Abstract
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.
Abstract (translated)
随着自动驾驶功能在道路交通和其操作设计领域（ODD）中的复杂性和关键性的增加，对在虚拟环境和模拟模型中涵盖开发、验证和验证的比例的需求不断增加。然而，如果仿真不仅仅是为了补充现实世界的实验，而是要替代它们，就需要定量方法来衡量仿真模型在多大程度上以及何时充分代表了现实世界。因此，对于与"开放世界"安全影响相关的R&D领域，特别是在与混合交通中的人类交通参与者行为相关的领域，仿真中缺乏足够的真实世界数据来参数化和/或验证仿真 - 尤其是在与自动驾驶功能混合交通中，自动驾驶功能将遇到的情况。我们提出了一个通过异质手段系统地获取公共交通数据的方法，将其转化为统一的表示，并将其用于自动参数化交通行为模型，以便在数据驱动的虚拟验证中使用自动驾驶功能。
URL
https://arxiv.org/abs/2405.01776
