Validation study of risky event classification using driving pattern factors

In recent years, due to the increasing sensing capabilities of mobile devices, smartphones have become a suitable solution for telematics systems. By combining multiple sensors, GPS information and environmental data, smartphones can be used to detect abnormal driving events that are used to compute driving scores. In this paper we propose a Multivariate Normal model for abnormal driving events detection. This model takes as input smartphone motion sensors and GPS data and detects abnormal driving maneuvers that are classified as events of three different classes: acceleration, braking and cornering. Based on these events, a driving score is computed. In order to validate the reliability of the computed scores, we propose a correlation analysis of the driving score against multiple well-known driving pattern factors proposed in the literature.