  Automated vehicles rely heavily on data-driven methods, especially for
complex urban environments. Large datasets of real world measurement data in
the form of road user trajectories are crucial for several tasks like road user
prediction models or scenario-based safety validation. So far, though, this
demand is unmet as no public dataset of urban road user trajectories is
available in an appropriate size, quality and variety. By contrast, the highway
drone dataset (highD) has recently shown that drones are an efficient method
for acquiring naturalistic road user trajectories. Compared to driving studies
or ground-level infrastructure sensors, one major advantage of using a drone is
the possibility to record naturalistic behavior, as road users do not notice
measurements taking place. Due to the ideal viewing angle, an entire
intersection scenario can be measured with significantly less occlusion than
with sensors at ground level. Both the class and the trajectory of each road
user can be extracted from the video recordings with high precision using
state-of-the-art deep neural networks. Therefore, we propose the creation of a
comprehensive, large-scale urban intersection dataset with naturalistic road
user behavior using camera-equipped drones as successor of the highD dataset.
The resulting dataset contains more than 11500 road users including vehicles,
bicyclists and pedestrians at intersections in Germany and is called inD. The
dataset consists of 10 hours of measurement data from four intersections and is
available online for non-commercial research at: http://www.inD-dataset.com
