Automatic Road Anomaly Detection Using Smart Mobile Device
Date Issued
2009
Date
2009
Author(s)
Tai, Yu-Chin
Abstract
Maintaining the quality of roadways is a major challenge for governments around the world. Poor road surfaces pose significant safety threats to drivers and motorists. According to the statistics of the Ministry of Justice in Taiwan, there are 220 claims for state compensation caused by road quality problems from 2005 to 2007, and the government paid a total of 113 million NTD in compensation.his research explores utilizing a mobile phone with tri-axial accelerometer to collect acceleration data while riding in the motorcycle. The data is analyzed to detect road anomaly and to evaluate the quality of the road segments. Acceleration data on motorcycles are collected on twelve road segments, three hours long, with a total length of about 60 kilometers in our experiments. Both supervised and unsupervised machine learning methods are used to recognize the road condition. SVM learning is used to detect road anomaly and to identify its corresponding position from labeled acceleration data. This method achieves a precision of 78.5% in road anomaly detection. To construct a model of smooth roads, unsupervised learning is used to learn the thresholds by clustering data collected from the accelerometer. The results are used to rank the quality of multiple road segments. We compare the rank list from the evaluator with the rank list from human testers who rode on the roads segments. The experiment showed that the automatic rank result is good based on the Kendall tau rank correlation coefficient.
Subjects
accelerometer
mobile device
mobile phone
mobile sensing
road surface anomaly
pothole
Type
thesis
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