Wavelength sensitivity of roughness measurements using connected vehicles

Bridgelall, Raj; Rahman, Tahmidur; Tolliver, Denver; Daleiden, Jerome

doi:10.1080/10298436.2017.1316645

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…This can be done with the help of the future development of sensor technology. Lastly, the intensive on-going research on RIF and TWIT [95][96][97][98][99][100][101] as the alternatives for IRI in connected vehicle environment will be promising for large-scale implementation.…”

Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

“…Regarding new roughness index, a speed-independent road impact factor -RIF (individual vehicle) and its corresponding time-wavelength-intensity-transform -TWIT (vehicle groups) for connected vehicles were established using advanced signal processing in [94]. Further studies were conducted intensively to investigate and validate the RIF regarding sampling rate selection [95], localisation [96,97], RIF-IRI proportionality [98], deterioration forecasts in consideration of suspension parameter variances [99], stop-andgo conditions [100], and wavelength sensitivity [101].…”

Section: Signal Processingmentioning

confidence: 99%

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

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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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.

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Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

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.

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“…A third issue is that traversing the same anomaly at different speeds will produce different signal intensities. For example, the mechanical filtering action of a suspension system can reduce the roughness intensity produced from traversing a narrow bump at a high speed but increase that intensity at a lower speed (Bridgelall, Rahman, et al 2019). Similarly, the roughness intensity from traversing a wide bump increases with speed.…”

Section: Low-cost Sensor Challengesmentioning

confidence: 99%

Enhancement of signals from connected vehicles to detect roadway and railway anomalies

Bridgelall

Chia

Bhardwaj

et al. 2019

Meas. Sci. Technol.

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Frequent network-wide monitoring of the condition of roadways and railways prevent fatalities, injuries, and financial losses. Even so, agencies cannot afford to inspect vast transportation networks using present methods. Therefore, the idea of using low-cost sensors aboard connected vehicles became appealing. However, low-cost sensors introduce new challenges to improve poor signal quality which causes detection errors. Common approaches apply computationally complex filters to individual signal streams, which limits further improvements. This paper presents a method that combines signals from each traversal in a manner that leads to ever-increasing signal quality. The proposed method addresses the challenges of poor accuracy and precision of position estimates from global positioning system (GPS) receivers, and errors from the non-uniform sampling of low-cost accelerometers. The result is improved signal quality from a 20% improvement in signal alignment over GPS and a 90-fold enhancement in distance precision.

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“…First, some researchers have focused on improving the IRI to better represent the actual pavement roughness by modifying the methods to calculate the IRI, investigating the relationship between IRI and other indicators of pavement, and predicting the IRI for evaluating the quality of road surfaces. 23–25 Second, the quantitative relationship between vehicle response and the IRI has been investigated extensively via theoretical derivation or statistical regression. 26 Third, considering the human–vehicle–road–environment system, researchers have recently begun to examine the effect of the IRI on drivers’ physiological state and fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Crash recognition algorithm of automatic crash notification system with adaptive discrimination threshold

Lü

Liu

Shu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Efficiency and notification accuracy are two critical criteria for evaluating the performance of automatic crash notification (ACN) systems. The discrimination threshold (DT) is used to assess whether a collision accident occurs. Typically, the DT value is determined based on the maximal acceleration peak from multiple road tests. Because an overlarge DT value is unnecessary in most driving scenes and simultaneously adversely affects notification accuracy, a crash recognition (CR) algorithm with adaptive DT is proposed. First, a road–vehicle simulation model is constructed using the CarSim software. Next, the vehicle acceleration data at different driving speeds are obtained based on this road–vehicle model. Subsequently, a correlation model comprising a discrimination threshold value, an international roughness index, and the vehicle speed is developed. Finally, a CR algorithm is designed, in which discrimination threshold values that match the road roughness and vehicle speed are specified. Road and collision tests show that the proposed algorithm can identify collisions and calculate the speed change value more accurately compared with the conventional CR algorithm which has a fixed discrimination threshold.

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Wavelength sensitivity of roughness measurements using connected vehicles

Cited by 11 publications

References 13 publications

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

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

Enhancement of signals from connected vehicles to detect roadway and railway anomalies

Crash recognition algorithm of automatic crash notification system with adaptive discrimination threshold

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