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  • 1.
    Ainegren, Mats
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Quality Management and Mechanical Engineering.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Drag Area, Frontal Area and Drag Coefficient in Cross-Country Skiing Techniques2018In: Proceedings, Volume 2, ISEA 2018 / [ed] Hugo Espinosa, David Rowlands, Jonathan Shepherd, David Thiel, MDPI, 2018, Vol. 2, article id 313Conference paper (Refereed)
    Abstract [en]

    The aim of this study was to investigate the air drag, frontal area and coefficient of drag of cross-country skiing classical and free style techniques. One highly skilled cross-country skier performed skiing-like classical and free style techniques on a force plate in a wind tunnel. The skier was also photographed from the front in order to analyze the projected frontal area, which was determined from digital images using Matlab. From the results of the air drag and the frontal area measurements, the drag coefficient was also calculated. The results can be used by researchers to calculate the theoretical effect of air drag on cross-country skiing performance.

  • 2.
    Casselgren, Johan
    et al.
    Lulea Univ Technol, Div Fluid & Expt Mech, S-97187 Lulea, Sweden.
    Rosendahl, Sara
    Lulea Univ Technol, Div Fluid & Expt Mech, S-97187 Lulea, Sweden.
    Sjödahl, Mikael
    Luleå Univ Technol, Div Fluid & Expt Mech, S-97187 Luleå, Sweden.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Road condition analysis using NIR illumination and compensating for surrounding light2016In: Optics and lasers in engineering, ISSN 0143-8166, E-ISSN 1873-0302, Vol. 77, p. 175-182Article in journal (Refereed)
    Abstract [en]

    An investigation of a NIR camera system for road surface classification has been conducted for several road conditions. The surfaces were illuminated with three wavelengths, 980 nm, 1310 nm and 1550 nm and a halogen lamp, to simulate a real environment application with surrounding light. A measuring scheme to deal with surrounding light has been implemented enabling road condition classification from NIR images in a real environment. The retrieved camera images have been analyzed and an RGB representation of the different surfaces has been created to classify the different road conditions. The investigation shows that it is possible to distinguish between dry, moist, wet, frosty, icy and snowy road surfaces using a NIR camera system in a disturbed environment. (c) 2015 Elsevier Ltd. All rights reserved.

  • 3.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Classification of Road Conditions: From Camera Images and Weather Data2011In: 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS (CIMSA), IEEE conference proceedings, 2011, p. 91-96Conference paper (Refereed)
    Abstract [en]

    It is important to correctly determine road condition as it contains essential information for improving traffic safety. Knowledge about the road condition is used by maintenance personnel as a trigger for snow removal and deicing tasks. The presence of severe road conditions is also communicated as warnings and speed reduction recommendations to road users. Previous research shows that road images and data from Road Weather information Systems (RWiS) give enough information to identify road conditions, such as dry, wet, snowy, icy and tracks. The hypothesis of the new model was that it should be possible to develop a model that could classify road conditions from existing RWiS road weather data and road images. This paper proposes a model that gives a correct classification of the road conditions dry, wet, snowy and icy at an accuracy rate of 91% to 100%.

  • 4.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Intelligent networked sensors for increased traffic safety2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Our society needs to continuously perform transports of people and goods toensure that business is kept going. Every disturbance in the transportation ofpeople or goods affects the commerce and may result in economical losses forcompanies and society. Severe traffic accidents cause personal tragedies forpeople involved as well as huge costs for the society. Therefore the roadauthorities continuously try to improve the traffic safety. Traffic safety may beimproved by reduced speeds, crash safe cars, tires with better road grip andimproved road maintenance. The environmental effects from roadmaintenance when spreading de-icing chemicals need to be considered, i.e.how much chemicals should be used to maximize traffic safety and minimizethe environmental effects. Knowledge about the current and upcoming roadcondition can improve the road maintenance and hence improve traffic safety.This thesis deals with sensors and models that give information about the roadcondition.The performance and reliability of existing surface mounted sensors wereexamined by laboratory experiments. Further research involved field studies tocollect data used to develop surface status models based on road weather dataand camera images. Field studies have also been performed to find best usageof non intrusive IR technology.The research presented here showed that no single sensor give enoughinformation by itself to safely describe the road condition. However, the resultsindicated that among the traditional road surface mounted sensors only theactive freezing point sensor gave reliable freezing point results. Furtherresearch aimed to find a model that could classify the road condition indifferent road classes from existing road weather sensor data and road images.The result was a model that accurately could distinguish between the roadconditions dry, wet, snowy and icy. These road conditions are clearly dissimilarand are therefore used as the definition of the road classes used in this thesis.Finally, results from research regarding remote sensing IR technology showedthat it significantly improves knowledge of the road temperature and statuscompared to data from surface mounted sensors.

  • 5.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Remote sensor for winter road surface status detection2011In: Proceedings of IEEE Sensors / [ed] IEEE, IEEE conference proceedings, 2011, p. 1285-1288Conference paper (Refereed)
    Abstract [en]

    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.

  • 6.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Road Condition Discrimination: using Weather Data and Camera Images2011In: 2011 14TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE conference proceedings, 2011, p. 1616-1621Conference paper (Refereed)
    Abstract [en]

    An intelligent way of determining the road condition is needed to perform an effective road maintenance that results in high accessibility of the road network and high traffic safety. The hypothesis is that data from existing meteorological sensors and camera images from Road Weather information Systems (RWiS) could be used to improve the road condition classification. Previous research has found that an image analysis alone can estimate the road condition. This paper aims to evaluate if an extensive dataset retrieved from a RWiS site is sufficient to give a more accurate road condition classification than one obtained with an image analysis alone. The study reveals that RWiS data gives additional information for discrimination of the road conditions compared to image analysis only.

  • 7.
    Jonsson, Patrik
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Surface Status Classification, Utilizing Image Sensor Technology and Computer Models2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    There is a great need to develop systems that can continuously provide correct information about road surface status depending on the prevailing weather conditions. This will minimize accidents and optimize transportation. In this thesis different methods for the determination of the road surface status have been studied and analyzed, and suggestions of new technology are proposed. Information about the road surface status is obtained traditionally from various sensors mounted directly in the road surface. This information must then be analyzed to create automated warning systems for road users and road maintenance personnel. The purpose of this thesis is to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Another purpose is also to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Furthermore, the aim is to develop non-contact technologies able to determine and classify road conditions over a larger area, since there is no system available today that can identify differences in road surface status in the wheel tracks and between the wheel tracks.

    Literature studies have been carried out to find the latest state of the art research and technology, and the research work is mainly based on empirical studies. A large part of the research has involved planning and setting up laboratory experiments to test and verify hypotheses that have emerged from the literature studies. Initially a few traditional road-mounted sensors were analyzed regarding their ability to determine the road conditions and the impact on their measured values when the sensors were exposed to contamination agents such as glycol and oil. Furthermore, non-contact methods for determining the status of the road surface have been studied. Images from cameras working in the visible range, together data from the Swedish Transportation Administration road weather stations, have been used to develop computerized road status classification models that can distinguish between a dry, wet, icy and snowy surface. Field observations have also been performed to get the ground truth for developing these models. In order to improve the ability to accurately distinguish between different surface statuses, measurement systems involving sensors working in the Near-Infrared (NIR) range have been utilized. In this thesis a new imaging method for determining road conditions with NIR camera technology is developed and described. This method was tested in a field study performed during the winter 2013-2014 with successful results.

    The results show that some traditional sensors could be used even with future user-friendly de-icing chemicals. The findings from using visual camera systems and meteorological parameters to determine the road status showed that they provide previously unknown information about road conditions. It was discovered that certain road conditions such as black ice is not always detectable using this technology. Therefore, research was performed that utilized the NIR region where it proved to be possible to detect and distinguish different road conditions, such as black ice. NIR camera technology was introduced in the research since the aim of the thesis was to find a method that provides information on the status of the road over a larger area. The results show that if several images taken in different spectral bands are analyzed with the support of advanced computer models, it is possible to distinguish between a dry, wet, icy and snowy surface. This resulted in the development of a NIR camera system that can distinguish between different surface statuses. Finally, two of these prototype systems for road condition classification were evaluated. These systems were installed at E14 on both sides of the border between Sweden and Norway. The results of these field tests show that this new road status classification, based on NIR imaging spectral analysis, provides new information about the status of the road surface, compared to what can be obtained from existing measurement systems, particularly for detecting differences in and between the wheel tracks.

  • 8.
    Jonsson, Patrik
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Dobslaw, Felix
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Decision support system for variable speed regulation2012Conference paper (Refereed)
    Abstract [en]

    The problem of recommending a suitable speed limit for roads is important for road authorities in order to increase traffic safety. Nowadays, these speed limits can be given more dynamically, with digital speed regulation signs. The challenge here is input from the environment, in combination with probabilities for certain events. Here we present a decision support model based on a dynamic Bayesian network. The purpose of this model is to predict the appropriate speed on the basis of weather data, traffic density and road maintenance activities. The dynamic Bayesian network principle of using uncertainty for the involved variables gives a possibility to model the real conditions. This model shows that it is possible to develop automated decision support systems for variable speed regulation.

  • 9.
    Jonsson, Patrik
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Riehm, Mats
    Royal Institute of Technology, Stockholm, Sweden .
    Infrared Thermometry in winter road maintenance2012In: Journal of Atmospheric and Oceanic Technology, ISSN 0739-0572, E-ISSN 1520-0426, Vol. 29, no 6, p. 846-856Article in journal (Refereed)
    Abstract [en]

    There is significant interest among road authorities in measuring pavement conditions to perform appropriate winter road maintenance. The most common monitoring methods are based on pavement-mounted sensors. This study's hypothesis is that the temperature distribution in a pavement can be measured by means of a nonintrusive method to retrieve the topmost pavement temperature values. By utilizing the latest infrared (IR) technology, it is possible to retrieve additional information concerning both road temperatures and road conditions. The authors discovered that surface temperature readings from IR sensors are more reliable than data retrieved from traditional surface-mounted sensors during wet, snowy, or icyroad conditions. It was also possible to detect changes in the road condition by examining how the temperatures in wheel tracks and in between the wheel tracks differ from a reference dry road condition. The conclusion was that nonintrusive measurement of the road temperature is able to provide an increase in relation to the knowledge about both the road temperature and the road condition. Another conclusion was that the surface temperature should not be considered as being equal to the ground temperatures retrieved from traditional surface-mounted sensors except under conditions of dry, stable roadways. © 2012 American Meteorological Society.

  • 10.
    Jonsson, Patrik
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Thörnberg, Benny
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Casselgren, Johan
    Division of Experimental Mechanics, Luleå University of Technology, Luleå, Sweden .
    Road surface status classification using spectral analysis of NIR camera images2015In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 15, no 3, p. 1641-1656Article in journal (Refereed)
    Abstract [en]

    There is a need for an automated road status classification system considering the vast number of weather-related accidents that occur every winter. Previous research has shown that it is possible to detect hazardous road conditions, including, for example, icy pavements, using single point infrared illumination and infrared detectors. In this paper, we extend this research into camera surveillance of a road section allowing for classification of area segments of weather-related road surface conditions such as wet, snow covered, or icy. Infrared images have been obtained using an infrared camera equipped with a set of optical wavelength filters. The images have primarily been used to develop multivariate data models and also for the classification of road conditions in each pixel. This system is a vast improvement on existing single spot road status classification systems. The resulting imaging system can reliably distinguish between dry, wet, icy, or snow covered sections on road surfaces.

  • 11.
    Jonsson, Patrik
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Thörnberg, Benny
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Dobslaw, Felix
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and System science.
    Vaa, Torgeir
    Road Condition Imaging: Model Development2015Conference paper (Refereed)
    Abstract [en]

    It is important to classify road conditions to plan winter road maintenance, carry out proper actions and issue warnings to road users. Existing sensor systems only cover parts of the road surface and manual observations can vary depending on those who classify the observations. One challenge is to classify road conditions with automatic monitoring systems. This paper presents a model based on data from winter 2013-2014, retrieved from two installations in Sweden and Norway. To address that challenge an innovative and cost effective road condition imaging system, capable of classifying individual pixels of an image as dry, wet, icy or snowy, is evaluated. The system uses a near infra-red image detector and optical wavelength filters. By combining data from images taken from different wavelength filters it is possible to determine the road status by using multiclass classifiers. One classifier for each road condition was developed, which implies that a pixel can be classified to two or more road conditions at the same time. This multiclass problem is solved by developing a Bayesian Network that uses road weather information system data for the calculation of the probabilities for different road conditions.

  • 12.
    Jonsson, Patrik
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Vaa, Torgeir
    Bogren, Jörgen
    Thörnberg, Benny
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Road Condition Imaging - Case StudyManuscript (preprint) (Other academic)
    Abstract [en]

    It is often a problem to get descriptive road status information from the automatic road weather information systems. These monitoring stations are normally equipped with meteorological sensors and road temperature sensors. Even though some monitoring stations have road status sensors and cameras, it is difficult to assess the current road condition. The problem is that the road status sensors cover only a small part of the road’s width and length and camera images are time consuming and difficult to manually evaluate. In this paper, a new automatic road status camera that gives an image of the road status and that covers a whole lane is evaluated. This means that differences the road surface within and outside of wheel tracks can be examined. Hazardous situations with wet wheel tracks and ice in-between the tracks can be detected and road users can be warned until appropriate maintenance have been performed. This new infra-red road status camera was evaluated on data retrieved during winter 2013-2014, and it was found that road conditions differ considerably during the winter, and that differences in and between the wheel tracks occur. A correlation of friction data retrieved by vehicle mounted slip wheels with this new road condition imaging system was completed. The findings indicate that slip friction measurements correlate well with the classified road conditions from the infra-red imaging system.

  • 13.
    Patrik, Jonsson
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Road status sensors: A comparison of active and passive sensors2009In: TS116: Weather monitoring, 2009Conference paper (Refereed)
    Abstract [en]

    Knowledge of the road status and specifically knowledge of the freezing point of the road surface fluid is crucial in order to perform effective and environmentally safe road maintenance. Road status sensors installed in the road can be passive conductivity sensors or active freezing point sensors. In this paper the output from a passive and an active sensor has been studied when the sensors has been contaminated with common chemicals that can be present on the road surface such as oil, alcohol and glycol. The results indicated that only intelligent active sensors reliably can detect freezing points on the road surface.

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