LAUSR

Recently, several research efforts have been focused on automotive safety, due to the increasing technology embedded in our vehicles. Research community have produced different methods aimed, for instance, to profile driver behaviour, starting from a feature set gathered by the vehicle. The provided methods are mainly machine learning-based: these solutions, as largely demonstrate in literature, suffer from several issues, due to the context variability but also because they are not able to provide a rational reason for the specific prediction. To overcome these limitations, in this paper we propose a novel model checking based approach to driver identification. Furthermore, a novel automatic procedure able to infer a logical representation of the driver behaviour is discussed. Two real-world datasets for the evaluation of the proposed method are considered, obtaining interesting results in driver identification.