With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.

翻译：随着道路交通中自动驾驶功能及其运行设计域（ODD）的复杂性与关键性日益提升，对在虚拟环境中通过仿真模型涵盖开发、验证与确认重要比例的需求不断增加。然而，若仿真不仅旨在增强真实世界实验，更期望替代后者，则需要定量方法来衡量仿真模型在何种程度及何种前提条件下能充分表征现实，并据此合理运用其结果。特别是在与"开放世界"安全影响相关的研发领域，真实世界数据严重匮乏，难以用于仿真参数化及/或验证——尤其是针对自动驾驶功能将在混合交通中遭遇的人类交通参与者的行为。本文提出一种方法：通过异构手段系统采集公共道路数据，将其转换为统一表征，并自动用于交通行为模型参数化，以支持自动驾驶功能的数据驱动虚拟验证。