In densely populated metropolitan cities, people spend an average of 45-150 minutes inside a vehicle commuting to their workplace. Most of these travelers often experiences short term health conditions like drowsiness, breathlessness, discomfort and headache. Many accidents have been reported due to drowsiness while driving. There has been considerable increase in the number of death/choking cases reported due to CO 2 buildup inside the vehicle. Even though Vehicle Indoor Air Quality is an important parameter, the general awareness about it is very low, and only few high end new models of automobiles have indoor air quality sensing systems, with limited capability. This paper attempts to use a sensing system that can be retrofitted in any old or new vehicle to measure the CO 2 level inside the vehicle and use this sensed data to perform statistical and stochastic analysis of the growth pattern of CO 2 inside the vehicle chamber. © 2018 IEEE.

Divya Lohani

Computer Science and Engineering

(SoE) School of Engineering

Barthwal A.

Acharya D.

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Shiv Nadar University

Predictive Modelling of In-Vehicle CO 2 Concentration Using Sensor Data Analytics

Proceedings of IEEE Sensors

A working person on an average spends 1.5–2 h every day traveling either to their places of work or for other daily activities, using metros, trams, buses, and cars, as common modes of travel. Most of such commuters regularly suffer from health conditions like headache, breathless condition, drowsiness, etc. Numerous accidents have been reported due to drowsiness while driving, which may occur due to the build-up of carbon-dioxide (CO2) build in the vehicle chamber. This paper attempts to monitor, analyze, and predict air quality inside the vehicle. This work proposes a sensing system using an off-the-shelf sensor Sensordrone which is connected to an Android Smartphone using Bluetooth Low Energy. The data obtained from the proposed sensing system are then utilized to perform predictive analysis of CO2 build-up inside the vehicular chamber using Auto Regressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR). Root-Mean-Square Error for SVR and ARIMA models is 47.91 ppm and 55.32 ppm CO2, respectively, indicating that SVR outperformed ARIMA in predicting the CO2 build-up inside the vehicle. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.