Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings—Addressing Who’s Who

A wide range of new possibilities in the area of intelligent transportation systems (ITS) emerged when sensors, such as accelerometers, were introduced in practically every smartphone. A clear example is using a driver’s smartphone to detect the vertical movement experienced by the vehicle when passing over a pothole or bump; in other words, sensing the quality of the road. To this end, several approaches have been proposed in the literature, most of them based on thresholds applied to accelerometer readings. Nonetheless, no fair comparison of these approaches had been done until now, mainly because of the lack of public datasets. In this paper, we propose a platform to create road data sets that could be used by the community to create their own roads with their own requirements. Using this platform, we assembled a data set of 30 roads plagued with potholes and bumps, which we used to evaluate the most popular heuristics previously reported. From our study, a heuristic, called STDEV(Z), based on standard deviation analysis proposed by Mednis et al. obtained the best results among the considered reference methodologies. This finding suggests that measures of dispersion, specifically standard deviation, are among the best indicators to identify disruptions on accelerometer readings. From this point, we fused features used by all these heuristics within our own feature vector, which we used with a support vector machine. We show that the proposed methodology clearly outperforms all other evaluated methods. To support these conclusions, results were statistically validated. We expect to lay the first steps to homogenize future comparisons as well as to provide stronger baselines to be considered in subsequent works.