Abstract
In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance.
Highlights
During the operation of the road, the road surface will inevitably suffer from some defects or damage due to the crushing, impact, and weather changes of the passing vehicles
Harikrishnan [9] improved the Gaussian background model and proposed an abnormal road surface recognition method that can adapt to different vehicle speeds
Celaya [11] installed sensors at the front of the vehicle to obtain the vehicle vibration response when the vehicle passed the speed bump and used the multivariate genetic algorithm to detect the road surface anomaly. This method can realize the recognition of abnormal road surfaces with a low false alarm rate, but the calculation is complicated, and a large number of statistical features such as mean, variance, peak, and standard deviation are needed for machine learning
Summary
During the operation of the road, the road surface will inevitably suffer from some defects or damage due to the crushing, impact, and weather changes of the passing vehicles. Harikrishnan [9] improved the Gaussian background model and proposed an abnormal road surface recognition method that can adapt to different vehicle speeds. Celaya [11] installed sensors at the front of the vehicle to obtain the vehicle vibration response when the vehicle passed the speed bump and used the multivariate genetic algorithm to detect the road surface anomaly This method can realize the recognition of abnormal road surfaces with a low false alarm rate, but the calculation is complicated, and a large number of statistical features such as mean, variance, peak, and standard deviation are needed for machine learning. A Gaussian background model is used to identify abnormal roads, and an adaptive adjustment mechanism based on vehicle speed is proposed to improve the recognition accuracy. The parameters of the Gaussian background model are optimized by using fuzzy logic inference machines, making the method suitable for different types of vehicles, and using the kNN algorithm to classify abnormal roads
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