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

TL;DR

This paper introduces a robust driving style recognition system using Interval Type-2 Fuzzy Inference and Multiple Experts Decision-Making, effectively classifying drivers into calm, moderate, and aggressive categories based on vehicle kinematic data.

Contribution

It proposes a novel combination of type-2 fuzzy logic and multiple experts to improve accuracy and reduce bias in driving style classification.

Findings

Type-2 fuzzy system better handles noisy data.

System classifies driving styles with conservative kinematic profiles.

Outperforms clustering and type-1 fuzzy methods.

Abstract

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 type-2 fuzzy sets are more robust than type-1 fuzzy sets when handling noisy data, because their membership function are also fuzzy sets. In addition, a multiple experts approach can reduce the bias and imprecision while building the fuzzy rulebase, which stores the knowledge of the fuzzy system. 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 kinematic profiles for the driving styles classified with the type-2 fuzzy inference system when compared to other algorithms, which is in line with the more conservative approach adopted in the aggregation of the experts' opinions.