Automated Sensing System for Monitoring of Road Surface Quality by Mobile Devices

Astarita, Vittorio; Vaiana, Rosolino; Iuele, Teresa; Caruso, Maria Vittoria; Vincenzo, P. Giofrè; Francesco, De

doi:10.1016/j.sbspro.2014.01.057

Cited by 106 publications

(42 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Classification based on rough mereology does not provide promising results as the work of [8], but on the other hand, it still produces better results than the one obtained in [7] V. CONCLUSION AND FUTURE DIRECTIONS Usually sensory-based smartphones offer opportunities to build useful applications. For maximizing the benefits of these opportunities, this paper represents a framework of an application for monitoring road surface conditions in terms of speed bumps.…”

Section: Resultsmentioning

confidence: 96%

See 1 more Smart Citation

Intelligent road surface quality evaluation using rough mereology

Fouad

Mahmood²,

Mahmoud

et al. 2014

2014 14th International Conference on Hybrid Intelligent Systems

View full text Add to dashboard Cite

The road surface condition information is very useful for the safety of road users and to inform road administrators for conducting appropriate maintenance. Roughness features of road surface; such as speed bumps and potholes, have bad effects on road users and their vehicles. Usually speed bumps are used to slow motor-vehicle traffic in specific areas in order to increase safety conditions. On the other hand driving over speed bumps at high speeds could cause accidents or be the reason for spinal injury. Therefore informing road users of the position of speed bumps through their journey on the road especially at night or when lighting is poor would be a valuable feature. This paper exploits a mobile sensor computing framework to monitor and assess road surface conditions. The framework measures the changes in the gravity orientation through a gyroscope and the shifts in the accelerometer's indications, both as an assessment for the existence of speed bumps. The proposed classification approach used the theory of rough mereology to rank the modified data in order to make a useful recommendation to road users.

show abstract

Section: Resultsmentioning

confidence: 96%

“…Vittorio et al [8] used a simple application for smartphones that employs a GPS receiver for vehicle's localization and a three axis accelerometer to collect acceleration data due to vehicles motion on road anomalies. The proposed sensing system is automated to monitor road surface quality.…”

Section: Introductionmentioning

confidence: 99%

Intelligent road surface quality evaluation using rough mereology

Fouad

Mahmood²,

Mahmoud

et al. 2014

2014 14th International Conference on Hybrid Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In [77], a greedy heuristic approach for an optimal mobile sensor placement maximises the total length of the road inspected by sensors. Frequency filter, speed filter and small peaks filter were used to develop the vertical acceleration impulse that corresponds to a "high-energy event" on the road surface in UNIquALroad [78,79]. Dynamic Time Warping -DTW detects pothole by using the pattern-matching technique independent of time and speed [80].…”

Section: Signal Processingmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

show abstract

“…Moreover, studies which have aimed to detect potholes have proposed to threshold the speed to reject apparent anomalies caused by curb ramps (typically, curb ramps are approached at low speeds) [69]. In [293], it was hinted that it is possible to estimate both the depth and length of potholes from accelerometer measurements. This information could then be used to evaluate the priority of specific repairs.…”

Section: Road Condition Monitoringmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

View full text Add to dashboard Cite

Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

show abstract

Automated Sensing System for Monitoring of Road Surface Quality by Mobile Devices

Cited by 106 publications

References 6 publications

Intelligent road surface quality evaluation using rough mereology

Intelligent road surface quality evaluation using rough mereology

Response-based methods to measure road surface irregularity: a state-of-the-art review

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Contact Info

Product

Resources

About