Abstract
This paper presents a new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer. A wavelet transformation based filter was used to decompose the signals into multiple scales. These coefficients were correlated across adjacent scales and filtered using a spatial filter. Road anomalies were then detected based on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Our analyses show that the proposed algorithm detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates.
Highlights
Autonomous vehicles are vehicles capable of sensing their environment and navigating without human input under different terrains, over asphalt roads
In the tabular presentation of our results, the false alarm rate, accuracy, and the precision rate are reported for the anomaly detection capability of the algorithm, while the characterization results are reported in terms of the identification of either bumps or potholes
The performance of the developed Scale Space Filtering (SSF), Road Anomaly Detection Algorithm (RADA) and Road Anomaly Characterization Algorithm (RACA) were tested on new datasets which were not used during the training process
Summary
Department of Telecommunication Engineering, Federal University of Technology, Minna, Nigeria Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria Department of Transport Management, Federal University of Technology, Minna, Nigeria, and Department of Electrical/Electronics Engineering, Federal University of Technology, Minna, Nigeria
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