Abstract
Road anomaly detection has attracting increasing attention in recent years due to its significant role in the public transportation of modern cities. A few methods has been proposed to detect road anomaly with inertial sensors (e.g., accelerometer and gyroscope), which usually utilize classification techniques by extracting time and frequency domain features from inertial sensor data. However, existing methods are time consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the self-similarity of the data when vehicle passes over the road anomalies. In this paper, we propose QDetect, a road anomaly detection system with less data-dependency via querying and re-comparing. Specifically, QDetect consists of two phases: 1) Query filter. This phase is designed to roughly extract road anomaly segments by matching existing labelled anomalies; 2) Re-comparison on suspicious anomalies to identify their anomaly types. We have conducted comprehensive experiments on two real-world data sets, and the results show that our method outperforms some existing methods in both detection performance and running time. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.
Highlights
Road anomaly(such as pothole, metal bump or speed bump) is any permanent obstacle generated by the continuous use, weather conditions or traffic planning decisions in the road and can lead to serious traffic accidents
Between 2000 and 2011, there were 2 million traffic accidents in Canada, of which 33% were related to road conditions or bad weather
If one method performs well under small data set, the entire detection project will be much smooth, it needs a less data-dependent method to identify the anomalies effectively at the beginning of detection. Taking these issues into consideration, this paper focuses on: (1) building a more realistic and meaningful model where this system relies less on the size of data sets; (2) achieving better anomaly detection performance compared to some methods proposed in other papers; (3) enabling a less time-consumption road anomaly detection model no matter how large the size of data sets is
Summary
Road anomaly(such as pothole, metal bump or speed bump) is any permanent obstacle generated by the continuous use, weather conditions or traffic planning decisions in the road and can lead to serious traffic accidents. Between 2000 and 2011, there were 2 million traffic accidents in Canada, of which 33% were related to road conditions or bad weather. In 2015, about 50,000 British drivers were involved in traffic accidents caused by road anomalies, and road pits caused a car accident every 11 minutes. Governments spend huge amounts of manpower and resources on road maintenance. The British government announced that they spent $1.2 billion on road maintenance in 2017. In 2014, for the city of Toronto, Canada spent a total of $6 million on road repairs.
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