As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models excel at perception tasks, model outcomes are usually not dependable enough for safety-critical applications. In this work,we present a timeseries-aware uncertainty wrapper for dependable uncertainty estimates on timeseries data. The uncertainty wrapper is applied in combination with information fusion over successive model predictions in time. The application of the uncertainty wrapper is demonstrated with a traffic sign recognition use case. We show that it is possible to increase model accuracy through information fusion and additionally increase the quality of uncertainty estimates through timeseries-aware input quality features.

翻译：随着网络物理系统中人工智能组件的应用日益普及，对可靠系统架构的需求也随之涌现。尽管数据驱动模型在感知任务中表现出色，但其输出结果在安全关键型应用中通常不够可靠。本文提出一种面向时序数据的不确定性封装器，可为时序数据提供可靠的不确定性估计。该封装器结合时序上连续模型预测的信息融合技术进行应用。通过交通标志识别用例展示了该不确定性封装器的实际效果。研究证明，通过信息融合可提升模型精度，同时利用时序感知的输入质量特征可进一步提高不确定性估计的质量。