Employing Sensor and Service Fusion to Assess Driving Performance

Abstract

The remarkable increase in the use of sensors in our daily lives has provided an increasing number of opportunities for decision-making and automation of events. Opportunities for decision-making have further risen with the advent of smart technology and the omnipresence of sensors. Various methods have been devised to detect different events in a driving environment using smart-phones as they provide two main advantages: they remove the need to have dedicated hardware in vehicles and they are widely accessible.

Rewarding safe driving has always been an important issue for insurance companies. With this intention, they are now moving toward implementing plans that consider current driving usage (Usage-based-drive plans) in contrast with traditional history-based-plans. The detection of driving events is important in insurance telematics for this purpose. Events such as acceleration and turning are good examples of important information. The sensors are capable of detecting whether a car is accelerating or braking, while through fusing services we can detect other events like speeding or the occurrence of a severe weather phenomenon that can affect driving.

This thesis aims to look at the telematics from a new angle that employs smart-phones as the sensing platform. We proposed a new hybrid classification algorithm that detects acceleration-based events with an F1-score of 0.9304 and turn events with an F1-score of 0.9038. We further performed a case study on measuring the performance of driving utilizing various measures. This index can be used by a wide range of benefactors such as the insurance and transportation industries.