Road Anomalies Detection System Evaluation

Silva, Nuno; Shah, Vaibhav; Soares, João; Rodrigues, Helena

doi:10.3390/s18071984

Cited by 39 publications

(19 citation statements)

References 31 publications

(45 reference statements)

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“…Some research works stablish correlations between the vehicle accelerations and the pavement roughness [30][31][32][33] or obtain more detailed information about the roughness such as the locations of pavement distresses [34]. Recent studies also detect road anomalies (pavement evaluation) by means of the accelerometer sensors of smartphones [35][36][37]. In all these works the acceleration signal proved to be a valuable tool to describe the functional performance of pavements.…”

Section: Introductionmentioning

confidence: 99%

Tire/road noise, texture, and vertical accelerations: Surface assessment of an urban road

Vázquez

Hidalgo²,

García-Hoz

et al. 2020

Applied Acoustics

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Pavements are made up of several layers with different mechanical and functional characteristics. The correct design of the surface layer of a road may lead to pavements with better characteristics regarding ride quality and safety, but also pavements that may be used as a measure against noise. The use of low-noise pavements may be an effective measure to reduce the generation of acoustic pollution by road traffic. This work aims to assess some functional characteristics of a rehabilitated urban street, after two months in service conditions. The pavement was fabricated with a gap-graded bituminous mixture type Stone Mastic Asphalt (SMA) with crumb rubber (CR) from end-of-life (EOL) tires. This work studies the acoustic performance of the pavement, as well as other surface characteristics such as the macrotexture depth (MPD) and the unevenness (IRI), establishing the relationship between them and the tire/road noise at different frequencies. Finally, the main vertical acceleration frequencies of the pavement/vehicle system at 50 km/h were also assessed and related to the pavement unevenness and conservation. According to the results, this mixture might be used as a noise mitigation measure within the Action Plans of some urban areas with problems related to noise. The macrotexture depth of the mixture contributes to its acoustic performance at low frequencies; however, its acoustic performance cannot be totally explained from a macrotexture point of view.

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Section: Introductionmentioning

confidence: 99%

Tire/road noise, texture, and vertical accelerations: Surface assessment of an urban road

Vázquez

Hidalgo²,

García-Hoz

et al. 2020

Applied Acoustics

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“…This study achieved a detection accuracy of 97.14%. A similar study was conducted by Silva et al [22]. The authors used random forest classifier to detect road anomalies from mobile sensed data.…”

Section: Related Studiesmentioning

confidence: 94%

Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection

Huo

Goldberg

et al. 2019

IJGI

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Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.

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“…• RoDS-ACGAN: the method [35] used one novel method to process road anomaly data based on ACGAN and build a novel system to collect data and give results.…”

Section: ) Comparative Methodsmentioning

confidence: 99%

QDetect: Time Series Querying Based Road Anomaly Detection

et al. 2020

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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. INDEX TERMS Time series query, road anomaly detection, acceleration data, top-k.

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Road Anomalies Detection System Evaluation

Cited by 39 publications

References 31 publications

Tire/road noise, texture, and vertical accelerations: Surface assessment of an urban road

Tire/road noise, texture, and vertical accelerations: Surface assessment of an urban road

Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection

QDetect: Time Series Querying Based Road Anomaly Detection

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