Abstract
Roadway surface disruptions present multiple challenges for both individuals and governmental agencies; knowing the exact number and location of road imperfections in a specific area could help save lives, freight and money to citizens, companies and authorities. This work presents earlier results of a tool being proposed to report the existence of road’s disruptions by enabling citizens’ cars as road watchers. Using Android-based devices situated on the copilot floor side of a car 5 Mbytes of road information has been collected. We run a series of experiments aiming to observe changes in the acceleration patterns when vehicles pass over potholes, speed bumps and metal humps. Currently, the classification of disruptions is being experimented with techniques from the field of Machine Learning (ML). Our vision for such a tool is to offer an accurate and automated system that can report the presence of road imperfections to a web-based information system.
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González-Gurrola, L.C., Martínez-Reyes, F., Carlos-Loya, M.R. (2013). The Citizen Road Watcher – Identifying Roadway Surface Disruptions Based on Accelerometer Patterns. In: Urzaiz, G., Ochoa, S.F., Bravo, J., Chen, L.L., Oliveira, J. (eds) Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction. Lecture Notes in Computer Science, vol 8276. Springer, Cham. https://doi.org/10.1007/978-3-319-03176-7_49
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DOI: https://doi.org/10.1007/978-3-319-03176-7_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03175-0
Online ISBN: 978-3-319-03176-7
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