  In this work, we utilized the methodology outlined in the IEEE Standard
2846-2022 for "Assumptions in Safety-Related Models for Automated Driving
Systems" to extract information on the behavior of other road users in driving
scenarios. This method includes defining high-level scenarios, determining
kinematic characteristics, evaluating safety relevance, and making assumptions
on reasonably predictable behaviors. The assumptions were expressed as
kinematic bounds. The numerical values for these bounds were extracted using
Python scripts to process realistic data from the UniD dataset. The resulting
information enables Automated Driving Systems designers to specify the
parameters and limits of a road user's state in a specific scenario. This
information can be utilized to establish starting conditions for testing a
vehicle that is equipped with an Automated Driving System in simulations or on
actual roads.
