  This paper presents a comprehensive review of trajectory data of Advanced
Driver Assistance System equipped-vehicle, with the aim of precisely model of
Autonomous Vehicles (AVs) behavior. This study emphasizes the importance of
trajectory data in the development of AV models, especially in car-following
scenarios. We introduce and evaluate several datasets: the OpenACC Dataset, the
Connected & Autonomous Transportation Systems Laboratory Open Dataset, the
Vanderbilt ACC Dataset, the Central Ohio Dataset, and the Waymo Open Dataset.
Each dataset offers unique insights into AV behaviors, yet they share common
challenges in terms of data availability, processing, and standardization.
After a series of data cleaning, outlier removal and statistical analysis, this
paper transforms datasets of varied formats into a uniform standard, thereby
improving their applicability for modeling AV car-following behavior. Key
contributions of this study include: 1. the transformation of all datasets into
a unified standard format, enhancing their utility for broad research
applications; 2. a comparative analysis of these datasets, highlighting their
distinct characteristics and implications for car-following model development;
3. the provision of guidelines for future data collection projects, along with
the open-source release of all processed data and code for use by the research
community.
