The Carbin App: Assessing Road Quality Using Crowdsourced Smartphone Measurements
Thursday, July 30, 2020 at 11:00am to 12:00pm
We propose, calibrate, and validate a crowdsourced approach for estimating road roughness power spectral density (PSD) based on an inverse analysis of measured vertical acceleration by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement-vehicle interactions, the inverse analysis employs an L2 norm regularization to estimate, from the acceleration PSD, ride quality metrics, such as the widely-used international roughness index (IRI); as well as the half-car dynamic vehicle properties and related excess fuel consumption of the vehicle evoking the fluctuation-dissipation theorem of statistical physics. The method is validated against (1) laser-measured road roughness data for both inner city and highway road conditions, and (2) road roughness data for the state of California. We also show that road roughness predictions are only marginally affected by the phone position in the vehicle; an important condition for crowdsourced capabilities of the proposed approach.
This webinar will be presented by CSHub research assistant Meshkat Botshekan.