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Driving styles summarize different driving behaviors that reflect in themovements of the vehicles. These behaviors may indicate a tendency to performriskier maneuvers, consume more fuel or energy, break traffic rules, or drivecarefully. Therefore, this paper presents a driving style recognition usingInterval Type-2 Fuzzy Inference System with Multiple Experts Decision-Makingfor classifying drivers into calm, moderate and aggressive. This systemreceives as input features longitudinal and lateral kinematic parameters of thevehicle motion. The type-2 fuzzy sets are more robust than type-1 fuzzy setswhen handling noisy data, because their membership function are also fuzzysets. In addition, a multiple experts approach can reduce the bias andimprecision while building the fuzzy rulebase, which stores the knowledge ofthe fuzzy system. The proposed approach was evaluated using descriptivestatistics analysis, and compared with clustering algorithms and a type-1 fuzzyinference system. The results show the tendency to associate lower kinematicprofiles for the driving styles classified with the type-2 fuzzy inferencesystem when compared to other algorithms, which is in line with the moreconservative approach adopted in the aggregation of the experts' opinions.

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