  In this paper, we propose SceNDD: a scenario-based naturalistic driving
dataset that is built upon data collected from an instrumented vehicle in
downtown Indianapolis. The data collection was completed in 68 driving sessions
with different drivers, where each session lasted about 20--40 minutes. The
main goal of creating this dataset is to provide the research community with
real driving scenarios that have diverse trajectories and driving behaviors.
The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as
non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time.
Certain flexibility is provided to users so that actors, sensors, lanes, roads,
and obstacles can be added to the existing scenarios. We used a Joint
Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the
road. We present some preliminary results of the proposed dataset and a few
applications associated with it. The complete dataset is expected to be
released by early 2023.
