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.

Divya Lohani

Computer Science and Engineering

(SoE) School of Engineering

Kumar N.

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

Modeling IoT Based Automotive Collision Detection System Using Support Vector Machine

Advances in Intelligent Systems and Computing

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.

2016 IEEE Annual India Conference, INDICON 2016

Real time in-vehicle air quality monitoring using mobile sensing

IEEE Access

Delay-Aware Accident Detection and Response System Using Fog Computing

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

QUICKEST FREEWAY ACCIDENT DETECTION UNDER UNKNOWN POST-ACCIDENT CONDITIONS

Neural Networks

Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels

Neurocomputing

Mean Absolute Percentage Error for regression models