Abstract
Abstract We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured 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 interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.
Highlights
Impact Statement One of the key drivers of safety, ride comfort, and environmental footprint of our road network is road roughness
The premise of the proposed approach is that an acceleration-based inverse analysis is able to accurately provide estimates of road roughness metrics commensurable with classical means such as laser-based measurements of longitudinal road profiles
A second goal of the approach is the crowdsourced assessment of road roughness metrics
Summary
Consider measurement of acceleration by smartphone attached somewhere to the vehicle body (sprung mass). Assuming in a first approach the sprung mass as rigid, the vertical displacement of a point ðx,yÞ is obtained by linear interpolation of the suspension displacements: XK zSðx, yÞ 1⁄4 Nkðx, yÞ zS,k,. We note that the Fourier transform of the sprung mass displacement of each suspension system, zcS,k, relates to the Fourier transform of the road roughness, ξbk, by the frequency response function (FRF), HzS,k : zcS,k 1⁄4 HzS,k ðωÞ ξbk:. The unknowns of the inverse problem are the characteristics of road roughness PSD, SξðωÞ, the vehicle properties of the front- and back-suspension systems, tires (parametrizing the FRFs, HzS, and HzS,2), and the dimensionless phone position, r.
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