  Autonomous Systems (AS) are increasingly proposed, or used, in Safety
Critical (SC) applications. Many such systems make use of sophisticated sensor
suites and processing to provide scene understanding which informs the AS'
decision-making. The sensor processing typically makes use of Machine Learning
(ML) and has to work in challenging environments, further the ML-algorithms
have known limitations,e.g., the possibility of false-negatives or
false-positives in object classification. The well-established safety-analysis
methods developed for conventional SC systems are not well-matched to AS, ML,
or the sensing systems used by AS. This paper proposes an adaptation of
well-established safety-analysis methods to address the specifics of
perception-systems for AS, including addressing environmental effects and the
potential failure-modes of ML, and provides a rationale for choosing particular
sets of guidewords, or prompts, for safety-analysis. It goes on to show how the
results of the analysis can be used to inform the design and verification of
the AS and illustrates the new method by presenting a partial analysis of a
road vehicle. Illustrations in the paper are primarily based on optical
sensing, however the paper discusses the applicability of the method to other
sensing modalities and its role in a wider safety process addressing the
overall capabilities of AS.
