To evaluate perception components of an automated driving system, it is necessary to define the relevant objects. While the urban domain is popular among perception datasets, relevance is insufficiently specified for this domain. Therefore, this work adopts an existing method to define relevance in the highway domain and expands it to the urban domain. While different conceptualizations and definitions of relevance are present in literature, there is a lack of methods to validate these definitions. Therefore, this work presents a novel relevance validation method leveraging a motion prediction component. The validation leverages the idea that removing irrelevant objects should not influence a prediction component which reflects human driving behavior. The influence on the prediction is quantified by considering the statistical distribution of prediction performance across a large-scale dataset. The validation procedure is verified using criteria specifically designed to exclude relevant objects. The validation method is successfully applied to the relevance criteria from this work, thus supporting their validity.

翻译：为评估自动驾驶系统的感知组件，需定义相关对象。尽管城市场景在感知数据集中较为常见，但该领域对相关性的定义仍不够明确。因此，本研究采用现有方法对高速公路场景的相关性进行定义，并扩展至城市领域。尽管文献中存在多种相关性的概念化定义和界定方式，但缺乏验证这些定义有效性的方法。为此，本文提出一种创新的相关性验证方法，该方法利用运动预测组件，其核心思想为：剔除无关对象不应影响反映人类驾驶行为的预测模块。通过分析大规模数据集中预测性能的统计分布，量化对预测结果的影响。验证流程采用专门设计的排除相关对象的准则进行校验，并成功应用于本文提出的相关性判定标准，从而证实了其有效性。