Using Crowdsourcing Techniques and Mobile Devices for Asphaltic Pavement Quality Recognition

Lima, Lucas Cedro de; Amorim, Vicente J. P.; Pereira, Igor Muzetti; Ribeiro, Filipe Nunes; Oliveira, Ricardo Augusto Rabelo

doi:10.1109/sbesc.2016.029

Cited by 22 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These dependencies are related to the vehicle's driving mode, where the main dependency factor we identified was the longitudinal speed [14,17,25,36,40,41,48,62]. The vehicle speed has two implications.…”

Section: Driving Propertiesmentioning

confidence: 99%

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications.

show abstract

Section: Driving Propertiesmentioning

confidence: 99%

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

show abstract

“…The estimate of the roughness index (IRI) requires a fixed accelerometer in the car cabin, calibration to take into account the tires and the suspension system of the car, and skilled labor. Accelerometer sensors and Global Positioning System (GPS) have been widely employed for detecting surface conditions [10,11,[13][14][15][16][17]. In common, these proposals attempt to detect single anomalies such as potholes, bumps, or other road surface anomalies.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

“…Roadscan, as proposed by [16], classifies segments of streets based on the standard deviation and peaks of the accelerometer readings. Authors show the speed of the vehicle impacts on the accelerometer readings and thus the road roughness sensing.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

“…In summary, the first solutions tackling road surface classification focused on detect anomalies, such as bumps and potholes, and they required specialized equipment [10,11,13]. Solutions aiming to map the road conditions into a metric appeared in [12,15,16,20,22,25]. Among these solutions, only [16,20] considered participatory sensing with users' collaboration.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

“…Solutions aiming to map the road conditions into a metric appeared in [12,15,16,20,22,25]. Among these solutions, only [16,20] considered participatory sensing with users' collaboration. Table 1 summarizes the main characteristics of each one of the proposed solution.…”

Section: Road Surface Conditions Based On Accelerometers Readingsmentioning

confidence: 99%

See 2 more Smart Citations

A participatory sensing framework to classify road surface quality

Nunes

Mota

2019

J Internet Serv Appl

View full text Add to dashboard Cite

Participatory sensing networks rely on gathering personal data from mobile devices to infer global knowledge. Participatory sensing has been used for real-time traffic monitoring, where the global traffic conditions are based on information provided by individual devices. However, fewer initiatives address asphalt quality conditions, which is an essential aspect of the route decision process. This article proposes Streetcheck, a framework to classify road surface quality through participatory sensing. Streetcheck gathers mobile devices' sensors such as Global Positioning System (GPS) and accelerometer, as well as users' ratings on road surface quality. A classification system aggregates the data, filters them, and extracts a set of features as input for supervised learning algorithms. Twenty volunteers carried out tests using Streetcheck on 1,200 km of urban roads of Minas Gerais (Brazil). Streetcheck reached up to 90.64% of accuracy on classifying road surface quality.

show abstract