Slippery Road Detection by Using Different Methods of Polarised Light

Casselgren, Johan; Kutila, Matti; Jokela, Maria

doi:10.1007/978-3-642-29673-4_19

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…In contact systems, lighting and powerful machines can easily be positioned at the roadside stations, but it is difficult to accomplish this in portable systems. 5 In contact-free remote measurement systems, image processing has been done with sonic radars, infrared detectors and cameras. [6][7][8][9] With the help of wavelength-sensitive cameras, 80%-90% success rate was achieved in determining the snowy, icy road condition, while the success rate in determining the dry and wet road condition was 70% at most.…”

Section: Related Workmentioning

confidence: 99%

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The design and implementation of road condition warning system for drivers

Teke

Duran

2019

Measurement and Control

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Intelligent transportation systems are advanced applications that inform vehicle drivers about road conditions. The main purpose of the intelligent transportation systems is to reduce either tangible or intangible loss for the drivers by ensuring the safety of passengers and vehicles. In this study, a system is designed and implemented using wireless sensor networks to inform vehicle drivers about the condition of the road surface. Icing has been chosen as the primary focus of the study since it is considered to be a big threat to road and driver’s safety. The temperature at 10 cm depth of the road, air temperature, relative humidity, air pressure and conductivity values are used as the input data for the prediction of icing on the road surface. The data were previously collected on Raspberry Pi which is a single-board computer and the data were read and processed instantly via k-nearest neighbor algorithm. Using these collected data, the road surface condition is classified as icy, dry, wet or salty-wet. The analyzed results for the road surface condition are presented to the drivers via a mobile application in real time. The drivers are alerted visually and audibly as they approach the coordinates on the road where risky conditions are present.

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Section: Related Workmentioning

confidence: 99%

“…The success of road surface prediction is between 80% and 90% in sensor-based optical systems, but for camera-based optical reflection (ICOR) systems it is between 70% and 80%. 5…”

Section: Related Workmentioning

confidence: 99%

The design and implementation of road condition warning system for drivers

Teke

Duran

2019

Measurement and Control

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“…A common piece of equipment in use for testing and evaluating road conditions is the road condition sensor (RCS), which can be used in the embedded measurement mode (Habib and Mohammed, 2017;Troiano et al, 2010;Zhi et al, 2015) or noncontact measurement mode (Casselgren et al, 2012a(Casselgren et al, , 2012bCasselgren et al, 2007;Ewan et al, 2013;Piccardi and Colace, 2019;Ruiz-Llata et al, 2017;Viikari et al, 2009) to classify the road conditions with high accuracy. The embedded measurement mode exhibits good reliability but needs to be embedded in the asphalt, with consequent maintenance problems and costs, whereas the noncontact measurement mode is more flexible.…”

Section: Introductionmentioning

confidence: 99%

“…The embedded measurement mode exhibits good reliability but needs to be embedded in the asphalt, with consequent maintenance problems and costs, whereas the noncontact measurement mode is more flexible. The quantitative calibration of RCS is critical for assessing road conditions because often, there is a need to measure the quantitative information of different road conditions to start mitigating measures (Bernhard et al, 2019;Casselgren et al, 2012aCasselgren et al, , 2012bGagnon et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

New method for the calibration of a road condition sensor

Yang

2022

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Purpose This study aims to improve the calibration accquracy of the road condition sensor. A road condition sensor is widely used to detect water or ice deposits on the road to assess associated driving risks. Its quantitative calibration is central to the thickness measurement accuracy and conventionally performed using the single fitting method-based calibration method. One existing limitation is that the distribution characteristics of calibration data of different road conditions are diversified, which leads to the fitting error can not be minimized when using the conventional calibration method. Thus, the multiple fitting methods-based calibration method is developed to realize an optimal calibration for the road condition sensor. Design/methodology/approach A fitting method assignment for the calibration data of different road conditions was introduced to realize an optimal combination for fitting method and calibration data. In the experiments, the calibration methods were tested in the absence of measurement errors, then tested with calibration data, and finally, in the covering thickness measurement. Findings The comparison results indicate that compared with the conventional calibration method, the multiple fitting methods-based calibration method cuts the fitting error in the quantitative calibration by 13.3% and contributes to reducing the thickness measurement error by 8.11% for different road conditions. Originality/value The multiple fitting methods-based calibration method has been successfully applied for quantitative calibration and shown to reduce calibration errors. The comparison between different calibration methods demonstrates the superior performance of the new calibration method.

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“…Conversely, different perception approaches have been proposed for a true prediction like the use of radars [15] or lasers [13]. The use of standard cameras have been proposed as well [3,8,11,14] exploiting the different polarization of the light reflected from the road surface.…”

Section: Introductionmentioning

confidence: 99%

Adverse Driving Conditions Alert: Investigations on the SWIR Bandwidth for Road Status Monitoring

Bertozzi

Fedriga

D’Ambrosio

2013

Image Analysis and Processing – ICIAP 2013

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Abstract. The 2WIDE SENSE (WIDE spectral band & WIDE dynamics multifunctional imaging SENSor Enabling safer car transportation) EU funded project is aimed at the development of a low-cost camera sensor for automotive applications able to acquire the full visible to Short Wave InfraRed (SWIR) spectrum, from 400 to 1700 nm. This paper presents the results obtained using this extended spectral responsivity sensor for a Road Status Monitoring application to inspect the vehicle's frontal area and detect layers of ice or water on the road surface.

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Slippery Road Detection by Using Different Methods of Polarised Light

Cited by 6 publications

References 3 publications

The design and implementation of road condition warning system for drivers

The design and implementation of road condition warning system for drivers

New method for the calibration of a road condition sensor

Adverse Driving Conditions Alert: Investigations on the SWIR Bandwidth for Road Status Monitoring

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