We survey the state of the art in off-board diagnostics for vehicles, their
occupants, and environments, with particular focus on vibroacoustic (VA)
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 VA 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.
