A robotic chauffeur should reason about spatial information with
a variety of scales, dimensions, and ontologies. Rich representations
of both the quantitative and qualitative characteristics of space not
only enable robust navigation behavior, but also permit natural
communication with a human passenger. We apply a hierarchical
framework of spatial knowledge inspired by human cognitive abilities,
the Hybrid Spatial Semantic Hierarchy, to common navigation tasks:
safe motion, localization, map-building, and route planning. We also
discuss the straightforward mapping between the variety of ways in
which people communicate with a chauffeur and the framework's
heterogeneous concepts of spatial knowledge. We present pilot
experiments with a virtual chauffeur.