Effective presenting method for different driving styles based on hexagonal eye diagram applied in pay-how-you-drive vehicle insurance

Pay-how-you-drive (PHYD), which is an advanced product form in usage based insurance (UBI) for vehicle, takes not only driving habit and behavior into consideration in its actuary process, but geographical factors, sociological factors, psychological factors, and even meteorological factors as well. PHYD insurance product is supported by refinement and analysis based on raw driving data of insured vehicles, and the very analysis process, which is based on big data algorithms, helps to recognize the risk level of each driving behavior by finding the relationship between them. Even if studies on risk level determination have been done adequately, research on feedback and presenting of risk evaluation results for the drivers of insured vehicles have not been reported much. In this paper, an effective presenting method for different driving styles based on hexagonal eye diagram which can be applied in PHYD insurance based driver vehicle interface (DVI) has been proposed, six key style evaluation indices have been chosen in such eye diagram. Evaluation examples have shown that by employing different colors which represent the occurrence frequency of certain evaluation results in such eye diagram, the hexagonal eye diagram proposed can describe the most representative driving style for a certain insured vehicle, even if there might be style changing from time to time.

I. Introduction

Information technology and big data analysis algorithms can help the insurance companies to fully master the driving behavior of all the insured vehicles from the raw data collected by the on-board diagnosis (OBD) modules which have been set up on them, and pay-how-you-drive (PHYD) insurance product has then been formed [1], [2]. PHYD, which has been recognized as an advanced product form in usage based insurance (UBI) for vehicle, takes not only driving habit and behavior into consideration in its actuary process, but geographical factors, sociological factors, psychological factors, and even meteorological factors as well [3]. PHYD insurance product is supported by refinement and analysis based on raw driving data of insured vehicles, and the very analysis process, which is based on big data algorithms, helps to recognize the risk level of each driving behavior by finding the relationship between them. As the insured vehicles which buy PHYD products can get the day-to-day driving evaluation results, timely evaluation results feedback and presenting technique would also be important for drivers because by which the bad driving habit can be improved to some extent.