An Explainable and Evolving Car Driver Identification System based on Decision Trees

Shared mobility represents a more and more widespread model ensuring several advantages for citizens and reducing gas emissions. The birth of car-sharing models drives the necessity to use car monitoring systems able to reduce the possibility that unauthorized people drive a certain car. In this paper, we discuss the architecture of car driver identification systems based on incremental fuzzy decision trees. The main features of the proposed system are i) the explainability, namely the possibility of giving explanations regarding its decisions, provided in terms of linguistic rules, and ii) the possibility of continuously updating the classification model. We show the preliminary results of an experimental campaign in which we compare both fuzzy and non-fuzzy incremental decision trees, both in terms of classification performance and model complexity/explainability