Influence of Vehicle Make on Accuracy of Real-time Road Anomaly Identification
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As road infrastructure in the United States is aging, road anomalies such as cracks, potholes, and other abnormalities are becoming much more prevalent. Currently there is no real-time understanding of the conditions of roads, thus we developed a machine-learning algorithm developed and trained to identify road conditions in real time based on data collected by smartphones. Since there are a multitude of different vehicles on the roads and locations where phones can be placed in the vehicle, creating a classification algorithm that can work regardless of the vehicle type and phone placement is incredibly important. Doing a comparative study on the different vibrations received at different locations in different vehicles will provide a baseline for future development of a universal algorithm that uses crowd sourced data from cell phones to allow for real-time awareness of changing road conditions. This in turn provides a way to identify and fix dangerous road anomalies quickly.
Saulnier, Michael Taylor (2018). Influence of Vehicle Make on Accuracy of Real-time Road Anomaly Identification. Undergraduate Research Scholars Program. Available electronically from