  We survey the state-of-the-art in offboard diagnostics for vehicles, their
occupants, and environments, with particular focus on vibroacoustic approaches.
We identify promising application areas including data-driven management for
shared mobility and automated fleets, usage-based insurance, and vehicle,
occupant, and environmental state and condition monitoring. We close by
exploring the particular application of vibroacoustic monitoring to vehicle
diagnostics and prognostics and propose the introduction of automated vehicle-
and context-specific model selection as a means of improving algorithm
performance, e.g. to enable smartphone-resident diagnostics. Towards this
vision, four strong-performing, interdependent classifiers are presented as a
proof-of-concept for identifying vehicle configuration from acoustic
signatures. The described approach may serve as the first step in developing
"universal diagnostics," with applicability extending beyond the automotive
domain.
