An Explainable Approach for Car Driver Identification

The increasing number of always more sophisticated car sensors, which allow to extract information about the driver, encourages auto vehicle developers and researchers to focus on the topic of driver identification. The advantages can be various, such as to customise and improve driver experience, to increase safety and to reduce global environmental problems. This work explores a set of features extracted from a car monitoring system, installed on real cars, to identify the driver on the basis of his/her driving behaviour. The proposed features are leveraged by a Multiobjective Evolutionary Learning Scheme for generating Fuzzy Rule-Based Classifiers characterized by different trade-offs between the classification accuracy and the explainability of the classification models. To evaluate the effectiveness and efficiency of the proposed approach, we carry out an experimental analysis on a real-world dataset, composed by actual measures extracted from 4 cars driven by 4 different drivers. The results show that the fuzzy classification models experimented in this work are more accurate and explaninable than the classification models generated adopting tree-based classifiers, such as decision trees and random forests.