Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision-Making

Driving styles summarize different driving behaviors that reflect in the movements of the vehicles. These behaviors may indicate a tendency to perform riskier maneuvers, consume more fuel or energy, break traffic rules, or drive carefully. Therefore, this paper presents a driving style recognition using Interval Type-2 Fuzzy Inference System with Multiple Experts Decision-Making for classifying drivers into calm, moderate and aggressive. This system receives as input features longitudinal and lateral kinematic parameters of the vehicle motion. The proposed approach was evaluated using descriptive statistics analysis, and compared with clustering algorithms and a type-1 fuzzy inference system. The results show the tendency to associate lower and consistent kinematic profiles for the driving styles classified with the type-2 fuzzy inference system when compared to other algorithms.