Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics
This addresses the problem of pervasive vehicle diagnostics for applications like shared mobility and insurance, but is incremental as it builds on existing survey work with a new proof-of-concept.
This paper surveys off-board vehicle diagnostics using vibroacoustic approaches and proposes automated model selection to improve algorithm performance, demonstrating a proof-of-concept with four classifiers that achieve strong performance in identifying vehicle configurations from acoustic signatures.
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.