The Citizen Road Watcher – Identifying Roadway Surface Disruptions Based on Accelerometer Patterns

González-Gurrola, Luis Carlos; Martínez‐Reyes, Fernando; Carlos-Loya, Manuel Ricardo

doi:10.1007/978-3-319-03176-7_49

Cited by 7 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This involved road anomaly classification using the SVM after a pre-processing stage. The experimental results obtained showed that the method in [15] performed better than the method in [5].…”

Section: Related Workmentioning

confidence: 93%

“…Road anomalies were detected and classified into mild, severe, or span levels. Similarly, a Multilayer Perceptron (MLP) algorithm was proposed in [5] to analyze road anomalies. The experimental results obtained revealed high detection, accuracy and precision rates.…”

Section: Related Workmentioning

confidence: 99%

“…A popular sensor used in modern vehicular technologies to monitor road surface conditions and to detect road anomalies is the Accelerometer [3,[5][6][7][8][9][10]. An Accelerometer is a device that measures the acceleration of a body relative to g-force [11].…”

mentioning

confidence: 99%

See 2 more Smart Citations

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Bello-Salau¹,

Onumanyi²,

Sadiq³

et al. 2019

IJECE

View full text Add to dashboard Cite

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods.

show abstract

Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Bello-Salau¹,

Onumanyi²,

Sadiq³

et al. 2019

IJECE

View full text Add to dashboard Cite

show abstract

“…Specific statistics such as the absolute mean, standard deviation, variance and the energy of the signal were used to train the SVM to classify road anomalies into either severe, mild or span levels. Gonzalez et al [16], in a similar approach used the Multilayer Perceptron (MLP) algorithm to analyze road anomalies. They demonstrated a high level of precision and detection accuracy in their experiments.…”

Section: Related Workmentioning

confidence: 99%

“…The process included a pre-processing stage, classification using an SVM, and visualization of the output. Results indicated better statistical performance than the other approaches considered in [16]. In [18], a GPS, video module and acceleration signals were used by an onsite real time algorithm for pothole detection.…”

Section: Related Workmentioning

confidence: 99%

New road anomaly detection and characterization algorithm for autonomous vehicles

Bello-Salau

Aibinu

Onumanyi

et al. 2018

ACI

View full text Add to dashboard Cite

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.

show abstract

RoadMonitor: An Intelligent Road Surface Condition Monitoring System

Mohamed

Fouad

Elhariri

et al. 2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

The Citizen Road Watcher – Identifying Roadway Surface Disruptions Based on Accelerometer Patterns

Cited by 7 publications

References 4 publications

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

New road anomaly detection and characterization algorithm for autonomous vehicles

RoadMonitor: An Intelligent Road Surface Condition Monitoring System

Contact Info

Product

Resources

About