Driver behaviour profiling using smartphone sensory data in a V2I environment

Abstract

Road traffic accidents prevention and mitigation are important issues that appear on top of the priority list of many countries around the world today. Many measures and approaches have been put in place in terms of policy level as well as technical level. Driver behaviour is one of many key factors that should be seriously considered to improve road safety. This paper proposes a method for driver behaviour profiling using sensory data on smartphones in a vehicle-to-infrastructure environment. Based on driving behaviours with the most risk to causing accidents, the profiling algorithm takes into account sudden driving events which occur during a journey to categorise drivers into different profiles according to their safety levels. The profiling algorithm offers the flexibility to adjust the parameters weightings in order to put an emphasis on specific driving events for different scenarios and applications. The impact on vehicle-to-infrastructure is that the stored driving profiles can be used to generate a norm for a given road section. Approaching vehicles deviating from the norm can be notified in real-time. Moreover, localised dangerous driving events can be clustered together to form a potential blackspot which can be deployed as an advanced warning for approaching vehicles as a location based service. As a result, the risk of road traffic accidents can be reduced. Real-world driving data was collected over two major routes in Thailand with four distinct profiles and five major factors to road accidents.