In this work, we present a preliminary approach to distinguish potholes from speed bumps by analyzing the acceleration values sensed by a mobile device. The information of the accelerometers is gathered by an experimental mobile application developed to automatically detect potholes. A driver, who has previously installed the application in her smartphone, places the device in a fixed position inside the vehicle. Thus, this application records the accelerometer oscillations and the place where the vehicle transits. Then, if the road is damaged, the vibrations produced in the vehicle can be captured by the accelerometers indicating the pothole. However, in a road there are other structures that can produce similar effects: speed bumps. In both potholes and street bumps, the accelerometers of the mobile device produce a sequence of oscillations in the three axis (X, Y and Z). We model these sequence as multivariate time series and then we classify these by using a temporal classification approach. The preliminary results were carried out with real-world data and showed a promising accuracy. Pothole detection, multivariate time series classification.
When a vehicle transits in a road, potholes and others structures affect its stability. These variations in the vehicle stability can be detected by the accelerometers of a mobile device (i.e. an smartphone). An accelerometer provides data on the acceleration of the three coordinate axes with (almost) continuous updates. This allows us to detect the slightest movements. Thus, placing the device in a fixed position inside the vehicle, the mobile movements can reflect the movements of the vehicle. Then, we can detect the presence of potholes or street bumps in the road, and its severity in terms of destabilization of the vehicle, by analyzing the accelerometers information. Moreover, if we combine this information with geolocation information, we can determine where these events occur. We name these events as stability events. Particularly, we consider two types of speed bumps: speed humps and street gutter (a depression running parallel to a street designed to collect rainwater, but that usually crosses perpendicular streets). Figures 1 and 2 show two examples of speed humps and street gutter, respectively.
The accelerometer information gathered by the application are tuples acc={rawX, diffX, rawY, diffY, rawZ, diffZ}. The variables rawX, rawY and rawZ correspond to the acceleration values sensed by the sensors in the axis X, Y and Z, respectively. Moreover, the variables dif f X, dif f Y and dif f Z represent the differences between the actual raw values and the previous one. After losing stability, the vehicle takes several seconds to stabilize again. For this reason, each stability event is composed of several tuples acc. Then, we define a stability event as a sequence se = f(t1; acc1); (t2; acc2); :::(tn; accn)g, where acci is the accelerometer information in time i within the stability event se.
In this context, a sequence se represents a multivariate time series. A multivariate time series is a
sequence of numerical vectors [
During the experimental results, we use the Kadous and Sammut approach. However, it is worth noticing that other multivariate time series approaches can be applied. 3
The experiments were carried out with real-world information extracted from Tandil, Buenos Aires, Argentina. In total, 48 journeys were processed. Moreover, we manually identified 24 potholes, 54 speed Page 38 of 40 humps and 33 street gutters in the streets through which the vehicle transited. Then, taking into account the potholes and the speed bumps identified, we extracted 371 stability events, particularly: 184 events associated to potholes, 128 events associated to speed humps, and 59 events associated to street gutters. In average, each event was compound of 19.04 tuples acc.
To run the experiment we used TClass1. TClass is the implementation of the approach to classify
multivariate time series proposed by Kadous and Sammut in [
The best results were obtained by using the global extractors duration, mean, min and max over the 6 attribute of the sequences: rawX, diffX, rawY, diffY, rawZ and diffZ by using a J48 classification tree. The accuracy of the approach was 63.64%. Table 1 shows the confusion matrix and precision and recall metrics for each class. The best precision and recall were obtained predicting speed humps and potholes, respectively. In contrast, the worst individual metrics were obtained by predicting street gutter. We think that this is because of the low number of stability events produced by the street gutter in the dataset.
Moreover, we grouped the speed hump and street gutter in a common class, in order to differentiate potholes from speed bumps. Considering this grouping, the accuracy increase to 70%. 4
In this work, we propose a preliminary approach that allows us to distinguish between potholes and speed bumps. This approach allows us to reduce the number of false positives produced during the pothole detection process. Moreover, this approach is key if we want to make available the application in multiple cities with the least effort. The preliminary results obtained from real-world data were promising.
Future work will focus on a more extensive experimentation. Moreover, we will analyze the use of other multivariate time series classification approaches. 1 https://sites.google.com/site/waleedkadous/software/tclass Page 39 of 40