Driving pattern recognition using mobile telematics

Abstract

Mobile Telematics is an application that enables it’s users to detect driving behavior through sensors that already exist on a common smart phone device. Sensors such as Gyroscopes, GPS, Radio-Positioning, Existing Cameras, Location APIs, etc will help detect and identify valuable pieces of information, this can then be used to create unsupervised learning models on the driver’s driving behavior and display feedback for the driver to identify and correct in order to minimize the risk of a potential accident. Current telematics models are not efficient and accurate enough to reduce the chances of road accidents, the sensors required to allow for users to access the needed data may also be expensive. Thus, we have found that our system is not only improved but also relatively inexpensive since we are using devices most of our user base already have -- Smartphones. Our framework consists of three main components -- Self Organizing Maps, Nine-Layers Deep Auto-Encoder, Partitive Clustering Algorithm. The self organizing maps simplifies complex pieces of data into consumable information, the deep auto encoder takes into consideration anomalies of said data and extracts its features, and the partitive clustering algorithms groups the data based on a set of driving behavior classifications.