Scenario-based testing of automated driving functions has become a promising method to reduce time and cost compared to real-world testing. In scenario-based testing automated functions are evaluated in a set of pre-defined scenarios. These scenarios provide information about vehicle behaviors, environmental conditions, or road characteristics using parameters. To create realistic scenarios, parameters and parameter dependencies have to be fitted utilizing real-world data. However, due to the large variety of intersections and movement constellations found in reality, data may not be available for certain scenarios. This paper proposes a methodology to systematically analyze relations between parameters of scenarios. Bayesian networks are utilized to analyze causal dependencies in order to decrease the amount of required data and to transfer causal patterns creating unseen scenarios. Thereby, infrastructural influences on movement patterns are investigated to generate realistic scenarios on unobserved intersections. For evaluation, scenarios and underlying parameters are extracted from the inD dataset. Movement patterns are estimated, transferred and checked against recorded data from those initially unseen intersections.

翻译：基于场景的自动驾驶功能测试已被证明是一种有前景的方法，相较于真实世界测试能减少时间和成本。在基于场景的测试中，自动驾驶功能将在一组预定义场景中进行评估。这些场景通过参数提供关于车辆行为、环境条件或道路特征的信息。为创建逼真的场景，需要利用真实世界数据对参数及参数依赖关系进行拟合。然而，由于现实中交叉口和运动构型的多样性，某些场景可能缺乏可用数据。本文提出了一种系统分析场景参数间关系的方法。利用贝叶斯网络分析因果关系，以减少所需数据量并迁移因果模式以生成未见场景。由此，通过研究基础设施对运动模式的影响，可在未观测交叉口生成逼真场景。为进行评估，从inD数据集中提取场景及底层参数。对运动模式进行估计、迁移，并与这些初始未见交叉口的记录数据进行对比验证。