Analysis of driving data for autonomous vehicle applications

Autonomous vehicle technology has been rapidly expanding through the incorporation of advanced driver assistance systems in many new vehicles. The integration of autonomous vehicle technology to assist and alert drivers is essential to increase driver safety. The main aim of this paper is to (1) compare real driving data from the Next Generation SIMulation I-80 dataset to an "ideal" driving scenario and (2) develop a tool that can be used to prescreen large datasets and filter the data points according to specific study parameters. This proposed tool uses a fuzzy inference system which outputs a warning level based on three inputs including relative velocity between the host and the preceding vehicle, velocity of the host vehicle and time headway. The warning level is used as a measure for initial analysis of real-life driving data to categorize the data and identity "unsafe" driving patterns. The "ideal" driving scenario and real driving data are compared and visualized using a graphical simulation in MATLAB. This visual comparison clearly highlights the importance of the integration of autonomous vehicle technology to increase driver safety.