The object perception of automated driving systems must pass quality and robustness tests before a safe deployment. Such tests typically identify true positive (TP), false-positive (FP), and false-negative (FN) detections and aggregate them to metrics. Since the literature seems to be lacking a comprehensive way to define the identification of TPs/FPs/FNs, this paper provides a checklist of relevant functional aspects and implementation details. Besides labeling policies of the test set, we cover areas of vision, occlusion handling, safety-relevant areas, matching criteria, temporal and probabilistic issues, and further aspects. Even though the checklist cannot be fully formalized, it can help practitioners minimize the ambiguity of their tests, which, in turn, makes statements on object perception more reliable and comparable.

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