internal and external environmental data could provide interesting hints, helpful in predicting trends and situations potentially dangerous and/or unease, that should be reported, enhancing the driver's awareness. This paper presents a study we have conducted with the aim of predicting indoor vehicle environmental conditions, thanks to a campaign of data collection. In particular, we have adopted a multi-sensor kit, installed within and outside a vehicle, then we have exploited driving sessions in a urban environment. Different machine learning algorithms have been adopted to test their accuracy in predicting internal conditions, on the basis of external ones, discussing the obtained results.