  Safety assurance is a central concern for the development and societal
acceptance of automated driving (AD) systems. Perception is a key aspect of AD
that relies heavily on Machine Learning (ML). Despite the known challenges with
the safety assurance of ML-based components, proposals have recently emerged
for unit-level safety cases addressing these components. Unfortunately, AD
safety cases express safety requirements at the system level and these efforts
are missing the critical linking argument needed to integrate safety
requirements at the system level with component performance requirements at the
unit level. In this paper, we propose the Integration Safety Case for
Perception (ISCaP), a generic template for such a linking safety argument
specifically tailored for perception components. The template takes a deductive
and formal approach to define strong traceability between levels. We
demonstrate the applicability of ISCaP with a detailed case study and discuss
its use as a tool to support incremental development of perception components.
