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The object perception of automated driving systems must pass quality androbustness tests before a safe deployment. Such tests typically identify truepositive (TP), false-positive (FP), and false-negative (FN) detections andaggregate them to metrics. Since the literature seems to be lacking acomprehensive way to define the identification of TPs/FPs/FNs, this paperprovides a checklist of relevant functional aspects and implementation details.Besides labeling policies of the test set, we cover areas of vision, occlusionhandling, safety-relevant areas, matching criteria, temporal and probabilisticissues, and further aspects. Even though the checklist cannot be fullyformalized, it can help practitioners minimize the ambiguity of their tests,which, in turn, makes statements on object perception more reliable andcomparable.

Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving