Vehicle Indoor Air Quality (VIAQ) is a very important parameter that we often tend to ignore in our daily driving. VIAQ can worsen due to build-up of carbon dioxide (CO2), carbon monoxide (CO) and other harmful gases emitted by vehicles. Poor VIAQ can lead to short term health conditions like drowsiness, breathlessness, discomfort and headache. Drowsiness while driving is a predominant health condition that leads to large number of accidents and deaths all over the world. Standard equipment of air intake and recirculation in both low end and high end cars do not completely support pervasive maintenance of VIAQ. Due to unavailability of miniaturized sensors, a general purpose solution to this problem was rarely studied and addressed. In this work, we propose a novel mobile sensing system to monitor and understand change in VIAQ. Our mobile sensing system is general purpose, built by using an Android Smartphone and a mobile sensor, is independent of car age and model, and monitors VIAQ at all times. © 2016 IEEE.

Divya Lohani

Computer Science and Engineering

(SoE) School of Engineering

Acharya D.

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Real time in-vehicle air quality monitoring using mobile sensing

2016 IEEE Annual India Conference, INDICON 2016

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.

Journal of Reliable Intelligent Environments

Modeling vehicle indoor air quality using sensor data analytics

Due to the rise in automotive accidents across the globe, a cheap and reliable system is needed that can be retrofitted in any type of vehicle and can monitor road collision events. This research is aimed to develop an IoT system that uses contemporary smartphone’s intrinsic sensors to accurately report vehicle collision accidents on the road. Absolute linear acceleration (ALA) and the speed of the vehicle have been used to train and test our Support Vector Machine (SVM) based collision detection model. During the testing, the accuracy of the model was found to be very high with a MAPE (mean absolute percentage error) of 0.6\%. © 2021, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Modeling IoT Based Automotive Collision Detection System Using Support Vector Machine

Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone's built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naïve Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score. © 2014 IEEE.

IEEE Internet of Things Journal

An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion

Millions of people get injured, disabled or die in automotive accidents each year. Knowledge about the type of road accident is invaluable to the emergency medical services providers for optimal planning and execution of the rescue operation. An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident. The system uses in-built sensors of passenger smartphone to detect and classify the accident as head-on collision, rollover or fall-off. The accuracy of the proposed system, which uses Naïve Bayes classifier for classification, has been evaluated using precision, recall, F1 score and ROC curve. © 2020 IEEE.

2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings

Modeling IoT Enabled Automotive System for Accident Detection and Classification

Focus on Surfactants

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Drone to the Rescue: Relay-Resilient Authentication using Ambient Multi-sensing

SAE Technical Paper Series

Vehicle Cabin Air Quality with Fractional Air Recirculation

Modeling CO2 Concentrations in Vehicle Cabin

Environmental Health Perspectives

Is CO2an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2Concentrations on Human Decision-Making Performance