Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

Mobile Development

Driving Events

Machine Learning

Classification

With the ever-increasing vehicle population and introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces an innovative solution that aims at understanding vehicle behavior based on sensors data. The behavior is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving and consuming a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption.  This measure will become more valuable as more autonomous vehicles and more ride sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce energy consumption.  By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors and are used to train machine learning algorithms to classify the events.

Alqudah, Yazan

Sababha, Belal

Qaralleh, Esam

Yousseff, Tarek

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 02 (2021); pp. 124-136

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-01-26

Copyright (c) 2021 Yazan Alqudah, Belal Sababha, Esam Qaralleh, Tarek Yousseff

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article