Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory

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.

Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach

“…With the rapid development of smartphones in recent years, the technology of road detection using the built-in sensors of smartphones has become an important supplement to road pothole detection. Many studies have demonstrated that the built-in accelerometers of mobile phones can be used to detect road anomalies [7,13,15]. Vaiana et al, investigated young drivers' driving behavior on horizontal curves, by analyzing speed and acceleration data collected via smartphones.…”

“…Gillespie et al [29] were the first to develop methods for the indirect detection of road anomalies. Botshekan et al [13] estimated road roughness from the power spectral density (PSD) of acceleration, based on stochastic vibration analysis of a semi-vehicle mechanical model of roughness-induced road-vehicle interaction, and demonstrated that the location of the mobile phone in the vehicle only slightly affects the detection of road roughness. On the basis of using the PSD of acceleration to reflect the road profiles, Daraghmi et al [7] used blind source separation technology to separate the vehicle model from the measurement of acceleration, so as to reduce the influence of the vehicle model on the detection results.…”

“…To meet current pavement maintenance needs, detecting road anomalies with high spatial and temporal coverage has been widely researched. The vibration of the vehicle is the most obvious response to the potholes on the road, and it has been demonstrated that road anomalies can be detected by establishing a relationship between some vibration signatures and road surface characteristics [7,[13][14][15]. For example, Andren [15] proposed that a road roughness spectrum could be derived from the vehicle vibration spectral density.…”