Evaluation of a Bicycle-Mounted Ultrasonic Distance Sensor for Monitoring Road Surface Condition

Taniguchi, Yoshiaki; Nishii, Kodai; Hisamatsu, Hiroyuki

doi:10.1109/cicsyn.2015.16

Cited by 18 publications

(8 citation statements)

References 4 publications

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“…Ngwangwa and Heyns [11] used acceleration sensors and an artificial neural network for estimating the condition of a road surface by approximation of its profiles and their roughness classes utilising displacement spectral densities. Taniguchi et al [12] proposed the use of an ultrasonic distance sensor for monitoring road-surface conditions. The authors used a low-cost ultrasonic sensor to measure the road-surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

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Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

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Section: Introductionmentioning

confidence: 99%

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

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“…The first category includes ultrasonic [13], [14] and acoustic [15], [16] based systems that transmit waves between 20 Hz to 20 MHz. These systems have shown a good ability to measure the condition of various types of infrastructure.…”

Section: A Road Survey Systemmentioning

confidence: 99%

“…These systems have shown a good ability to measure the condition of various types of infrastructure. For example, [13] implements a bicycle-mounted ultrasonic system to measure the road profile. Work by [15] uses an acoustic system to examine the thickness of the asphalt concrete layer of a road.…”

Section: A Road Survey Systemmentioning

confidence: 99%

Automatic Road Survey by Using Vehicle Mounted Laser for Road Asset Management

Burrow

Metje

et al. 2020

IEEE Access

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In most countries local roads (i.e., urban and rural) form over 80% of the entire road network and constitute the country's largest asset value. In order for local roads to remain fit for purpose and maintain their value, they require periodic maintenance. To make the best use of scarce maintenance resources, road maintenance needs to be preventative which requires the condition of the road to be assessed periodically. Traditional road surveys suffer from the lack of repeatability and reproducibility, are high cost and time consuming. This work proposes a vehicle mounted point laser system for the automated, rapid and inexpensive measurement of a major mode of local road deterioration, namely fretting. Compared to other technologies such as Ground Penetrating Radar (GPR), visual sensors and the Mobile Laser Scanning (MLS) system, the point laser requires less computational power, is less sensitive to the surrounding environment and is of comparatively low cost. A robust approach is proposed which consists of a number of pre-processing algorithms to deal with noise and the effects of the vehicles dynamic motion, and a signal processing algorithm which analyses histograms of the distance from the road surface measured by the laser to account for changes in road texture. Road fretting measured by the proposed system on a variety of roads is compared with fretting determined using a standard visual assessment process. The results indicate that the proposed system can measure road fretting to the levels of detail which are suitable for planning, programming and preparations road management functions.

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“…Due to the extensive use of bicycles observed in literature for monitoring smart cities ( [8,9,10,11,12]), the bicycle was selected as a crowd sensing probe for monitoring the city environment. The development of the proposed platform was divided into four main steps.…”

Section: Introductionmentioning

confidence: 99%

SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution

Corno¹,

Montanaro²,

Migliore³

et al. 2017

IJECE

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In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users' bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.

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Evaluation of a Bicycle-Mounted Ultrasonic Distance Sensor for Monitoring Road Surface Condition

Cited by 18 publications

References 4 publications

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Automatic Road Survey by Using Vehicle Mounted Laser for Road Asset Management

SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution

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