AUTOMATUM DATA: Drone-based highway dataset for the development and validation of automated driving software for research and commercial applications
Paul Spannaus, Peter Zechel, Kilian Lenz
Abstract
Recent innovation in highly automated driving in industrial and scientific domains has created a growing demand for logical description of statistically meaningful real-world motion data. On one hand this data supports learning-based probabilistic methods in software development while on the other it allows validation and testing. The AUTOMATUM DATA dataset is a new dataset which is now available at automatum-data.com, and was generated initially using 12 characteristic highway-like scenes from 30 hours of drone videos. The processing pipeline for determining the object trajectories was validated with reference vehicles, where the relative speed error was less than 0.2 percent. To generate the dataset described in this study, the objects from the drone videos were first identified and classified. The detected objects were then linked to their coordinate system results to produce valid object trajectories. The presented dataset is freely available for future research and development-based endeavors (Creative Commons license model CC BY-ND).