Abstract
Lack of comprehensive and up-to-date information on degrading road conditions is only exacerbating the already inefficient, expensive and labor-expensive methods associated with road maintenance and repair. Among many sensing modalities that are currently being explored, MEMS accelerometers are increasingly becoming popular in the task of road quality assessment, especially in large-scale crowdsourced data gathering efforts. In this paper, recent results on the use of accelerometers for detection of road abnormalities is presented. In particular, with an emphasis on developing a low-cost yet robust road abnormality detection system, predictive effectiveness of features generated from acceleration sensors is explored. By utilizing a signal transmission model that captures the system dynamics and low-pass filtering effects associated with vehicle suspension system, a simplified approach towards the `reconstruction' of road surface conditions from vertical acceleration measurements is presented. Furthermore, with this particular signal transmission model in place, predictive effectiveness of features derived from acceleration sensors is explored via the use of a statistical approach referred to as relief algorithm. The signal model and feature analysis approach is demonstrated via a real-life dataset.
Published Version
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