Algorithm evaluation for road anomaly detection and wear estimation on trucks using an accelerometer

Abstract: The condition of the roads is a factor that may not only affect the wear of a vehicle, car or truck, but as well may reduce fuel consumption, increase comfort, lower noise and maybe most importantly increase traffic safety. This gives a need of a system that can measure road quality and detect potholes, which could be of interest to haulers and to local road authorities that would get valuable information of road sections that are in need of maintenance. In this Master Thesis different algorithms were developed, and tested, that could automatically detect different kind of road anomalies using only an three-axis accelerometer mounted on the chassis of heavy duty trucks from Scania. Data collection was performed using two different trucks and the road anomalies were noted by the co-driver using the keyboard of a laptop. This Master Thesis also explored the correlation between the acceleration levels on the chassis and high elongation values on the front leaf spring. Using a developed evaluation framework, the anomaly detections from the different algorithms were compared to the test oracle to determine if the anomaly detection given by the algorithm was a true positive hit or a false positive. A great advantage of the developed evaluation framework is that additional algorithms could easily be added for evaluation. For the evaluation of the algorithms the statistical F-measure, which is the harmonic mean of the precision and sensitivity, was used for the test’s accuracy of the algorithms. The two algorithms that had the best performance results regarding detection of road anomalies were Algorithm – T and Algorithm – SDT. These two algorithms had a F-measure score of 65% and 64% respectively when the precision and sensitivity were equally weighted. For the correlation between acceleration levels and high elongation levels, Algorithm – SDT scored the highest F-measure value of 14%. This value is far from satisfying and a reason for the low value is that the algorithms were primarily developed for detection of road anomalies.