INTELLIGENT NETWORKED SENSORS FOR INCREASED TRAFFIC …460376/FULLTEXT01.pdf · Paper III Infrared...

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Thesis for the degree of Licentiate of Technology Östersund 2011

INTELLIGENT NETWORKED SENSORS FOR INCREASED TRAFFIC SAFETY

Patrik Jonsson

Supervisors: Prof. Hans-Erik Nilsson

Assistant Prof. Åke Malmberg

Department of Information Technology and Media Mid Sweden University, SE-831 25 Östersund, Sweden

ISSN 1652-8948 Mid Sweden University Licentiate Thesis 68

ISBN 978-91-86694-52-4

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Akademisk avhandling som med tillstånd av Mittuniversitetet i Sundsvall framläggs till offentlig granskning för avläggande av teknologie licentiatexamen onsdag, 12 oktober, 2011, klockan 10:15 i sal Q221, Mittuniversitetet Östersund. Seminariet kommer att hållas på svenska.

INTELLIGENT NETWORKED SENSORS FOR INCREASED TRAFFIC SAFETY

Patrik Jonsson

© Patrik Jonsson, 2011 Department of Information of Technology and Media Mid Sweden University, SE-831 25 Östersund Sweden Telephone: +46 (0)771-975 000 Printed by Kopieringen Mid Sweden University, Sundsvall, Sweden, 2011

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INTELLIGENT NETWORKED SENSORS FOR INCREASED TRAFFIC SAFETY Patrik Jonsson Department of Information Technology and Media Mid Sweden University, SE-831 25 Östersund, Sweden ISSN 1652-8948, Mid Sweden University Licentiate Thesis 68; ISBN 978-91-86694-52-4

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ABSTRACT Our society needs to continuously perform transports of people and goods to ensure that business is kept going. Every disturbance in the transportation of people or goods affects the commerce and may result in economical losses for companies and society. Severe traffic accidents cause personal tragedies for people involved as well as huge costs for the society. Therefore the road authorities continuously try to improve the traffic safety. Traffic safety may be improved by reduced speeds, crash safe cars, tires with better road grip and improved road maintenance. The environmental effects from road maintenance when spreading de-icing chemicals need to be considered, i.e. how much chemicals should be used to maximize traffic safety and minimize the environmental effects. Knowledge about the current and upcoming road condition can improve the road maintenance and hence improve traffic safety. This thesis deals with sensors and models that give information about the road condition. The performance and reliability of existing surface mounted sensors were examined by laboratory experiments. Further research involved field studies to collect data used to develop surface status models based on road weather data and camera images. Field studies have also been performed to find best usage of non intrusive IR technology. The research presented here showed that no single sensor give enough information by itself to safely describe the road condition. However, the results indicated that among the traditional road surface mounted sensors only the active freezing point sensor gave reliable freezing point results. Further research aimed to find a model that could classify the road condition in different road classes from existing road weather sensor data and road images. The result was a model that accurately could distinguish between the road conditions dry, wet, snowy and icy. These road conditions are clearly dissimilar and are therefore used as the definition of the road classes used in this thesis. Finally, results from research regarding remote sensing IR technology showed that it significantly improves knowledge of the road temperature and status compared to data from surface mounted sensors. Keywords: Road Weather information System (RWiS), Remote sensing, InfraRed (IR), Computer models

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SAMMANFATTNING Vårt samhälle bygger på att det finns effektiva transporter av människor och varor för att säkerställa att samhällets funktioner fungerar och att företagen kan genomföra sina affärer. Störningar i transporterna av människor och varor påverkar handeln och kan leda till ekonomiska förluster för både företag och vårt samhälle. Allvarliga trafikolyckor orsakar personliga tragedier för de inblandade samt stora kostnader för samhället. Det är med denna bakgrund som vägmyndigheterna kontinuerligt arbetar med att förbättra trafiksäkerheten. Trafiksäkerheten kan förbättras genom att minska hastigheterna, se till att bilarna blir krocksäkra, krav på däck med bättre väggrepp och ett bättre vägunderhåll. Miljöeffekterna från vinterväghållning där avisningsmedel sprids på vägarna måste beaktas, d.v.s. hur mycket kemikalier bör användas för att maximera trafiksäkerheten och minimera miljöpåverkan. Denna avhandling handlar om sensorer och modeller som ger information om väglaget. En kunskap om aktuellt och kommande väglag kan förbättra väghållningen och därmed öka trafiksäkerheten. I avhandlingen har prestanda och tillförlitlighet hos befintliga vägmonterade sensorer granskats i laboratorieexperiment. Data från fältstudier har använts för att utveckla modeller som kan ge information om vägytans status baserat på meteorologiska mätdata och kamerabilder. Det har också genomförts fältstudier för att utforska den fördelaktigaste användningen av beröringsfri infraröd sensorteknik. Den forskning som presenteras här visar att ingen enskild givare ger tillräcklig information för att säkert beskriva väglaget. Från de traditionella ytmonterade sensorerna drogs slutsatsen att den aktiva fryspunktsgivaren gav de mest tillförlitliga fryspunktsresultaten. Det vidare arbetet handlade om att hitta en modell som skulle kunna klassificera vägförhållanden i olika vägklasser genom att utnyttja information från befintliga sensorer och kamerabilder. Detta arbete resulterade i en modell som tillförlitligt kan särskilja väglagen torr, våt, snöig och isig. Dessa väglag är väsentligt olika och har därför valts som väglagsklasser i denna avhandling. Under en säsong genomfördes även fältförsök med beröringsfri infraröd mätteknik där det visade sig att den beröringsfria teknologin förbättrar kunskapen om vägbanans temperatur och vägbanans status.

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TABLE OF CONTENTS

LIST OF PAPERS .......................................................................................................... VII

ABREVIATIONS .......................................................................................................... VIII

1 INTRODUCTION ...................................................................................................... 1

1.1 BACKGROUND ......................................................................................................... 1

1.1.1 Surface mounted sensors ................................................................................ 2

1.1.2 Road condition discrimination and classification .......................................... 4

1.1.3 IR thermometry ............................................................................................... 5

1.2 PURPOSE ................................................................................................................. 6

1.3 RESEARCH QUESTIONS ............................................................................................ 6

1.4 RESEARCH METHOD ................................................................................................ 7

1.5 CONTRIBUTION ....................................................................................................... 8

1.6 THESIS STRUCTURE ................................................................................................. 8

2 MATERIALS AND METHODS .............................................................................. 9

2.1 DESCRIPTION OF RESEARCH EQUIPMENT AND RESEARCH SITE................................. 9

3 SUMMARY OF PAPERS ........................................................................................ 12

3.1 ACTIVE AND PASSIVE ROAD STATUS SENSORS (PAPER I) ....................................... 12

3.2 ROAD CONDITION DISCRIMINATION AND CLASSIFICATION (PAPER II AND IV) ....... 15

3.3 IR THERMOMETRY (PAPER III) .............................................................................. 29

4 DISCUSSION ........................................................................................................... 34

5 FUTURE RESEARCH .............................................................................................. 36

6 ACKNOWLEDGEMENTS ..................................................................................... 38

7 REFERENCES ........................................................................................................... 39

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LIST OF PAPERS

This thesis is mainly based on the following papers. These papers are referred to as they appear in the reference section at the end of the thesis:

Paper I Road status sensors - a comparison of active and passive sensors [1]

Patrik Jonsson 16th ITS World Congress, Stockholm, 2009, paper Id: 3244

Paper II Road Condition Discrimination using Weather Data and Camera Images [2]

Patrik Jonsson IEEE ITSC 2011, Washington, 2011, paper Id: 186

Paper III Infrared Thermometry in winter road maintenance [3] Patrik Jonsson, Mats Riehm Manuscript

Paper IV Classification of Road Conditions From Camera Images and Weather Data [4]

Patrik Jonsson CIMSA 2011. Ottawa, 2011, paper Id: 1569451369

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ABBREVIATIONS

DB Database dry Road condition with completely dry roads icy Road condition with the road surface is covered with ice IR Infra Red MVDA MultiVariate Data Analysis Non intrusive Contactless measurement methods NN Neural Networks PC Personal Computer PCA Principal Component Analysis PLS Partial Least Squares Pt100 Linear platinum temperature sensor with a resistance of 100

ohm at 0 Celsius. RWiS Road Weather information System snowy Road condition with the road surface covered with snow SQL Server A Microsoft database engine SVM Support Vector Machines STA Swedish Transport Administration (Formerly known as the

Swedish Road Administration) tracks Road condition where there is snow with wheel tracks wet Road condition when the road surface is completely wet

1 Introduction - 1.1 Background

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1 INTRODUCTION

1.1 Background

An extensive knowledge about the road condition is crucial for performing effective and environmentally safe road maintenance. The goal is to maximize the traffic flow and traffic safety while minimizing the effect on the environment. Difficult road weather conditions cause several tragic accidents and give the society a lot of costs for traffic injuries as well as costs for companies due to decreased accessibility [5]. Hence, determining the current, and especially the future road condition, in order to perform effective preventive road maintenance, is an essential task for the society [6]. In order to eliminate the impact of weather, extensive road maintenance is performed in the northern sub-zero regions. Road maintenance involves snow removal and the spread of de-icing chemicals. Before the 1980’s when electronics became common, manual supervision of the road status was performed. The development of automatic field stations equipped with meteorological sensors and remote access possibilities made the manual supervision of road status superfluous. The first countries to deploy Road Weather information Systems (RWiS) were Sweden, Finland and the UK [7]. Today there is a fairly good knowledge of the road status due to the RWiS. Therefore it is possible to perform preventive tasks that minimize risk of slippery roads. The preventive tasks are snow removal and the spread of de-icing fluids. Spreading de-icing fluids is however not healthy for the environment [8], why the amount of chemicals spread on the road surfaces need to be minimized while the road status is kept at a level that maximizes the friction. This optimization problem needs to be improved and new sensors and systems may be helpful. The road is often treated with de-icing chemicals, causing a freezing point depression of the surface fluid, while the surface fluid freezing point changes over time [9]. This makes the prediction of hazardous road condition very difficult as the icing condition may occur at road temperatures between -20 to 0 degrees Celsius instead of precisely at 0 degrees Celsius. Therefore it is not possible to use the road temperature as a single variable that differentiates between wet and ice as suggested by [10]. The actual road condition is often monitored through road cameras that are installed at city road intersections as well as at monitoring stations located at countryside highways. These road cameras were installed with the aim to give the road keepers an overview of the actual road condition [11]. From these cameras it can be seen if the road is covered by snow, if it has been ploughed recently, plus the amount of snow on the road and roadside [12]. It is of high


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importance to differentiate between the road conditions wet and icy and this is very difficult by only considering static image data, even though some successful investigations have been conducted using image analysis [13], neural networks [14, 15] and multivariate analysis [16] on road status images. Local road weather prognosis can be done to estimate the road condition and risk of slipperiness by using the roadside sensor values air temperature and road temperature [17]. The detection of ice formation on the road surface can be found from freezing point sensors that will include the preventive de-icing tasks [1]. However, it is difficult to determine critical road conditions using data from a single sensor as has been observed by [18] regarding hoar-frost. Additional efforts have been made to detect the presence of clear ice on the road surface, so called black ice, by using laser technique [19]. Research has also been done to detect road condition with radar technologies [20]. Surface mounted sensors or road side mounted sensors are located at a specific spot on the road surface or at the roadside. The sensor readings are supposed to represent a road stretch and efforts have been done to address the length stretch problem by building climatologic models [21] and by using vehicle mounted sensors [22]. Laser and radar techniques as well as vehicle mounted sensors are not further evaluated in this thesis. The focus in this thesis is on using existing sensor technologies together with camera images to detect road conditions. The non intrusive approach is achieved by using cameras and IR thermometry.

1.1.1 Surface mounted sensors

The problem with not knowing the actual freezing point is that it may lead to spreading too much or too little de-icing chemicals. Too little de-icing chemicals being spread can cause severe traffic accidents due to slippery roads, and too much may give a negative impact on the environment in terms of contaminated ground water and wells [8, 23]. The importance of finding when a wet road surface freezes implies that a correct monitoring of the wet surface freezing point is performed. Today there are two main types of surface mounted freezing point sensors, passive sensors and active sensors. Chemicals, or road salts, used for road maintenance that lowers the freezing point is sodium chloride (NaCl), calcium chloride (CaCl2), magnesium chloride (MgCl2) and calcium magnesium acetate (CMA). Other chemicals that are used are potassium acetate, urea and ethylene glycol. When road maintenance is done, normally sodium chloride (NaCl) and calcium choride (CaCl2) is used because of the pricing for especially sodium chloride is lower than for other de-icing substances [23]. In the last years the environmental effects of road


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salting has raised the need for alternative de-icing fluids such as different sugar mixtures [7]. The definition of active and passive freezing point detection sensors installed in the road surface is that passive sensors measure the conductivity from a water and salt solution while the active sensors freezes the fluid and detects the freezing point. The passive sensors detect the freezing point by translating the conductivity value to a freezing point value for the de-icing solution used. Passive methods of determining the freezing point involves indirectly determining the freezing point by measuring the conductivity of a de-icing fluid and water solution. The conductivity, κ, is determined from measuring the electrolytic conductance under reference conditions with a specified electrode surface area and with the electrodes spaced at a specified distance as in (1) from [24]. It should also be noted that the conductivity reading has a nonlinear dependency to the weight percent and that the conductivity reading is temperature dependent. This is further discussed in [1] .

(1)

Ω

The active method of determining the freezing point implies a freezing of the water and de-icing fluid solution. Water undergoes a phase shift from liquid to solid state during freezing. Water extracts during the formation of ice and according to the second law of thermodynamics, the water will release its latent heat of fusion when it undergoes a change from water to a more ordered form as ice crystals. When water is cooled to its freezing point at 0° C, it may not freeze. In fact, distilled water that has a freezing point of 0° C can be cooled down to -40° C without being transformed to solid ice state. When the fluid is cooled below its freezing point it is called supercooled. Any contamination of the distilled water will make the transition from liquid to solid at a temperature higher than -40° C. The phenomenon of supercooling is well known and applies to many fluids if the cooling is done sufficiently rapid [25]. When cooling the liquid water solution it can be seen from the isobar in Figure 1, that the water and salt solution will have a clear knee when the first order exothermic transition, or freezing, occurs [26]. Figure 1 also shows the phenomenon of supercooling that occurs when the temperature goes below Tf


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before the time tf, and the freezing point Tf can be observed as the knee after the freezing has occurred.

Figure 1. Freezing point curve. The freezing point, Tf, is found as the knee in the curve after the freezing has occurred at time tf. The rise in temperature originates from the release of latent heat during freezing.

1.1.2 Road condition discrimination and classification

A common RWiS field station has a lot of sensors that monitors the local weather and the road surface temperature. By combining data from all available standard road weather sensors together with extracted features from camera images taken at the same site, it is possible to accurately classify the road condition. A model that takes all sensor data together with camera images in account suggests an automatic road condition classification. The benefit of this kind of computerized model is that it is based on existing hardware and can be deployed without additional hardware and field installation costs. This type of combination of data from several sensors is generally referred to as sensor fusion [27]. Previous research classifying road conditions have used camera images only. Neural networks used with support vector machines for discriminating between gravel roads, snow covered road and paved road has showed an accuracy of 88% to 94% [28]. Another approach reaching 81% to 89% differentiates between the road conditions bare, snow covered and track bare by using support vector machines only [29]. A study discriminating between

Time, t [s]

Temperature, T [C]

Tf

tf


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the road conditions dry, wet, slushy, snowy and icy from camera images using multivariate discriminant analysis is done with a reliability of 84% to 99% [13]. Yet another study is performed utilizing multivariate analysis of images captured by vehicle mounted cameras. The results show a reliability of 83% to 92% [16]. Another way of determining the road condition would be to use probability reasoning on data from several installation sites [30]. This approach implies that the entire raw dataset must be transferred to a common evaluation computer which can be a problem if large amount of data is used for analysis. The probability reasoning approach is not further discussed here. The usage of image processing and sensor fusion is done by Ogura et al. [31]. They limited the sensors to an image detector and two pyranometers that detect the reflection rate. The research presented here uses all standard RWiS sensor data together with standard camera images from one site to determine the road condition. Multivariate Data Analysis (MVDA) is one method of classifying data. Some other common methods are neural networks (NN) [14, 15] and support vector machines (SVM) [28, 29]. In this thesis MVDA is used for road status classification. MVDA is based on mathematical statistics and the well defined developmental process allows for an orderly evaluation of the model performance [32]. MVDA aims to find relations between the input variables and gives information of how they interact. Developed models can operate locally, thus only transferring results to host systems.

1.1.3 IR thermometry

There is a great interest in non intrusive sensor technology for monitoring road status among the world road authorities working with winter maintenance. The main reason for this interest is the simple installation procedure of non intrusive sensors as no physical intrusion is necessary on the road surface. This also avoids costly traffic lane closures with shielding of maintenance personnel [33]. The road authorities interest for remote sensors suggested making a detailed study of how the road surface temperature distribution is varying over time and during changes in weather and changes in road condition. The knowledge of the road temperature distribution is essential for evaluating where new sensors should be installed, knowing the impact of traffic on the road temperature and knowing the impact on road temperature distribution due to changes in weather. Remote sensing technologies give information about the topmost temperature which is crucial for finding possible icing occasions. It is time critical to find the time of freezing of the road surface fluid, and by monitoring the topmost temperature it is possible to give a more exact


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time of freezing and to perform preventive road maintenance before the icing event. The advantage of IR thermometry is that there is no need to have direct contact between the sensor and the object. This implies that it is possible to perform non invasive measurement of temperatures. The temperature range possible to measure with IR thermometry is very wide, between 50K and 6000K. Heat is transferred by three fundamental mechanisms, conduction, convection and radiation. The radiation is electromagnetic radiation which is known as thermal radiation. This thermal radiation can be used to measure temperatures by gathering the thermal radiation and convert it to an electrical signal corresponding to the temperature [34].

1.2 Purpose

The purpose of the research is to find intelligent sensor technology and to develop models that increase the confidence of determined road conditions. Intelligence has been defined in many different ways depending on who made the statement. As an example, intelligence is defined as the “General ability to learn and to deal with problems, new situations, and abstract concepts...” [35]. In this thesis intelligence is defined as the capacity to acquire knowledge from the environment and return data for practical use. Knowing the road condition will reduce the number of traffic accidents due to better road maintenance or better traffic information systems. More trafficable roads will also lead to an increase in the business within the society. Sensors and systems giving a better understanding of the road condition will help to optimize the spreading of chemicals which will reduce costs and help protect our environment.

1.3 Research questions

The all-embracing question for this research is how to increase traffic safety utilizing intelligent sensors and systems. The hypothesis is that it can be achieved by making use of intelligent sensors and systems, in this case non intrusive IR technology and computer models. The research questions connected to the papers follows Figure 2.

1 Introduction - 1.3 Research questions

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Figure 2. Mapping of research questions with papers

1.4 Research method

The research is performed by performing literature studies, laboratory experiments and field studies. Thus, the hypotheses are verified by empirical studies.

Paper I Road status sensors a comparison of active and passive sensors

Paper II Road condition discrimination using weather data and camera images

Paper III Infrared Thermometry in winter road maintenance

How to improve traffic safety?

How can we help saving the environment?

Is it possible to develop intelligent road status detection?

Can non intrusive sensor technology be utilized?

Paper IV Classification of Road Conditions from Camera Images and Weather Data

1 Introduction - 1.5 Contribution

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1.5 Contribution

The research has contributed to the insight that active surface mounted sensors seem to be the most appropriate method of determining the freezing point on wet road surfaces. This is due to the ability to give correct readings when contaminated with common fluids leaking from vehicles and the possibility to detect freezing point of environmentally safe de-icing fluids. Another contribution is that is possible to develop models that makes better use of existing weather data and camera images to determine the road condition. Additionally, the research has contributed with knowledge of how IR thermometry could be utilized for road maintenance. The temperature distribution monitored with IR sensors of winter roads was found to give more exact information about heat transfer and road condition changes than the existing surface mounted sensors [36]. Finally it was found that the traffic intensity could be estimated by using IR thermometry.

1.6 Thesis structure

The thesis is organized in three sections concerning traditional surface mounted sensor technology, road condition discrimination and classification and finally non intrusive IR technology. These sections are organized according to the published papers, where traditional surface mounted sensors are discussed in the first section [1], classification of road conditions in the second section [2, 4] and non intrusive IR technologies in the third section [3]. The section with traditional surface mounted sensors technology shows the performance of existing surface mounted road sensors. The active and passive freezing point detectors are examined and compared based on reliability and accuracy. The next section shows how road conditions can be discriminated and classified using existing weather data and camera images to get a model of how the road status condition can be classified as dry, wet, snowy, icy and tracks, where tracks is defined as a snowy road with wheel tracks. Finally, in the third section a non intrusive IR technology is utilized with an IR camera and IR sensors to monitor the road status. Temperature differences compared to traditional surface mounted sensors is done and a way of finding deviations from a normal and stable dry road condition is suggested.

2 Materials and methods - 2.1 Description of research equipment and research site

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2 MATERIALS AND METHODS

The research was initiated by a literature review and market survey of existing technologies for road status detection. Furthermore, experiments have been conducted on these existing surface mounted sensor technologies in order to evaluate their abilities to measure freezing points and to withstand contaminations commonly present on a road surface. The next step was to collect weather and road status data together with camera images from a RWiS field station. This data was used to find relations and correlations between variables and their influence on different road conditions. The goal was to find a model that could estimate the road status using multivariate data analysis. Finally, a non intrusive infrared sensor technology was investigated and tested on a field site. The research intended to gain new insights of road surface temperatures and road statuses that could be used in future applications.

2.1 Description of research equipment and research site

The research in this thesis is focused on data retrieved from sensors installed at a RWiS site. The sensor at the site is described in Figure 3. Figure 4 and Figure 5 shows pictures from the site where data is collected. The site is localized in the middle of Sweden at 60˚19’44.46”N, 15˚19’06.25”E.

Figure 3. A STA field station equipped with meteorological sensors, standard black and white or a colour camera. Additional equipment is a freezing point sensor and IR camera. A locally installed ruggedized PC was used for extensive logging purposes.

Camera and searchlight

Field computer box

Surface temperature sensor

Freezing pointsensor

Wind speed and direction

Precipitation sensor

Antenna

Air temperature and humidity

IR Camera

Ruggedized PC with SQL Server DB


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Figure 4. Sensor installation framework pole with road weather sensors at the top of the framework pole. The Air temperature and humidity sensor is located in the middle of the mast.

Figure 5. Sensor installation site. Road status sensors are located in the road surface traffic lane closest to the monitoring station. An IR camera and a road image camera are installed near the top of the framework mast.


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Additional sensors installed for research purposes used in this thesis is an infrared camera. This infrared camera is installed on the pole next to the ordinary camera and searchlight, see Figure 5. The infrared camera type was A320 from FLIR systems [37]. The IR camera operates in the range 8-12 µm where the atmospheric absorption is low, see Figure 25. The sensitivity of the camera is less than 70 mK at 30ºC and the accuracy is better than ± 2ºC at 20ºC. The accuracy can be calibrated to achieve a higher quality by using the surface mounted pt100 probes available at the site. A pt100 ground temperature sensor was also installed at 0.3 m. In addition to the IR camera, an IR thermometer of brand Optris CT laser [38] was installed approximately 2 meters above the road and 0.5 meter from the road surface edge. The measurement spot size on the road surface was 8 centimetre in diameter, thus measuring over a much smaller area than the IR camera. The temperature reading from the IR thermometers were recorded at a 1 Hz time interval. The IR thermometer had a spectral range of 8-14 µm and an accuracy of ± 1%.

3 Summary of papers - 3.1 Active and passive road status sensors (paper I)

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3 SUMMARY OF PAPERS

3.1 Active and passive road status sensors (paper I)

The examination of active and passive sensors was done in laboratory environment. A passive sensor made of conductive plastic rings shielded by plastic insulation as in Figure 6 was used.

Figure 6. A passive sensor that measures conductivity. 1 - semiconducting plastic rings, 2 - insulating plastic material, 3 and 4 - temperature sensors

The passive sensor was connected to a conductance meter with a display. As active freezing point sensor the Frensor® was used, see Figure 7 [39]. The Peltier element is thermally connected to a copper body that will receive heat or cold depending if the sensor is cooling or heating the fluid on the other side of the Peltier element.

Figure 7. An active sensor that freezes water to detect the freezing point

The Frensor® controller unit delivered the freezing point values to a computer connected through a serial interface.

Peltier element

Copper (Cu) heat sink


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The performance from active and passive surface mounted sensors was evaluated by laboratory experiments. The sensors ability to withstand contaminations was also examined. The ability to detect freezing points was examined by using the most widely used deicing fluid Sodium Cloride, NaCl. The results showed that both passive and active sensors detect the freezing points correctly during clean laboratory conditions. But when the sensors were affected by substances commonly present on a road surface, differences in the detected freezing point could be observed. The sensor types were tested with respect to their resistance to contaminations from oil, alcohol and glycol. All of these contaminations are present on the road surface and they affect the freezing point.

TABLE 1 RESULTS FROM CONTAMINATION OF FLUID ON PASSIVE AND ACTIVE SENSOR AT 22.5˚C.

Passive sensor Active sensor

Sensor status Conductance change [µS]

Calculated freezing point change [°C]

Freezing point change [°C]

Change in conductance from tap water to tap water with 1 drop of glycol

-12,0 1,40 -2,4

Change in conductance from tap water to tap water with 1 drop of ethylene alcohol

11,0 -1,29 -1,9

Change in conductance from tap water to tap water with 1 drop of oil

46,0 -5,38 -0,2

As seen from Table 1 the freezing point change from the passive sensor is both positive and negative when contaminated. The active sensor shows a small freezing point depression for all contaminations. In order to evaluate an environmentally friendly de-icing fluid, ordinary table sugar was dissolved in tap water and the conductance of the water and sugar mixture was measured with the passive sensor. The freezing point of the sugar solution was also measured with the active sensor. The results are in Table 2.


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TABLE 2

RESULTS OF FREEZING POINT DEPRESSION WITH TABLE SUGAR

Sensor status Freezing point change with active sensor [°C]

Freezing point change [°C] for passive sensor

Tap water with 10% sugar

-0,7 -4,79

The freezing point change detected by the active sensor is supposed to be the exact value because of the measurement principle. The freezing point decrease for the passive sensor may be due to other minerals present in the table sugar. The conclusions from [1] is that passive sensors can be made more ruggedized than active surface mounted freezing point sensors and therefore can be assumed to have a very long lifetime. Active sensors have the advantage of measuring the real freezing point instead of a conductivity value that is translated to a freezing point. Another advantage of active sensors is that they can be used to detect the freezing point of environmentally safe freezing point depression fluids that have no conductivity such as sugar. Normally it is important to know the freezing point in order to be able to perform accurate road maintenance tasks that results in safe traffic and minimum influence on the environment. The experiments indicated that the only way to determine a correct freezing point of the road fluid is by using an active sensor.

3 Summary of papers - 3.2 Road condition discrimination and classification (paper II and IV)

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3.2 Road condition discrimination and classification (paper II and IV)

Applying MVDA to RWiS data gives a possibility to detect road conditions taking all RWiS variables together with extracted features from camera images in consideration. As MVDA is a statistical method the development of a model able to determine road conditions is fully analysable. Road weather data and road status data was collected from a RWiS field station together with camera images. The camera images were compressed using lossless image data compression to ensure that all image data were kept. The data was analysed at specific times and a classification of the corresponding road condition was done manually. From a database with a total of 1176 datasets, 20 datasets with complete meteorological data and corresponding images for each road surface class were randomly selected for the MVDA. Another 20 datasets for each road surface class were randomly selected to be used for verification of the developed model. Even though a lot of datasets were present, only a few of them were describing the road conditions ice and track, which implied the use of only 20 datasets of each road condition to get the same amount of datasets for each road condition. All of the available variables from RWiS and camera images were used for the model development. The camera system gave road images and also information about day or night lighting conditions through a lighting sensor. The images from the camera system had to be converted to image variables, or features, to be able to be analyzed using computer software [19]. In total, 28 real value variables that described the road condition at a specific time were used, see Table 3. As the papers had limited length, additional images and figures are shown here.


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TABLE 3 VARIABLES RETRIEVED THAT DESCRIBED THE ROAD CONDITION

Variable Description

AirTemp Air temperature

AirHumidity Air humidity

AirDewPoint Air dew point

PrecCount Precipitation count, number of particles

PrecSize Precipitation particle size

SurfTemp1 Surface temperature

WindSpeed Wind speed

WindSpeed10 Wind speed as last 10 minute average

WindSpeed30 Wind speed as last 30 minute average

WindSpeedMax Wind speed as maximum value of last 30 minutes

WindDir Wind direction

WindDir10 Wind direction as last 10 minute average

Feature1 Image feature: median grayness of image

Feature2 Image feature: median gray of grayness of image

Feature3 Image feature: Laplace edge

Feature4 Image feature: Roberts edge

Feature5 Image feature: long run vertical emphasis

Feature6 Image feature: short run vertical emphasis

Feature7 Image feature: grey level vertical distribution

Feature8 Image feature: run length vertical distribution

Feature9 Image feature: long run horizontal emphasis

Feature10 Image feature: short run horizontal emphasis

Feature11 Image feature: grey level horizontal distribution

Feature12 Image feature: run length horizontal distribution

Feature13 Image feature: difference to neighboring 4 pixels

Feature14 Image feature: difference to neighboring 8 pixels

D/N (Day) Day signal from detector

D/N (Night) Night signal from detector

Every set of variables have a corresponding road condition class that is set to dry, wet, snowy, icy and tracks, where tracks means snowy with wheel tracks, for every timestamp of the data. By performing MVDA on the 28 variables describing the road status, it was possible to build a model that is able to determine road conditions based on the variable set. It was also possible to identify which variables that best describe the road condition, see [4].


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The MVDA starts with a Principal Component Analysis (PCA) to find relations and correlations between input variables by performing least squares projection for finding principal components. By applying PCA to a problem it is possible to find out if a few principal components can successfully describe a large amount of input variables in the X space. If the PCA analysis shows correlation between input and output variables, the MVDA is extended to a Partial Least Squares (PLS) to find these relations. PLS extends PCA by discriminating the data into two variable blocks, input variables X and output variables Y. PCA finds the components direction having maximum variance in the X space, and PLS finds the directions in the X and Y spaces that corresponds to maximum covariance. In [2] the PCA analysis is described. PLS finds the relationship between Y and X as in (2). !"# $ % (2) The matrix X is the predictor variables. The function f(X) is a linear relation or a polynomial. The PLS analysis has the objectives to approximate X and Y space and to maximize the correlation between X and Y which can be used to predict Y from X. The results from PCA can be found in [2, 4] and the results from PLS in [4]. The mathematical description of PCA and PLS can be found in [32]. The road conditions to be modelled were dry, wet, snowy, icy and tracks. The road condition tracks is defined as a snowy road with wheel tracks. The snow is removed in the wheel tracks and either a wet, dry or icy road condition may be found in the wheel tracks, see Figure 14 and Figure 15. Examples of the road conditions can be found in Figure 8 to Figure 17. In the automatic process of extracting image features, only the part of the images describing the road was used.


18

Figure 8. An example of the road condition dry during daytime.

Figure 9. An example of the road condition dry during night time.


19

Figure 10. An example of the road condition icy during day time.

Figure 11. An example of the road condition icy during night time.


20

Figure 12. An example of the road condition snowy during daytime.

Figure 13. An example of the road condition snowy during night time.


21

Figure 14. An example of the road condition tracks during day time. It is a snowy road with snow removed in the wheel tracks.

Figure 15. An example of the road condition tracks during night time. It is a snowy road with snow removed in the wheel tracks.


22

Figure 16. An example of the road condition wet during daytime.

Figure 17. An example of the road condition wet during night time.

Images and weather data was used to build a model that could distinguish between the road condition dry, wet, snowy, icy and tracks. The road condition tracks is defined as a snowy road with wheel tracks, but the wheel tracks can be dry, wet or icy which may be a problem for the developed model. The model may interpret the dry, wet or icy wheel tracks as the major road condition


23

instead of a part of the snowy road condition with wheel tracks. The model is developed by utilizing MVDA according to the process described by [32]. To start with a PCA (Principal Component Analysis) was done to find out if there were groupings among the road conditions. PCA also shows which variables are important for discriminating different road conditions. In [2] it was found that all variables could be considered important for discriminating the different road conditions. An analysis of how well the different road conditions were discriminated showed that the road conditions were significantly different; and an example can be seen in Figure 18. This implied that further model development could be done. A model was developed by applying PLS to the data. By utilizing PLS (Partial Least Squares) it is possible to analyze relations between X block variables (meteorological parameters, road status parameters and image parameters) and Y block variables (road classes). The resulting road class groups can be seen in Figure 19 to Figure 21 and the relations between variable and road classes can be viewed in Figure 22 to Figure 24. The plots in Figure 19 to Figure 24 show plots for the three most relevant principal components. More details about the model development is described in [4].

-6

-4

-2

0

2

4

6

8

10

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Component 1

Dry Ice Snow Track Wet

Com

pon

ent

2

Figure 18. An example plot from the initial PCA shows a grouping of datasets for different road conditions. This grouping gives a hint that it should be possible to develop a model that can distinguish between different road conditions.


24

Figure 19. A plot of the datasets on the principal components 1 and 2 from the PLS model development shows a grouping of dry, marked with a solid ellipse, and snow, marked with a dashed ellipse. There are indeed two outliers among the snow datasets when looking at components 1 and 2, but they proved to belong to the snow group considering the other principal components.

Figure 20. A plot of the datasets on the principal components 2 and 3 shows a grouping of the road conditions ice, marked with a solid ellipse, and wet, marked with a dashed ellipse.

-6

-4

-2

0

2

4

6

-5 -4 -3 -2 -1 0 1 2 3 4 5

Com

pone

nt 3

Component 2

Dry IceSnowTrack Wet

-5

-4

-3

-2

-1

0

1

2

3

4

5

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Com

pone

nt 2

Component 1

DryIce Snow Track Wet


25

Figure 21. A plot of the datasets on the principal components 3 and 4 shows a grouping of the road condition tracks, marked with a solid circle.

Figure 22. A plot of the relations among the input variables and also relations with the output variables, denoted with Caty(). The output variables are not correlated, which indicates that a model will be able to to distinguish between them.

-0,40

-0,30

-0,20

-0,10

-0,00

0,10

0,20

0,30

-0,30 -0,20 -0,10 -0,00 0,10 0,20 0,30

Com

pone

nt 2

Component 1

XY

AirTempAirHumidit

AirDewPoin

PrecCount

PrecSize

SurfTemp1

WindSpeed

WindDir

Feature1

Feature2

Feature3Feature4

Feature9Feature11

D/N(Day)

Caty(Dry) Caty(Ice)

Caty(Snow)

Caty(Track

Caty(Wet)

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Com

pone

nt 4

Component 3

Dry IceSnowTrack Wet


26

Figure 23. A plot of the relations among the input variables and also relations with the output variables, denoted with Caty(). Wind speed variables are closely correlated as well as wind direction variables, which is expected, as they differ only in the integration time of the data retrieval.

Figure 24. A plot of the relations among the input variables and also relations with the output variables, denoted with Caty(). The image features are well separated, which indicates that they will all contribute to the model performance.

-0,2

0,0

0,2

0,4

0,6

-0,30 -0,20 -0,10 -0,00 0,10 0,20 0,30

Com

pone

nt 4

Component 3

X Y

AirTemp

AirHumidit

AirDewPoin

PrecCountPrecSize

SurfTemp1

WindSpeedWindSpeed1

WindSpeedM

WindDirWindDir10

Feature1

Feature2

Feature3

Feature5Feature7

D/N(Day)

Caty(Dry)

Caty(Ice)Caty(Snow)

Caty(Track

Caty(Wet)

-0,30

-0,20

-0,10

-0,00

0,10

0,20

0,30

-0,40 -0,30 -0,20 -0,10 -0,00 0,10 0,20 0,30

Com

pone

nt 3

Component 2

XY

AirTemp

AirHumidit

PrecCount

PrecSize

SurfTemp1

WindSpeed

WindSpeed3 WindSpeedM

WindDirWindDir10 Feature1

Feature2

Feature3

Feature5

Feature9

Feature10 Feature11

D/N(Day)

Caty(Dry)

Caty(Ice)

Caty(Snow)

Caty(TrackCaty(Wet)


27

TABLE 4

RESULTS FROM PLS ANALYSIS

Yj Dry Ice Snow Track Wet

cj 0,497 0,497 0,497 0,497 0,497

Xi ↓

kji

AirTemp 0,093 -0,237 0,070 0,103 -0,030

AirHumidit -0,320 0,006 0,016 0,106 0,192

AirDewPoin -0,037 -0,219 0,072 0,134 0,051

PrecCount 0,122 -0,154 0,225 0,024 -0,217

PrecSize 0,042 -0,100 0,173 0,067 -0,183

SurfTemp1 -0,057 -0,203 -0,004 0,163 0,101

WindSpeed -0,087 0,131 -0,086 0,080 -0,038

WindSpeed1 -0,050 0,104 -0,021 -0,003 -0,029

WindSpeed3 -0,035 0,068 -0,003 -0,028 -0,002

WindSpeedM 0,013 0,035 0,036 -0,103 0,020

WindDir 0,076 0,037 -0,003 0,078 -0,189

WindDir10 0,052 0,064 -0,026 0,028 -0,117

Feature1 0,080 0,089 0,124 0,103 -0,395

Feature2 -0,075 0,074 -0,100 0,455 -0,354

Feature3 0,350 -0,207 0,058 -0,059 -0,141

Feature4 0,299 -0,182 0,042 -0,048 -0,111

Feature5 -0,122 0,174 -0,004 -0,016 -0,032

Feature6 -0,135 0,063 -0,024 0,042 0,054

Feature7 -0,008 0,000 0,123 -0,059 -0,057

Feature8 -0,123 0,051 0,075 -0,033 0,030

Feature9 0,063 0,003 0,103 -0,052 -0,117

Feature10 0,047 -0,121 0,071 0,011 -0,008

Feature11 0,036 -0,032 0,142 -0,060 -0,085

Feature12 -0,098 0,023 0,087 -0,034 0,022

Feature13 0,160 -0,030 -0,012 0,006 -0,124

Feature14 -0,039 0,056 0,084 -0,053 -0,048

D/N(Day) -0,195 0,209 -0,058 0,084 -0,040

D/N(Night) 0,195 -0,209 0,058 -0,084 0,040

Table 4 shows the model constants for determining the road condition from meteorological variables and from image features. The model is developed according to (3). Every input variable in X is multiplied with a value, k, and added to a constant, c, to get a value for the predicted road condition Y.


28

&' ( $) *+', · ./01

,2 (3)

Yj is the road surface class, j. cj is the constant for the road surface class, j. kji is the value with each input variable Xi should be multiplied. The input variables are indexed by i, and they are numbered from 1 to n, where n = 28. The values for kji and cj are found in table 4. The model was verified by using previously unused datasets. The result is shown in table 5.

TABLE 5 CLASSIFICATION RESULTS FROM VERIFICATION

Modeled road condition

Dry Icy Snowy Tracks Wet

Observed road condition

Percentage of

correct

classifications

Dry 20 0 0 0 0 100 %

Icy 0 10 0 0 1 91 %

Snowy 1 0 19 0 0 95 %

Tracks 0 7 9 4 0 20 %

Wet 0 0 0 1 19 95 %

As the model gave 90-100% correct classification for the road conditions dry, wet, snowy and icy from the prediction set, the conclusion is that a robust model has been developed that can distinguish between different road conditions using camera images and weather data. The verification results in table 5 shows that the road conditions dry, wet, snowy and icy has a good model compliance. The poor model compliance for the road condition tracks can be explained by how this road condition is defined. As tracks is defined as a snowy road with wheel tracks where it can be snowy or icy it is possible to explain the model behaviour for the road class tracks. As the wheel tracks can be dry, wet or icy, the model may classify the road condition according to the status of the wheel tracks instead of looking at a snowy road with wheel tracks.

3 Summary of papers - 3.3 IR thermometry (paper III)

29

3.3 IR thermometry (paper III)

An infrared camera of type A320 from FLIR systems [37] was used for area measurements and an IR thermometer of brand Optris CT laser [38] was used for single spot measurements. The IR measurement technique is based on a fundamental thermodynamic relationship. Plank’s law (4) is used for IR thermometry. See [3] for a detailed description of IR thermometry.

34,6 789:;4<=> ?@ABCDE

(4)

34,6 F G+´ + HI´ J F K

Figure 25. Absorption of IR light by common atmospheric gases. It can be seen that the atmospheric absorbance is low in some wavelength bands, for example between 8-14 µm. This band may thus be utilized for IR thermometry.

As seen from Figure 25 the IR absorption of the atmosphere is low in the frequency range 8 to 14 µm. Therefore, this is a commonly used frequency band for commercial IR temperature sensors. The emissivity, ε, is the relative ability of a surface to emit energy by radiation. The emissivity is the ratio of energy radiated by a particular material to energy radiated by a black body at the same temperature. A black body has ε = 1 while any real object will have ε < 1. The emissivity for a road surface is between 0.8 and 0.99 depending on the precipitation building up on the surface [40], see Table 6. The emissivity variance due to precipitation build-up will affect the temperature reading with only 0.1%, which implies that it is possible to

0 2 4 6 8 10 12 14 16 18 200

0.2

0.4

0.6

0.8

1

Wavelength [µm]

Absorp

tion


30

monitor the temperature without performing emissivity adjustments during precipitation and still maintain acceptable accuracy.

TABLE 6 EMISSIVITY AT 300K

Material Emissivity

Asphalt 0.93 Water 0.96 Ice 0.97 - 0.99 (at 273K) Snow 0.8 - 0.9

Data from all sensors were recorded at a 1 minute time interval and stored in a local database, residing in the ruggedized PC in Figure 3. Backups from the database and from the IR camera images were done regularly to ensure data availability. Images from the colour camera were recorded at a 10 minute interval. The road condition used as a parameter in the evaluation was determined by examining road status data and weather data together with camera images and information from the road keepers about salting and snow removal. The readings from the IR camera and the IR thermometer were compared with pt100 surface sensors and the road surface state in order to find correlations and connections. The data used in this study was collected a winter period from February 2010 to April 2010. The data presented derives from 3 different periods during the year 2010 with varying road conditions. A reference period with dry road conditions started on the 14th of March and ended on the 16th of March. One of the evaluation periods with road conditions varying between snow and ice covered lanes and with dry wheel tracks started on the 21st of February and ended on the 25th of March. Another evaluation period started on the 25th of February and ended on the 3rd of March, with road conditions varying from wet to snowy. According to the Swedish Transport Administration, best practice states that the temperature of the road surface is quite uniform and that the temperature raise in the wheel tracks can be neglected most of the time [33]. Even though the observed temperature differences were small, up to 1.0C, they can cause very different road conditions. This small temperature difference of wheel tracks and in between can cause severe road conditions on parts of the road when the road temperature is close to the road surface fluid freezing point. The temperature distribution is recorded with an IR camera and outliers in the retrieved IR thermometry data as in Figure 26 are caused by vehicles. Figure 27 shows a vehicle caught on a thermal image. As the outlier temperature values differ from the road temperature, it is possible to identify vehicles and to perform traffic counting. The complete results from IR thermometry is described in [3].


31

Figure 26. Outliers in IR temperatur readings. Outliers are marked with red '+'

Figure 27. A thermal image outlier showing a car

One would expect the road surface temperature observed at 2 mm depth to correlate with the IR measurements on the road surface. This is true during dry weather conditions, see table 7, but the correlation of temperatures during weather changes shows a better correlation between the IR and air temperature, see Table 7, Table 8 and Table 9.

TABLE 7 TEMPERATURE CORRELATIONS DURING DRY REFERENCE PERIOD

Air

temperature Surface

temperature IR

temperature

Air temperature

1 0.91 0.76

Surface temperature

0.91 1 0.88

IR temperature

0.76 0.88 1

03/14 03/15 03/16 03/17-10

-5

0

5

10

Tem

per

atu

re [

C]


32

TABLE 8 TEMPERATURE CORRELATIONS DURING A WINTER PERIOD

WITH PARTLY SNOW OR ICE COVERED ROAD SURFACE

Air

temperature Surface

temperature IR

temperature

Air temperature

1 0.20 0.81

Surface temperature

0.20 1 -0.14

IR temperature

0.81 -0.14 1

TABLE 9

TEMPERATURE CORRELATIONS DURING A PERIOD WITH WET AND SOMETIMES SNOWY ROAD SURFACE

Air

temperature Surface

temperature IR

temperature

Air temperature

1 0.73 0.88

Surface temperature

0.73 1 0.53

IR temperature

0.88 0.53 1

It is noticeable that the correlation between the surface temperatures monitored with a pt100 sensor at 2 mm depth and an IR surface temperature sensor is bad during wet, icy and snowy road conditions. It was found that the timestamp for heat flow to change from into the road to out of the road is different for the pt100 surface sensor and the IR thermometry equipment. The common method used today with surface mounted probes may give incorrect readings of the heat flow direction as it responds too slowly to weather changes and the present surface status. Another discovery was that it could potentially be possible to detect changes in the road condition by investigating differences in temperature, as a negative temperature difference tends to indicate changes from a normal dry road condition. However, further research is needed on larger datasets on different roads with varying weather and traffic intensity. Attention needs to be taken when using IR thermometry. The sources contributing to the result of the IR sensor is the object radiation, reflected radiation and atmosphere radiation. The immediately observed cause of error for road surface monitoring was the traffic. On roads with light traffic conditions this is not a severe problem; instead the thermal effects from the vehicles can be used to measure traffic intensity. However, if the road is under heavy traffic conditions or is congested, it may be hard or impossible for the infrared instruments to measure the road surface temperature correctly. Another source that affects the results from IR measurements is the radiation from sunlight, which can cause sudden temperature raises due to direct


33

radiation from the sun or from reflections. Other causes of IR measurement errors may be the relation between the visible sky area and the area covered by obstacles, discrepancy in the pavement, local surface repairs and variations in the road construction [41].

4 Discussion

34

4 DISCUSSION

The limitations with existing technology is not the ability to get qualitative spot measurements, on the contrary, the existing technology gives very exact data at a point of the road. The problem is to get data that represents the actual road condition over a larger surface area. The existing surface mounted sensor technology has often only one sensor mounted in the road surface which should represent a road stretch and a cross section of the road. Efforts to solve this have been done by installing several sensors in the wheel tracks and in-between wheel tracks and applying climate mapping models. But as each sensor has a very small detection area they may easily be affected by small aggregations of de-icing material or contaminations. The research shows that among traditional sensors installed in the road surface, only active freezing point sensors will handle contaminations. By combining data from several sensors it is possible to get an accurate understanding of the road condition, especially if camera images are taken in consideration. By performing Multi Variate Data Analysis (MVDA) on Road Weather information System (RWiS) field station data together with standard RWiS road status camera images it is possible to intelligently classify road condition in the road status conditions dry, wet, snowy, icy and tracks. As the camera image covers an area of the road section the resulting road status class should be more descriptive than were obtained if only the individual sensor data was used. The non intrusive IR thermometry technology gives the possibility to measure the topmost temperature of the road surface. The road temperature is in Swedish RWiS monitored by pt100 sensor probes installed 2 mm below the road surface. The topmost temperature differs significantly from the temperature at 2 mm dept during some weather conditions, especially during wet, snowy and icy road conditions. This concludes that determining the risk for slipperiness from the topmost surface temperature due to frozen road surfaces is improved by using IR thermometry. It was assumed that the freezing of water could be observed by analyzing images from an IR camera, thus being a tool to indicate slipperiness and changes in road conditions. This assumption could not be concluded due to the fact that there were only a very few occasions when water on the road surface actually froze to ice during the research period. If IR camera thermometry is utilized it is possible to monitor the surface temperature over a larger area compared to if only spot IR thermometry is done. But according to the results it may be enough to install spot IR thermometers that measure the surface temperature in the wheel tracks and in-between wheel tracks of the lanes to be able to detect road conditions.

4 Discussion

35

This enables a more cost effective solution that is attractive to introduce at a larger scale. It is tempting to assume that the non intrusive technology may completely replace the surface mounted sensors. This is possible regarding the road status classification but the ability to detect the freezing point is more difficult to perform with non intrusive technologies. The usage of IR technology to detect freezing points involves a natural or forced freezing of the road surface fluid. The natural freezing of the road surface fluid is not acceptable from a road maintenance perspective and a forced freezing implies work on the road surface installing cooling elements. Consequently, it is difficult to measure the freezing point with non intrusive technology. Nevertheless, the pricing of IR sensors and systems are decreasing due to the technological development which will increase the usage of these sensors.

5 Future research

36

5 FUTURE RESEARCH

Surface mounted sensors, especially freezing point sensors, have a very limited detection area. Even though these sensors give accurate freezing points, the freezing point value is only valid on a very small point of the road. According to STA tests this is not representative for a road stretch why the use of new technology is encouraged [42]. Especially a larger detection area is requested.

Figure 28. Water absorption [43, 44]

An idea is to use non intrusive sensor technology at IR frequencies that make it possible to detect different road conditions on a road surface area. The absorption of water is shown in Figure 28 and an idea for future research is to use this absorption to differentiate road conditions. Some products are available today but they all suffer from only detecting road conditions on a very small area, approximately 15 cm in diameter [45]. The goal is to have information about a complete road section including all lanes. This could be achieved by using image sensors operating in appropriate IR bands [46]. The spectral response for an image sensor operating at 250K is shown in Figure 29.

Figure 29. Spectral response from a Sofradir Mars SW detector

1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

Wavelength [µm]

Absorp

tion

5 Future research

37

Preparation for this research has been done as laboratory equipment is procured and a setup is made, see Figure 30. The laboratory equipment has photodiodes with peak sensitivity at 1000 nm, 1500 nm and 2000 nm. The 1000 nm sensor is intended to be used as a reference sensor and the 1500 nm and 2000 nm are intended to detect water, snow or ice, according to the water absorbance shown in Figure 28.

Figure 30. Laboratory equipment for IR sensors. Three optical units containing IR photodiodes and one optical unit containing a light source.

The MVDA approach utilized in this thesis is based on already existing sensors. Improvement of the intelligence in road status classification systems can be done by utilizing MVDA on new sensor technology together with existing sensor technology. An improvement of the system performance is to let individual sensors and sensor systems communicate in networks. By interacting and evaluating data from these sensors and systems it is expected to get previously unknown information. A future research idea is to deploy sensors and applications on intelligent mobile phones that are used by car and lorry drivers. This would give a possibility to deploy large sensor networks at low cost.

6 Acknowledgements

38

6 ACKNOWLEDGEMENTS

I would like to thank Combitech AB for making it possible to perform my PhD studies. The Swedish Transport Administration has also been kind to help with data, field test sites and some financing of materials and equipment during my research.

7 References

39

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