IPASS: Intelligent pavement signaling System

Claussen, Heiko; Aparicio, Juan Pablo; Rosca, Justinian; Tas, Nazif Cihan

doi:10.1109/itsc.2012.6338699

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…en, standard models of these devices were established to detect pavement anomalies in real scenarios. However, it is relatively inefficient when the amount of available devices is too small, which is opposite to our final purpose [79]. e second period is utilizing a certain number of vehicles and smartphones in the same type, such as buses, taxies, trains, and even patrolling cars of traffic and security departments, to minimize differences of data collection devices.…”

Section: Experiments Stagementioning

confidence: 99%

“…Multiple filtering methods were implemented to deal with the noise of sensor signals, the contribution of gravity, and the redundancy of collected data. Additionally, some filters were utilized for collecting metadata, categorizing the data access patterns [78], directly measuring impulse patterns [79], and amplifying the acceleration signals [80]. e most popular filters in this topic included high-pass filter [81], low-pass filter [82,83], and moving average filter [84].…”

Section: Data Processing Phasementioning

confidence: 99%

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Pavement Management Utilizing Mobile Crowd Sensing

Tian

Yuan

Zhou

et al. 2020

Advances in Civil Engineering

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Pavement management, which is vital in road transportation and maintenance, is facing some troubles, such as high costs of labors and machineries, low detecting efficiency, and low update rate of pavement conditions by means of traditional detection ways. Benefiting from the development of mobile communication, mobile computing, and mobile sensing techniques, the intelligence of mobile crowd sensing (MCS), which mainly relies on ubiquitous mobile smart devices in people’s daily lives, has overcome the above drawbacks to a large extent as one new effective and simple measure for pavement management. As a platform for data collection, processing, and visualization, a common smart device can utilize inertial sensor data, photos, videos, subjective reports, and location information to involve the public in pavement anomalies detection. This paper systematically reviewed the studies in this field from 2008 to 2018 to establish an overall knowledge. Through literature collection and screening, a database of studies was set up for analysis. As a result, the year profile of publications and distribution of research areas indicate that there has been a constant attention from researchers in various disciplines. Meanwhile, the distribution of research topic shows that inertial sensors embedded in smartphones have been the most popular data source. Therefore, the process of pavement anomalies detection based on inertial data was reviewed in detail, including preparatory, data collection, and processing phases of the previous experiments. However, some of the key issues in the experimental phases were investigated by previous studies, while some other challenges were not tackled or noticed. Hence, the challenges in both experiment and implementation stages were discussed to improve the studies and practice. Furthermore, several directions for future research are summarized from the main issues and challenges to offer potential opportunities for more relevant research studies and applications in pavement management.

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Section: Experiments Stagementioning

confidence: 99%

Section: Data Processing Phasementioning

confidence: 99%

Pavement Management Utilizing Mobile Crowd Sensing

Tian

Yuan

Zhou

et al. 2020

Advances in Civil Engineering

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“…bumps or potholes [17], [18], [19], or artificial irregularities, e.g. "Braille-like" pavement stripes [20]. In addition, other readings from alternative phone sensors, such as temperature, pressure, sound or radio signal strength, could also be associated to specific zones in a parking.…”

Section: B Distance Calculationmentioning

confidence: 99%

ParkZoom: A parking spot identification system

Alasaadi

Aparicio

Tas

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Accurate localization in outdoor and indoor spaces is a challenging task. The widely used GPS is not designed for high accuracy applications and yields accuracy levels not sufficient for lane or spot level localization. In addition, errors from inertial sensors accumulate with time due to integration drift. We introduce a smartphone based, infrastructure aided parking localization system called ParkZoom for estimating (zooming into) the precise parking spot location of a vehicle during traversal in both indoor and outdoor parking lots. On the vehicle side, the proposed method utilizes conventional smartphones for generating and transferring continuous sensor data, such as accelerometer, gyroscope, and compass readings. On the infrastructure side, ParkZoom employs statistical learning of sensor data signatures, pattern classification of data, constraint propagation and error correction for accurate parking spot identification. The paper presents experimental results with the ParkZoom algorithm obtained on real data in city driving and two parking areas.

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IPASS: Intelligent pavement signaling System

Cited by 2 publications

References 14 publications

Pavement Management Utilizing Mobile Crowd Sensing

Pavement Management Utilizing Mobile Crowd Sensing

ParkZoom: A parking spot identification system

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