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.

通过采用现有方法扩展到城市领域，本研究定义感知数据集中的相关对象，并提供了一种基于运动预测组件的新的相关性验证方法，通过考虑大规模数据集中的预测性能的统计分布来量化对预测的影响，成功地验证了所提出的相关性标准的有效性。