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In this work, we utilized the methodology outlined in the IEEE Standard2846-2022 for "Assumptions in Safety-Related Models for Automated DrivingSystems" to extract information on the behavior of other road users in drivingscenarios. This method includes defining high-level scenarios, determiningkinematic characteristics, evaluating safety relevance, and making assumptionson reasonably predictable behaviors. The assumptions were expressed askinematic bounds. The numerical values for these bounds were extracted usingPython scripts to process realistic data from the UniD dataset. The resultinginformation enables Automated Driving Systems designers to specify theparameters and limits of a road user's state in a specific scenario. Thisinformation can be utilized to establish starting conditions for testing avehicle that is equipped with an Automated Driving System in simulations or onactual roads.

Extraction of Road Users' Behavior From Realistic Data According to Assumptions in Safety-Related Models for Automated Driving Systems