Machine learning methods for driver behaviour classification

Driver behaviour detection and evaluation is becoming an essential task for vehicle manufacturers. Driver distraction is the major cause of road accidents and infrastructure deformation. Furthermore, secondary roads accidents are mainly affected, since external distraction and pedestrian presence are higher than highways. In this paper, we propose a comparison of three machine learning classification methods to identify the driver's behaviour on secondary roads. The classification and comparison are based on the evaluation of real data.