Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics

TL;DR

This paper surveys off-board vehicle diagnostics, emphasizing vibroacoustic methods, and proposes automated model selection to enhance diagnostics, demonstrating classifiers for vehicle configuration identification from acoustic data.

Contribution

It introduces a framework for automated, context-specific model selection in vibroacoustic diagnostics and presents classifiers for vehicle configuration identification as a proof-of-concept.

Findings

Four classifiers effectively identify vehicle configurations from acoustic signatures.

Vibroacoustic monitoring shows promise for vehicle diagnostics and prognostics.

Automated model selection can improve diagnostic algorithm performance.

Abstract

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.