  The zeitgeist of the digital era has been dominated by an expanding
integration of Artificial Intelligence~(AI) in a plethora of applications
across various domains. With this expansion, however, questions of the safety
and reliability of these methods come have become more relevant than ever.
Consequently, a run-time ML model safety system has been developed to ensure
the model's operation within the intended context, especially in applications
whose environments are greatly variable such as Autonomous Vehicles~(AVs).
SafeML is a model-agnostic approach for performing such monitoring, using
distance measures based on statistical testing of the training and operational
datasets; comparing them to a predetermined threshold, returning a binary value
whether the model should be trusted in the context of the observed data or be
deemed unreliable. Although a systematic framework exists for this approach,
its performance is hindered by: (1) a dependency on a number of design
parameters that directly affect the selection of a safety threshold and
therefore likely affect its robustness, (2) an inherent assumption of certain
distributions for the training and operational sets, as well as (3) a high
computational complexity for relatively large sets. This work addresses these
limitations by changing the binary decision to a continuous metric.
Furthermore, all data distribution assumptions are made obsolete by
implementing non-parametric approaches, and the computational speed increased
by introducing a new distance measure based on the Empirical Characteristics
Functions~(ECF).
