Crowdsourcing undersampled vehicular sensor data for pothole detection
The increased availability of embedded vehicle sensors allows for the detection of road features such as potholes. Despite being a promising approach, current vehicle embedded sensors operate at low frequencies and undersample sensor signals, thus degrading detection accuracy. One emerging solution is to crowdsource such undersampled sensor data from multiple vehicles to increase the detection accuracy. Aggregating sensor data from multiple vehicles, nonetheless, is a challenging task given the heterogeneity among vehicles, asynchronous sensor operation, GPS error, and sensor noise. Additionally, there may be bandwidth restrictions in vehicular networks which limit the amount of data available for aggregation. We investigate these issues by focusing on the problem of pothole detection. To quantify the detection accuracies and effects of real-world limitations, we design and evaluate three crowdsourcing pothole detection schemes involving vehicles and the Cloud. We also address the issue of lack of extensive model training data by demonstrating that a detection model applicable to real-world systems can be derived using simulated data. We validate our pothole detection methods using 38.1 km of real-world data collected from driving on roads in Warren, Michigan.