  Perception, localization, planning, and control, high-level functions often
organized in a so-called pipeline, are amongst the core building blocks of
modern autonomous (ground, air, and underwater) vehicle architectures. These
functions are increasingly being implemented using learning-enabled components
(LECs), i.e., (software) components leveraging knowledge acquisition and
learning processes such as deep learning. Providing quantified component-level
assurance as part of a wider (dynamic) assurance case can be useful in
supporting both pre-operational approval of LECs (e.g., by regulators), and
runtime hazard mitigation, e.g., using assurance-based failover configurations.
This paper develops a notion of assurance for LECs based on i) identifying the
relevant dependability attributes, and ii) quantifying those attributes and the
associated uncertainty, using probabilistic techniques. We give a practical
grounding for our work using an example from the aviation domain: an autonomous
taxiing capability for an unmanned aircraft system (UAS), focusing on the
application of LECs as sensors in the perception function. We identify the
applicable quantitative measures of assurance, and characterize the associated
uncertainty using a non-parametric Bayesian approach, namely Gaussian process
regression. We additionally discuss the relevance and contribution of LEC
assurance to system-level assurance, the generalizability of our approach, and
the associated challenges.
