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

Abstract: 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.