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.
翻译:驾驶风格总结了在车辆运动中反映的不同驾驶行为。 这些行为可能表明有进行更冒险的动作、 消耗更多的燃料或能量、 破坏交通规则或小心驾驶的倾向。 因此, 本文展示了一种驱动风格识别, 使用跨性类型 2 模糊推断系统, 并配有多种专家决策, 用于将驱动器分类为平静、 中度和进取性。 这个系统作为输入性特征, 用于车辆运动的长度和横向运动参数。 在处理噪音数据时, 类型 2 的模糊装置比类型-1 模糊装置更强大, 因为它们的成员功能也是模糊的。 此外, 多位专家方法可以减少偏差和不准确性, 同时建立模糊规则库, 以存储对模糊系统的知识。 提议的方法是使用描述性统计分析, 并与组合算法和类型-1 模糊推断系统进行比较。 结果显示, 将驱动式类型-2 分类的较低运动特征与类型 1 模糊推断系统联系起来的趋势, 与其他算术比较, 与其他专家采用的方法比较, 比较起来比较比较比较比较比较更为保守的方法。