Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

Abstract

Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to model the quality of the driving behaviour based on FIS (fuzzy inference systems). The models consider vehicle dynamics as long as the human behaviour parameters, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, assessment-driving behaviour model is simulated and tested by two different FISs: Mamdani and Sugeno-TSK. The simulation results illustrate the critical distinctions between the two FISs using the proposed driving behaviour models. These differences are based on various processing times, robust behaviour of the FISs, outputs MFs (membership functions), fuzzification-techniques, flexibility in the systems design and computational efficiency.