Qiu M, Bazan P, Antesberger T, German R (2023)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Defining sensor errors is the key issue of the reliability assessment of perception sensor systems. Recently, the most common sensor error types considered are false negatives (FNs) and false positives (FPs). Under certain circumstances, a sensor perception result may trigger a pair of correlated FN and FP, e.g., when detecting an object with inaccurate depth estimation. To investigate the frequency of the occurrence of this type of error, and to provide a more explainable interpretation of the sensor errors, we redefine them into nondetections, ghost objects, and localization errors, where localization errors are caused by low-quality perception results. Furthermore, we propose a three-step association algorithm to identify these error types from perception results and the ground truths, utilizing the Hungarian algorithm with distance matrices based on the Generalized Intersection over Union (GIoU) and Intersection over Union (IoU). Moreover, we extend a proven reliability model to account for the new error types and fusion methods, e.g., Kalman filters. Finally, we conduct a case study on the nuScenes dataset to show the significance of the proposed sensor error definitions and reliability model.
APA:
Qiu, M., Bazan, P., Antesberger, T., & German, R. (2023). Improved sensor error definitions for reliability analysis of multi-sensor systems. In Proceedings of the The 7th International Conference on System Reliability and Safety. Bologna, IT.
MLA:
Qiu, Minhao, et al. "Improved sensor error definitions for reliability analysis of multi-sensor systems." Proceedings of the The 7th International Conference on System Reliability and Safety, Bologna 2023.
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