Abstract
The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data. This approach is evaluated on an experimental dataset collected using 12 different vehicles driving for more than 40 km of road. The results show that the proposed approach outperforms significantly and across different sampling rates both the application of CNN to the original time domain representation and the application of shallow machine learning algorithms. The approach achieves an identification accuracy of 97.2%. The results presented in this paper are based on an extensive optimization both of the CNN algorithm and the spectrogram implementation in terms of window size, type of window, and overlapping ratio. The accurate detection of road anomalies/obstacles could be useful to road infrastructure managers to monitor the quality of the road surface and to improve the accurate positioning of autonomous vehicles because road anomalies/obstacles could be used as landmarks.
Highlights
Detection of road anomalies like potholes, road cracks, and road safety features/obstacles like speed bumps has been investigated by the research community in recent years using a variety of sensors including cameras, Light Detection And Ranging (LiDAR)s, and InertialMeasurement Units (IMU)s
The approach is based on the transformation of the collected data into the spectral domain, which is given as an input to Convolutional Neural Network (CNN)
On the samples collected in the time domain and on the application of shallow machine learning algorithms like Support Vector Machine (SVM) and K Nearest Neighbor (KNN), as is usually proposed in the existing research literature
Summary
Detection of road anomalies like potholes, road cracks, and road safety features/obstacles like speed bumps ( called speed humps) has been investigated by the research community in recent years using a variety of sensors including cameras, Light Detection And Ranging (LiDAR)s, and InertialMeasurement Units (IMU)s. Detection of road anomalies like potholes, road cracks, and road safety features/obstacles like speed bumps ( called speed humps) has been investigated by the research community in recent years using a variety of sensors including cameras, Light Detection And Ranging (LiDAR)s, and Inertial. One of the main applications for the detection of road anomalies is the monitoring of the road conditions, which can be used to repair the road surface once a road anomaly (e.g., pothole) is detected or to improve the comfort and safety of the vehicle in Advanced Driver. The upcoming evolution of modern vehicles to autonomous vehicles could benefit from this information and use it for a variety of purposes. It could be used to improve the position of the autonomous vehicle.
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