Worst Perception Scenario Prediction for Testing Autonomous Driving Perception

Recent studies have suggested that potential short-comings of certain perception modules can be discovered by analyzing the performance of worst scenarios. However, finding the worst perception scenario (WPS) requires datasets with rich semantic annotations of the scenes in visual perception tasks and it is time-consuming to label all the scenario data. To address this, we proposed a method of prediction for WPS, which utilized prior information to predict the model performance under the absence of annotations. Specifically, this paper introduced a scenario matcher based on hybrid re-ranking, which combined the labeled and unlabeled data to generate the pseudo-sample set. In addition, we designed a sample reorganization module to update this sample set through the nearest neighbor retrieval. We also discussed the distribution relationship between labeled and unlabeled data, categorizing it into three cases, and validated the effectiveness of the proposed method on the KITTI and ApolloScape datasets.