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.

翻译：本研究采用IEEE标准2846-2022中概述的"自动驾驶系统安全相关模型中的假设"方法论，提取驾驶场景中其他道路使用者的行为信息。该方法包括定义高层场景、确定运动学特征、评估安全相关性以及制定关于合理可预测行为的假设。这些假设以运动学边界的形式表示。通过使用Python脚本处理来自UniD数据集的真实数据，提取了这些边界的数值。所得结果使自动驾驶系统设计人员能够针对特定场景指定道路使用者状态的参数和限值。该信息可用于确定搭载自动驾驶系统的车辆在仿真或实际道路测试中所需建立的初始条件。