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：随着人工智能(AI)组件在网络物理系统中的应用日益普及，对可靠系统架构的需求也随之产生。尽管数据驱动模型在感知任务中表现优异，但其输出结果在安全关键性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，用于提供可靠的不确定性估计。该不确定性封装器与信息融合技术结合使用，对连续时间内的模型预测结果进行融合处理。我们通过交通标志识别案例展示了该不确定性封装器的具体应用。研究表明，信息融合技术能够提升模型准确度，同时基于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。