Extensible platform for studying the behavior of drivers in urban traffic

Research on traffic management and automation focuses on macro-scale problems involving large number of vehicles. It seems that the influence of individual decisions of particular drivers has not received sufficient attention over last decades. Understanding the characteristics of particular drivers is crucial for traffic optimization and for decisions making process performed by automated vehicles. In this paper we would like to present a prototype platform for studying characteristics of individual human drivers. The platform collects data during driving in urban environment and performs analysis and classification of observed behaviors. It is extensible in terms of measured parameters and classification methods. We also present results of first experiment using the created platform - classification of drivers during the left turn maneuver.