  RGB cameras are one of the most relevant sensors for autonomous driving
applications. It is undeniable that failures of vehicle cameras may compromise
the autonomous driving task, possibly leading to unsafe behaviors when images
that are subsequently processed by the driving system are altered. To support
the definition of safe and robust vehicle architectures and intelligent
systems, in this paper we define the failure modes of a vehicle camera,
together with an analysis of effects and known mitigations. Further, we build a
software library for the generation of the corresponding failed images and we
feed them to six object detectors for mono and stereo cameras and to the
self-driving agent of an autonomous driving simulator. The resulting
misbehaviors with respect to operating with clean images allow a better
understanding of failures effects and the related safety risks in image-based
applications.
