Capturing uncertainty in heavy goods vehicles driving behaviour

There is a growing interest in understanding and identifying risky driving behaviours due to the numerous road fatalities attributed to them. For Heavy Goods Vehicles (HGVs), understanding driving behaviour and its impact on road safety is a subject of interest for researchers, the government and industrial sectors, as they rely on HGVs for the delivery of goods and services. The current literature on HGV driving behaviour uses machine learning techniques to uncover core driving incident stereotypes. However, human behaviour contains different levels of uncertainty and stereotyping driving behaviour with traditional crisp methods may cause information loss and establish unfair boundaries as they do not take context into consideration. Moreover, the sensor readings also have uncertainties, and the driving stereotypes may have different subjective interpretations. In order to capture those intermediate possibilities in driver stereotyping, we propose a data-driven Fuzzy Logic system that can capture the uncertainties within driving features (data) and between driving stereotypes, and classifies drivers according to the risk of their driving styles on a scale of 0 to 100, where 0 is a low risk driver and 100 high risk. The results from telematics data show that our proposed method provides a reliable, fair and explainable approach for real-time identification of HGV driving risk level.