Detection of risky driving events using smartphone based sensing is a growing technology devoted to impact positively driving behaviors. This technology might improve traffic and reduce the number of car accidents. However, data measured from inbuilt smartphone sensors, represented as a multivariate time series, commonly contains strong temporal dynamics. As a result, there is a growing need for developing methods able to handle such dynamics to make any inference based on the data in hand. In this work, we present a