Road condition monitoring using on-board Three-axis Accelerometer and GPS Sensor

Chen, Kongyang; Lu, Mingming; Fan, Xiaopeng; Wei, Mingming; Wu, Jinwu

doi:10.1109/chinacom.2011.6158308

Cited by 60 publications

(22 citation statements)

References 15 publications

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“…While the profiled studies in the U.S., Finland, Poland, Latvia, Romania, Australia, and New Zealand showed that smartphone devices could be used to differentiate between "good" and "bad" pavement, identify the existence of road defects, and differentiate among road defects, they did not present their data in terms of an industry standard metric, such as IRI. Researchers in China did attempt to develop a low-cost accelerometer and GPS sensor approach to collect IRI data directly, though it was not smartphone-based (Kongyang et al 2011). While concluding their system was effective, they did not employ a baseline IRI for comparison, conducted their data collection at 40 km/h (below the recommended 50-80 km/h data collection speed outlined in Sayers et al), and classified IRI values of 4.9 m/km as "smooth" roads, which does not correspond to the subjective roughness descriptions in Sayers et al (1986).…”

Section: The Potential Of Consumer Technology For Pavement Managementmentioning

confidence: 99%

Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values

2014

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International roughness index (IRI) values were calculated from multi-step processing of accelerometer data collected using three smartphone devices in three consumer vehicles under 11 test scenarios on a 1000 m stretch of secondary highway in New Brunswick. These data were compared to IRI data from a Class 1 inertial profiler averaged over 1000 m (2.60 m/km, std. dev. = 0.029). The combinations of factors producing average IRI values closest to Class 1 inertial profiler were the compact car, Galaxy SIII, windshield mount, at 80 km/h (2.58 m/km, std. dev. = 0.075) and the SUV, iPhone 5, windshield mount, at 50 km/h (2.63 m/km, std. dev. = 0.054). Changes in device type, vehicle type, and mounting arrangement significantly impacted IRI variance, while vehicle speed (50 km/h and 80 km/h) did not. The development of correction factors and analysis automation could make these devices a low-cost option for real-time network-level pavement management.Résumé : Les valeurs de l'indice de rugosité international (IRI) ont été calculées en plusieurs étapes à partir de données d'accéléromètre recueillies en utilisant trois téléphones intelligents dans trois véhicules commerciaux réguliers selon 11 scé-narios sur un segment de 1000 m d'autoroute secondaire au Nouveau-Brunswick. Ces données ont été comparées aux données IRI d'un profilomètre inertiel de Classe 1 dont la moyenne a été établie sur 1000 m (2,60 m/km, écart-type = 0,029). Les combinaisons de facteurs produisant les valeurs IRI moyennes les plus près de celles du profilomètre inertiel de Classe 1 ont été la voiture compacte avec Galaxy SIII installé sur le parebrise à 80 km/h (2,58 m/km, écart-type = 0,075) et le VUS avec iPhone 5 installé sur le parebrise à 50 km/h (2,63 m/km, écart-type = 0,054). Les changements de type de dispositif, de type de véhicule et de montage ont eu un impact important sur la variance de l'IRI, alors que a vitesse du véhicule (50 et 80 km/h) n'ont eu aucun effet. Le développement des facteurs de correction et l'automatisation de l'analyse permettrait d'utiliser ces dispositifs à fiables coûts pour la gestion, en temps réel, des chaussées d'un réseau routier. [Traduit par le Rédaction] Mots-clés : indice de rugosité international, téléphone intelligent, profilomètre inertiel, faible coût, gestion des chaussées.

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Section: The Potential Of Consumer Technology For Pavement Managementmentioning

confidence: 99%

Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values

2014

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“…Their vibration-based system consisted of a low-cost three-axis MEMS accelerometer and a global positioning system (GPS) sensor (this is a typical combination of devices for the described solutions), which, together with an Arduino board, were embedded in a road vehicle in order to monitor roads' condition and potential anomalies. The authors proved that such a solution is less expensive than previously known as [2][3][4]. It was also less expensive than at least one another device using the Arduino board presented in [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of MEMS Sensors for Evaluation of the Dynamics for Cargo Securing on Road Vehicles

Gnap

Jagelčák

Marienka

et al. 2021

Sensors

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Safety is one of the key aspects of the successful transport of cargo. In the case of road transport, the dynamics of a vehicle during normal events such as braking, steering, and evasive maneuver are variable in different places in the vehicle. Several manufacturers provide different dataloggers with acceleration sensors, but the results are not comparable due to different sensor parameters, measurement ranges, sampling frequencies, data filtration, and evaluation of different periods of acceleration. The position of the sensor in the loading area is also important. The accelerations are not the same at all points in the vehicle. The article deals with the measurement of these dynamic events with MEMS sensors on selected points of a vehicle loaded with cargo and with changes in dynamics after certain events that could occur during regular road transport of cargo to analyze the possibilities for monitoring accelerations and the related forces acting on the cargo during transport. The article uses evaluation times of 80, 300, and 1000 ms for accelerations. With the measured values, it is possible to determine the places with a higher risk of cargo damage and not only to adjust the packaging and securing of the cargo, but also to modify the transport routes. Concerning the purposes of securing the cargo in relation to EN 12195-1 and the minimum values of forces for securing the cargo, we focused primarily on the places where the acceleration of 0.5 g was exceeded when analyzing the monitored route. There were 32 of these points in total, all of which were measured by a sensor located at the rear of the semi-trailer. In 31 cases, the limit of 0.5 g was exceeded for an 80-ms evaluation time, and in one case, the value of 0.51 g was reached in the transverse direction for a 300-ms evaluation time.

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“…There is chance to not collect exact GPS data due to various environmental condition such as tunnels, high building, shady trees etc. [11]. An efficient approach to reduce such types of error is not available.…”

Section: Limitations Of Existing Approachesmentioning

confidence: 99%

An Efficient Algorithm for Roadways Obstacles Detection using Smartphone in Infrastructure-less Environment

Pandey¹,

Kumar²,

Gupta³

et al. 2019

IJRTE

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Apart from having smart systems in modern vehicles, roadways traffic positions in infrastructure-free environment are in various ways not suitable for driving which causes roadways accidents. Smart systems are costly, training and maintenance is not practiced by all, hence they are not useful for vehicles running in infrastructure-less environment. Modern days Smartphone are multifunctional, easily operated and consists of numerous sensors, can be utilized for roadways obstacles detection in infrastructure-less environment. In this study, we proposed an efficient algorithm depth learning approach for roadways obstacles detection using Smartphone which is useful for planned as well as unplanned roads. In this, road information is collected using Smartphone accelerometer sensors, data normalization take place using Euler angle method and location determination of obstacles take place using space interpolation method. In comparison to other same type of approach, our algorithm is efficient and cost-effective

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Road condition monitoring using on-board Three-axis Accelerometer and GPS Sensor

Cited by 60 publications

References 15 publications

Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values

Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values

Application of MEMS Sensors for Evaluation of the Dynamics for Cargo Securing on Road Vehicles

An Efficient Algorithm for Roadways Obstacles Detection using Smartphone in Infrastructure-less Environment

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