Poster Abstract: Road Quality Classification for Road Repair Authorities and Regular Drivers, Using an On-Board Data Logger

This paper attempts to improve road condition monitoring by providing a framework for real-time detection of road anomalies for city municipalities and regular drivers. We have developed and publicly shared a vast quantity of heterogeneous data set collected over a continuous drive for four different kinds of road anomalies - Cat eyes, Potholes, Manholes and Speed Bumps, using a custom-made, dedicated device for data logging, recording vehicle's acceleration and GPS location. Sequential forward selection was performed on three groups of features i.e. FFT, DWT and peak accelerations for feature extraction and Classification and Regression Tree (CART) was used as the classifier. Performance metrics including FPR, FNR and TPR were calculated and compared and the best results have been reported. In addition to this, we also present annotated maps for repair authorities and regular drivers for visual inspection and intelligent navigation.