  Fully automated vehicles will require new functionalities for perception,
navigation and decision making -- an Autonomous Driving Intelligence (ADI). We
consider architectural cases for such functionalities and investigate how they
integrate with legacy platforms. The cases range from a robot replacing the
driver -- with entire reuse of existing vehicle platforms, to a clean-slate
design. Focusing on Heavy Commercial Vehicles (HCVs), we assess these cases
from the perspectives of business, safety, dependability, verification, and
realization. The original contributions of this paper are the classification of
the architectural cases themselves and the analysis that follows. The analysis
reveals that although full reuse of vehicle platforms is appealing, it will
require explicitly dealing with the accidental complexity of the legacy
platforms, including adding corresponding diagnostics and error handling to the
ADI. The current fail-safe design of the platform will also tend to limit
availability. Allowing changes to the platforms, will enable more optimized
designs and fault-operational behaviour, but will require initial higher
development cost and specific emphasis on partitioning and control to limit the
influences of safety requirements. For all cases, the design and verification
of the ADI will pose a grand challenge and relate to the evolution of the
regulatory framework including safety standards.
