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

arXiv: https://arxiv.org/abs/2007.03759 · HTML: https://mlsanity.com/paper/2007.03759/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Cyberphysical Sequencing for Distributed Asset Management with Broad Traceability](https://mlsanity.com/paper/2112.02079/) (2021)
- [The AI Mechanic: Acoustic Vehicle Characterization Neural Networks](https://mlsanity.com/paper/2205.09667/) (2022)
- [Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks](https://mlsanity.com/paper/2208.05192/) (2022)
- [Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)](https://mlsanity.com/paper/2409.12112/) (2024)

## Abstract (arXiv)

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.