Optical roadstate monitoring for infrastructure-side co-operative traffic safety systems

Kutila, Matti; Jokela, Maria; Burgoa, J.; Barsi, Árpád; Lovas, Tamás; Zangherati, S.

doi:10.1109/ivs.2008.4621162

Cited by 18 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Imaging detectors has the benefit of giving area coverage, thus for pavement condition monitoring showing surface condition variations in wheel tracks and in-between. Images also gives the possibility to apply image analysis on the retrieved data that will further extract information about the surface conditions, as was done with the graininess analysis done by [8]. It can be assumed that an enhanced image analysis will further improve the performance of an imaging surface status sensor system.…”

Section: Cooling Liquid Inletmentioning

confidence: 99%

“…Research using polarized IR light resulted in the knowledge that it is possible to detect ice by comparing the vertical and horizontal components of reflected light [7]. Further research utilizing the polarization phenomena and introducing a graininess analysis for discriminating between road conditions, concluded that a graininess analysis makes it possible to separate ice from other road conditions [8]. Camera imaging systems has been used to discriminate between road conditions using either standard camera images only [9], or using camera images together with meteorological data [10].…”

Section: Introductionmentioning

confidence: 99%

“…Camera imaging systems has been used to discriminate between road conditions using either standard camera images only [9], or using camera images together with meteorological data [10]. Further research with camera systems involving IR camera detectors operating in the spectral region of 900 nm to 1700 nm has also showed promising results in road status classification [8]. The purpose with this paper is to find a cost effective sensor that reliably can distinguish between the road conditions dry, wet, snowy and icy on a road surface.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Remote sensor for winter road surface status detection

Jönsson

2011

2011 IEEE SENSORS Proceedings

View full text Add to dashboard Cite

This paper suggests a cost effective remote sensor for increasing traffic safety by detecting road surface conditions. One limitation of existing systems is the ability to reliably detect the presence of ice and snow on the road surface. By utilizing infrared detectors sensitive in the water absorption spectral range, it is possible to remotely detect the presence of water on a surface. Using the near infrared spectra to detect water is well known, but further research is desired on methods to distinguish water in the form of water, ice and snow. Remote sensors are easy to install and they have low service costs compared to road mounted sensors. Existing remote sensors are currently expensive, but by utilizing cost effective infrared detectors a sensor has been made that can be deployed at any road weather information system. Laboratory results showed that the sensor gave reliable output that distinguishes between the surface conditions dry, wet, snowy and icy.

show abstract

Section: Cooling Liquid Inletmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensor for winter road surface status detection

Jönsson

2011

2011 IEEE SENSORS Proceedings

View full text Add to dashboard Cite

show abstract

“…On the other hand, the SAFESPOT project implemented technology which can be used also by roadside systems [4] to detect reduced friction levels. In both projects, VTT has been developing the camera based low cost system since especially in the on-board case cost factor is the major constrain.…”

Section: State Of the Artmentioning

confidence: 99%

In-vehicle sensor data fusion for road friction monitoring

Kutila

Pyykönen

Kauvo

et al. 2011

2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing

View full text Add to dashboard Cite

This study presents the in-vehicle road friction estimation module to provide information for the driver to adapt his/her driving style. Our main aim is to enable support for saving fuel, minimising ecological impact and improving traffic safety. The module is based on the IcOR camera system developed by VTT which has been combined with inertia unit. Moreover, GPS receiver has been implemented to provide geographical position of the vehicle which is important for the infrastructure side of cooperative traffic services. The test results indicate clear correlation with the lateral accelerations and IcOR road state measures. The most importantoutcome is the chance to reduce number of false alarms of the IcOR system with acceleration based measurement. Moreover, the benefit of the acceleration data is the possibility to maintain the detection reliability of IcOR system in level of 70 % when driving in dark environment such as tunnels or night time.

show abstract

“…The average speed of downstream road segments will slow down due to the occurrence of a sudden event or rain on an upstream road segment. A wet road particularly increases the safety risk of drivers [14]. Nevertheless, the forecast [7], [11] cannot immediately consider the sudden events because of data aggregation delay.…”

Section: Introductionmentioning

confidence: 99%

Average-Speed Forecast and Adjustment via VANETs

Yang

Chou

Tung

et al. 2013

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

A wet road is slippery so vehicles often slow down their speed to increase the safety margin, thus usually reducing the average speed. This reduction in average speed may produce a chain reaction that shifts, extends, or amplifies a slowdown on downstream road segments. Conventional average-speed forecasting approaches are unable to respond to sudden chain reactions because these approaches do not consider the effect of weather factors and upstream road segments. Since accurate forecast of average speed can improve gas consumption, carbon dioxide emissions, and travel time, this paper proposes a short-term average-speed forecast and adjustment (ASFA) approach based on a study of prediction bias correlation among adjacent road segments and on weather factors, such as temperature, humidity, and rainfall. First, this approach applies an artificial neural network to predict the average speed of a road segment. Then, vehicles can monitor current traffic to calculate average speed via vehicular ad hoc networks (VANETs). Finally, the vehicles adjust the predicted average speed according to the observed average speed. Real traffic measurements and weather data are used for the evaluation of the proposed scheme and Civic Boulevard in Taipei City is selected as the prediction target. The results show that the proposed ASFA improves accuracy by 57.4% when compared with a hybrid approach on an urban street during rush hour. This paper estimates and simulates a case study by aggregating traffic data in 186 ms on Shi-Min Boulevard via the VANETs during rush hour.Index Terms-Average speed, data aggregation, forecast, neural networks, vehicular ad hoc networks (VANETs).

show abstract

Optical roadstate monitoring for infrastructure-side co-operative traffic safety systems

Cited by 18 publications

References 2 publications

Remote sensor for winter road surface status detection

Remote sensor for winter road surface status detection

In-vehicle sensor data fusion for road friction monitoring

Average-Speed Forecast and Adjustment via VANETs

Contact Info

Product

Resources

About