A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.