  In learning-enabled autonomous systems, safety monitoring of learned
components is crucial to ensure their outputs do not lead to system safety
violations, given the operational context of the system. However, developing a
safety monitor for practical deployment in real-world applications is
challenging. This is due to limited access to internal workings and training
data of the learned component. Furthermore, safety monitors should predict
safety violations with low latency, while consuming a reasonable amount of
computation.
  To address the challenges, we propose a safety monitoring method based on
probabilistic time series forecasting. Given the learned component outputs and
an operational context, we empirically investigate different Deep Learning
(DL)-based probabilistic forecasting to predict the objective measure capturing
the satisfaction or violation of a safety requirement (safety metric). We
empirically evaluate safety metric and violation prediction accuracy, and
inference latency and resource usage of four state-of-the-art models, with
varying horizons, using an autonomous aviation case study. Our results suggest
that probabilistic forecasting of safety metrics, given learned component
outputs and scenarios, is effective for safety monitoring. Furthermore, for the
autonomous aviation case study, Temporal Fusion Transformer (TFT) was the most
accurate model for predicting imminent safety violations, with acceptable
latency and resource consumption.
