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Article summary:
1. This article provides a survey of perception datasets for anomaly detection in autonomous driving.
2. It includes datasets that are public and available, provide sensor data from the ego-perspective, and include pixel- or point-wise anomaly labels.
3. The article also provides an extensive discussion on similarities, issues, and research gaps related to these datasets.
Article analysis:
The article is generally reliable and trustworthy as it provides a comprehensive overview of perception datasets for anomaly detection in autonomous driving. The authors have provided clear selection criteria for the datasets included in the survey and have excluded works which do not meet their criteria. Furthermore, they have provided detailed information about each dataset such as its year of publication, sensors used, size of test/validation sets, resolution, anomaly source, temporal information, number of out-of-distribution classes and ground truth labels. Additionally, they have provided an extensive discussion on similarities, issues and research gaps related to these datasets which can be useful for researchers in this field.
The only potential bias in the article is that it does not provide any counterarguments or explore any unexplored points of consideration regarding the use of these datasets for anomaly detection in autonomous driving. However, this is understandable given that the purpose of the article is to provide a survey rather than to make any claims or arguments about the use of these datasets.
Topics for further research:
Autonomous driving anomaly detection methods
Autonomous driving anomaly detection datasets
Autonomous driving anomaly detection performance evaluation
Autonomous driving anomaly detection challenges
Autonomous driving anomaly detection applications
Autonomous driving anomaly detection research trends