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The advent of in-vehicle networking systems as well as state-of-the-art sensors and communication technologies have facilitated the collection of large volume and almost real-time data on vehicles and drivers, thus opening up future possibilities. Processing and analyzing this data provides unprecedented opportunities to offer remarkable insights and solutions for driving behavior analysis (DBA). Characterizing driving behavior plays a key role in a variety of research areas such as traffic safety, the development of automated vehicles, energy and fuel management, risk assessment, and driver identification and profiling. Advances in DBA-based driver inattention or drunk driver detection can help reduce fatal car crashes, and understanding the driving style (e.g. eco-friendly or aggressive) of drivers can contribute to fuel management and risk assessment of the drivers. These facts have led to a growing interest in addressing DBA challenges. This paper aims to present the state-of-the-art methodologies for DBA and provide a clear roadmap about the main current and future trends in DBA. To this end, we propose categorizing the current research on driving behavior based on the types of data employed for the analysis, the ultimate goals of the analysis, and the techniques based on which the driving data are modeled. We provide an overview of different data resources and available datasets for DBA. Moreover, we discuss the application of DBA along with the key research challenges in this field and potential future directions.
Azadani et al. (Thu,) studied this question.