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sensors Article Optical Detection of Dangerous Road Conditions Armando Piccardi * and Lorenzo Colace NooEL—Nonlinear Optics and OptoElectronics Laboratory and CNIT—Photonic Networks and Technologies National Lab, Department of Engineering, University ROMA TRE, Via Vito Volterra 62, 00146 Rome, Italy; lorenzo.colace@uniroma3.it * Correspondence: armando.piccardi@uniroma3.it Received: 13 February 2019; Accepted: 12 March 2019; Published: 19 March 2019 Abstract: We demonstrated an optical method to evaluate the state of asphalt due to the presence of atmospheric agents using the measurement of the polarization/depolarization state of near infrared radiation. Different sensing geometries were studied to determine the most efficient ones in terms of performance, reliability and compactness. Our results showed that we could distinguish between a safe surface and three different dangerous surfaces, demonstrating the reliability and selectivity of the proposed approach and its suitability for implementing a sensor. Keywords: optical sensor; scattering from rough surfaces; polarized scattering; polarization contrast ratio 1. Introduction The assessment of surface conditions that are exposed to the effects of atmospheric agents is crucial when dealing with safety, requiring the development of technologies to exploit sensing elements to assess and reduce the associated risks. For instance, a number of methods have been developed for ice detection, each focused on a specific application from aircraft safety to cooling systems or environmental monitoring: these exploit combined measurements of temperature and humidity [1–3], capacitive [4–6] or piezoelectric [7] sensors and fiber optic devices [8,9], and some of them are available as commercial systems [10]. Contact sensors for ice detection on roads or other surfaces are moderately reliable, but they have problems in terms of installation and maintenance, since they need to be mounted directly on the iced surface. For these reasons, contactless systems based on electrical resistance measurements [11], reflected radiation at microwaves [12] or optical frequencies [13], and infrared imaging [14,15] have been proposed. With reference to road safety, smart systems equipped on both vehicles and the roadside are desirable when there is the need for a reliable way to distinguish between safe and potentially dangerous driving situations [16,17]. A number of methods have been developed based on the refractive index difference of asphalt, water and ice, by measuring light reflection at different wavelengths. Unfortunately, they show limited reliability due to their low sensitivity, thus preventing selective measurement [18–20]. More recently it has been demonstrated that more information can be derived from the analysis of the reflected polarization, which is very sensitive to different environmental conditions [21–24]. Here we propose an optical method to detect different surface conditions based on the measurement of two orthogonally polarized components of a light beam scattered by the surface. Since the polarization of light is sensitive to the surface under investigation, its analysis gives information on the surface itself and it allows to distinguish different conditions. Sensors 2019, 19, 1360; doi:10.3390/s19061360 www.mdpi.com/journal/sensors
Sensors 2019, 19, 1360 2 of 8 This method was proposed in recent paper where the working principle and some preliminary results were reported [25]. It was based on the combined measurement of the polarization contrast ratio and the amplitude of the scattered light, and it focused on the identification of ice on an asphalt surface. In this work, we started with the original idea and its preliminary results and developed an optical method to distinguish different surface conditions. Even though, in principle, this method is not dependent on the type of surface, our work focused on detecting the state of an asphalt surface and assessing potentially dangerous road conditions. Using polarimetry measurement, we investigated different sensing configurations in order to optimize both reliability and selectivity in the detection of asphalt conditions. Furthermore, we implemented a sensing device and developed an electronic read-out suitable to handle the optical signals. 2. Materials and Methods To investigate the different surfaces we employed a diode that emitted at λ = 980 nm. A near infrared source has several advantages: first of all, it is invisible to human eye and therefore it avoids the risk of annoying light scattering; it is in a wavelength range where the most common atmospheric agents (e.g., water and ice) feature a high degree of selectivity [26]; and, additionally, low cost near infrared optoelectronic devices are widely available. The radiation is modulated with a square wave at a frequency of about 1 kHz, which optimizes the detection sensitivity and immunity to environmental (i.e., continuous) light when a narrowband receiver is used. A lens was used to collimate the radiation and obtain a spot size of about 5 mm on the surface under investigation. This solution compensates for beam diffraction, allowing maximization of the light impinging on the detectors. A polarizer can be added to the source to study the sensor response as a function of incident light polarization. Two further polarizers were used to select the polarization components of the scattered light parallel and perpendicular to the incident plane and a couple of photodiodes detected polarized radiation and converted the optical signals into current signals. The front-end electronics performed a current to voltage conversion by means of trans-impedance amplifiers (TIA). The signals corresponding to the orthogonal polarizations were detected by a heterodyne receiver (i.e., a lock-in amplifier) in order to extract and process the information about the surface condition even in the presence of large noise. The block diagram of the sensor is sketched in Figure 1a. The figure of merit that was exploited to distinguish between different surfaces was the polarization contrast ratio (PC). This parameter is a normalized quantity sensitive to the differences between the polarization components, which was defined as PC = (ITE − ITM )/(ITE + ITM ), where ITE/TM is the light intensity corresponding to the two polarization components. In addition, the setup was equipped with a third photodetector in order to measure the total unpolarized light scattered from the surface, thus providing a second useful parameter. Since Fresnel’s coefficients depend on the incidence angle [27], preliminary measurements were performed to identify the optimal geometric configuration. Thus, we first found the angle for which the distance among the polarization contrast values corresponding to different surface states was maximized. Once this angle was found, the polarization contrast was calculated from the converted voltage values (representing the light intensities) and it was plotted against the total (unpolarized) scattered light. In this way, the position of the single point on the parameter space (PC vs unpolarized scattering) is a function of the surface condition [25]. We focused our experiments on asphalt surfaces with the aim of detecting different atmospheric agents affecting road safety. The investigated samples refer to different asphalt conditions, as indicated in Figures 2–5 (later in the text). Four kinds of surfaces were tested. The reference condition corresponds to dry asphalt, which can be identified as a safe condition. Several kinds of asphalts were tested, differing in both roughness and composition. Though no formal classification of the kinds of asphalt
Sensors 2019, 19, 1360 3 of 8 Sensors 2019, 19, x FOR PEER REVIEW 3 of 8 was performed, we verified (using data in Figures 3 and 5, see Section 3) that all kinds of asphalt sharedA first potentially similar dangerouswith optical properties condition respectistothe thepresence scatteredofpolarized water on radiation. the asphalt surface. This has been Astudied in two different first potentially dangerous states: wet asphalt condition and asphalt is the presence where of water surface surface. on the asphalt was completely This has covered been studiedby ainthick layer (i.e., two different a few states: wet millimetres) of water. asphalt and asphalt whereA the second surfacehazardous condition was completely was covered determined by the by a thick layer presence (i.e., of ice: we used a few millimetres) an asphalt of water. surface A second that wascondition hazardous covered by a thin was ice layer by determined (1 mm). the presence of ice: we used an asphalt surface that was covered by a thin ice layer (1 mm). Thus, we investigated investigated the the scattered scattered radiation radiation from from four four different different states, states, identified identified as as dry, dry, wet, wet, water and and ice iceininthe thefollowing. following.We We would like to emphasize that it is not within the scope would like to emphasize that it is not within the scope of this paper of this paper to investigate to investigate the optical the properties optical properties of these of these kinds kinds ofbut of surfaces, surfaces, only tobut only the validate to validate approachthe to approach efficiently to efficiently detect detectcondtions. the surface the surface condtions. Two major geometries were investigated, as shown in Figure 1. The first was based on Fresnel reflection with the source and detectors mounted in a θ–2θ θ–2θ configuration configuration (Figure (Figure 1b). 1b). Using this geometry, the horizontal (vertical) polarized reflected radiation was maximized (minimized) at the Brewster angle (like for a smooth surface) and thus featured a wide range of polarization contrast ratio with benefits to the system sensitivity. Nevertheless, the system required the source (S) and the detector (D) to be placed in different positions, increasing increasing the the space space needed needed for for the the sensor. sensor. The alternative geometry measured the polarization components of the backscattered light that was always present during scattering from a rough surface (Figure 1c). In this case, higher sensitivity was needed neededfrom from the the receiver, receiver, but thebut the resulting resulting sensor was sensor muchwasmoremuchcompact.more compact. In both In both configurations, configurations, the presence of an theadditional presence of an additional detector, used to detector, sense the used to sensescattered unpolarized the unpolarized light from scattered light the direction from the direction of the surface normal, of the surface normal, was considered as well. was considered as well. Figure 1. Block Blockdiagram diagramandandset-up. set-up.(a)(a)Block Blockdiagram diagram of of thethesensor. TheThe sensor. radiation radiationfrom thethe from LED (S) LED was collimated (S) was collimatedby by a lens a lens(L)(L)and andimpinged impingedon on the the surface investigation. After surface under investigation. After being reflected/scattered, reflected/scattered,ititwas wasdetected detectedbybytwo twophotodiodes photodiodes (PD1 (PD1 and PD2). and TwoTwo PD2). polarizers (P1 (P1 polarizers and and P2) selected the horizontal and vertical polarization components. Two trans-impedance P2) selected the horizontal and vertical polarization components. Two trans-impedance amplifiers amplifiers (TIA) and (TIA)the andlock-in amplifier the lock-in converted amplifier the the converted optical signals optical into signals two into twovoltage voltagelevels levelsready readyforforthe the signal signal processing. (b) θ–2θ processing. (b) θ–2θconfiguration. configuration.TheTheincidence incidence angle angle θ was θ was referred referred to surface to the the surface normal normal and aand a third third photodiode photodiode (PD3) (PD3) was usedwas used tothe to measure measure the unpolarized unpolarized scattering. (c)scattering. (c) Backscattering Backscattering configuration. configuration. The set-up wasThe set-up was analogous analogous to (b), but bothtophotodetectors (b), but both collecting photodetectors collectingscattering the polarized the polarized were scattering mounted on were themounted same sideon ofthe thesame side of the source. source. The experimental set-up was realized with commercial components: the source was a LED940E (Thorlabs) with a maximum maximum drivedrive current current ofof 100 mA mA for a power of 18 mW. The polarizers were LPV050 (Thorlabs) with an extinction ratio higher than (Thorlabs) with an extinction ratio higher than 100000:1 100000:1at the employed at the employed wavelength andand wavelength the detectors the were detectors BPW34F were BPW34F(OSRAM) (OSRAM) thatthat worked workedin the 780–1100 in the 780–1100nmnm spectral spectralrange and range andexhibited exhibiteda 2 amaximum maximumresponsivity ofof responsivity 0.70.7 A/W A/Watatλλ==950 950nm nmand andaasensitive sensitivearea 2.65×× 2.65 areaofof2.65 2.65 mm mm2. 3. Results 3. Results In the In the first first geometry, geometry, Figure Figure 1b, 1b, the the source source and and the the detectors detectors were were mounted on two mounted on two arms arms in in aa goniometric system to preserve the symmetry of the incident and reflection angles, with the source goniometric system to preserve the symmetry of the incident and reflection angles, with the source mounted approximately mounted approximately 3030 cm cm from from the the surface surface under under investigation. investigation. AA preliminary preliminary measurement measurement involved the evaluation of the polarization contrast as a function of the incident angle [28]. The results in the case of unpolarized light from the source are shown in Figure 2a, after being averaged over several acquisitions. Most of the curves corresponding to the different kind of surfaces were
Sensors 2019, 19, 1360 4 of 8 involved Sensors 2019,the 19, xevaluation of the polarization FOR PEER REVIEW contrast as a function of the incident angle [28]. The results 4 of 8 in the case of unpolarized light from the source are shown in Figure 2a, after being averaged superposed, over severalpreventing acquisitions. theMost identification of thecorresponding of the curves asphalt conditions. to theOnly for θ kind different = 50° of were the curves surfaces were separated, but the polarization contrast range was small. We repeated the same superposed, preventing the identification of the asphalt conditions. Only for θ = 50 were the curves measurement ◦ using polarized separated,light, but theexploiting the contrast polarization polarization rangedependence was small. We of repeated the Fresnel coefficients. the same measurementFigureusing 2b,c reports on the angular dependence of the scattered light from a source with horizontal polarized light, exploiting the polarization dependence of the Fresnel coefficients. Figure 2b,c reports and vertical polarization, on the angular respectively. dependenceWhen of the using horizontal scattered polarization light from a source with from the source horizontal and(i.e., perpendicular vertical to polarization, the plane of incidence) the curves exhibited some angles (θ > 50°) where the respectively. When using horizontal polarization from the source (i.e., perpendicular to the plane of safe condition was separated incidence)from all the exhibited the curves others, but none some of the(θ“dangerous” angles > 50◦ ) wherestates were the safe clearly distinguishable. condition was separated from On the other all the hand, others, the but vertical none of thepolarized “dangerous”lightstates (i.e., were polarization parallel to the plane clearly distinguishable. On the of other incidence) hand, guaranteed a larger separation between the different conditions, at least around the vertical polarized light (i.e., polarization parallel to the plane of incidence) guaranteed a larger 50°, where the polarization contrast value associated with water (for both the ◦water layer and wet separation between the different conditions, at least around 50 , where the polarization contrast value cases) exhibited a minimum associated.with This water was due(fortoboth the the Brewster angleand water layer associated wet cases)to the water surface exhibited (approximately a minimum. This was due 53°). to The transmitted radiation was minimized, increasing the intensity◦ associated the Brewster angle associated to the water surface (approximately 53 ). The transmitted radiation was with the reflected radiation, minimized, and thus increasing increasing the associated the intensity absolute value with theof the polarization reflected radiation, contrast and thus ratio. Thus, we increasing the concluded that vertical polarized radiation offered the best performances for absolute value of the polarization contrast ratio. Thus, we concluded that vertical polarized radiation the realization of a sensor in this geometry. offered the best performances for the realization of a sensor in this geometry. Figure 2.2. θ–2θ Figure configuration.Polarization θ–2θconfiguration. Polarization contrast contrast versus versus incidence incidence angle angle for unpolarized, for (a) (a) unpolarized, (b) (b) horizontally and (c) vertically polarized incident light. horizontally and (c) vertically polarized incident light. After setting the vertically polarized source to 50◦ with respect to the surface normal, we measured After setting the vertically polarized source to 50° with respect to the surface normal, we the intensities associated with the two polarization components and calculated the polarization contrast measured the intensities associated with the two polarization components and calculated the for all the surfaces under investigation. A large number of acquisitions were taken in order to evaluate polarization contrast for all the surfaces under investigation. A large number of acquisitions were statistical errors and to simulate the “on field” conditions, including motion and different kinds of taken in order to evaluate statistical errors and to simulate the “on field” conditions, including asphalt surfaces. motion and different kinds of asphalt surfaces. To evaluate the surface state, we built two parameter planes. The first included the polarization To evaluate the surface state, we built two parameter planes. The first included the polarization contrast values versus the total unpolarized scattering, evaluated from the measurement of the contrast values versus the total unpolarized scattering, evaluated from the measurement of the light light scattered along the surface normal and collected by the third photodetector. The second was scattered along the surface normal and collected by the third photodetector. The second was composed of the polarization contrast versus the sum of the two polarization components. In this composed of the polarization contrast versus the sum of the two polarization components. In this way, each surface condition could be defined by a sector of the parameter plane, as the values of way, each surface condition could be defined by a sector of the parameter plane, as the values of the the experimental measurements corresponding to the different surfaces generated a group of points experimental measurements corresponding to the different surfaces generated a group of points (with (with thethepoint pointdispersion dispersionbeing beingdue duetoto measurement measurement uncertainty, uncertainty, thethe different different kinds kinds of asphalt, of asphalt, or or slightly different surface conditions, like water or ice layer thickness), which should be slightly different surface conditions, like water or ice layer thickness), which should be included in included in the corresponding part of the the corresponding part of the plane.plane. Figure 33 shows Figure shows the the graphs graphs obtained obtained for for aa vertical vertical polarized polarizedsource sourceand andincidence incidenceangle angleof 50◦ . of50°. The former The former case, case, PC PCversus versusunpolarized unpolarizedscattering scattering(Figure (Figure3a), exhibited 3a), a problem exhibited withwith a problem the dry the (i.e., safe) dry (i.e., and iced (i.e., dangerous) conditions, where the two groups of points were safe) and iced (i.e., dangerous) conditions, where the two groups of points were partially partially overlapping. Thus, the choice overlapping. of geometry Thus, the choice andofmeasured geometryparameters and measureddid notparameters allow selective did measurement for the not allow selective considered surface measurement states. for the Conversely, considered using surface the sum states. of the orthogonal Conversely, using thepolarizations sum of theallowed us to orthogonal polarizations allowed us to discriminate among all four different states even if the voltage signal levels were slightly lower (thus, the required sensitivity was higher), as shown in Figure 3b. Even if the water and wet conditions were very close, they almost correspond to the same kind of dangerous
Sensors 2019, 19, 1360 5 of 8 discriminate among all four different states even if the voltage signal levels were slightly lower (thus, Sensors 2019, 19, the required Sensors 2019, 19, xx FOR PEER PEER REVIEW sensitivity FOR REVIEW was higher), as shown in Figure 3b. Even if the water and wet conditions 55 of of 88 were very close, they almost correspond to the same kind of dangerous conditions, thus the risk of conditions, thus conditions, thus the the risk risk of of confusing confusing the twotwo surfaces surfaces is is not not critical critical and and it it would would not not affect affect the the confusing the two surfaces is not criticalthe and it would not affect the performance of the sensor in an performance performance of the sensor in an of theinvestigation. application-based investigation. sensor in an application-based investigation. application-based Figure Figure 3. Figure 3. Mirror-like Mirror-likeconfiguration. Mirror-like configuration.Polarization configuration. Polarizationcontrast Polarization contrast contrast versus versus versus (a) (a)(a) the thethe total total scattering scattering total and andand scattering (b)sum (b) the (b) the the sum of theof twothe two orthogonal orthogonal polarized polarized components components sum of the two orthogonal polarized components (V and(V V TE and VTM, the voltage corresponding to (VTE , the voltage corresponding to horizontal TE TMand VTM, the voltage corresponding to horizontal and vertical horizontal and vertical polarization, polarization, and vertical polarization, respectively). respectively). respectively). The polarization The polarization contrast polarization contrast was contrast was close was close to close to zero for to zero for both both the the dry dry asphalt asphalt and and the the ice ice cases, cases, which which indicates that indicates that the the scattered scattered light light was was almost almost unpolarized, unpolarized, probably probably duedue to to the the depolarization depolarization effects effects from the the high high surface surface roughness roughness inin both both cases. cases. TheThe presence presence of of water water enhanced enhanced from the high surface roughness in both cases. The presence of water enhanced light polarization,lightlight polarization, polarization, even even if if the if even the groups groups of points the groups of points corresponding ofcorresponding to to the wettoand points corresponding the wet thewater and wet layer water layer were quite and water were layerdispersed.quite were quite dispersed. Nevertheless, dispersed. Nevertheless, this geometry this allows geometry us to allows discriminate us amongto discriminate some among potentially some dangerous Nevertheless, this geometry allows us to discriminate among some potentially dangerous potentially conditions. dangerous Furthermore, conditions. Furthermore, the third photodiode conditions. Furthermore, the is not the third photodiode photodiode is necessary. third is not not necessary. necessary. We performed We performed the the same same kind kind ofof measurement measurement procedure measurement procedure procedure inin the the backscattering backscattering configuration. backscattering configuration. configuration. As for for the the former former geometry, geometry, we we first first verified verified which which angle angle maximized maximized the As for the former geometry, we first verified which angle maximized the separation between the separationseparation between between different different conditions, different conditions, as sketched conditions, as sketched asinsketched Figure 4, inin Figure for Figure 4, for the unpolarized the unpolarized 4, for the(Figure (Figure 4a) and (Figure unpolarized polarized 4a) and polarized (horizontally 4a) and and polarized (horizontally vertically, and Figure vertically, 4b,c, Figures respectively) 4b,c, incidentrespectively) radiation. incident (horizontally and vertically, Figures 4b,c, respectively) incident radiation. radiation. Figure 4.4.Backscattering Figure Backscatteringconfiguration. Polarization configuration. contrast Polarization versus incidence contrast angle for (a) versus incidence incidence unpolarized, angle for (a) (a) Figure 4. Backscattering configuration. Polarization contrast versus angle for (b) horizontally unpolarized, (b) and (c) vertically horizontally and polarized (c) incident vertically light.incident light. polarized unpolarized, (b) horizontally and (c) vertically polarized incident light. It can It can bebe noted notedthat thatthe thepolarization polarizationcontrast contrast was waslower lowerwith respect with to the respect previous to the the case.case. previous ThisThis was It can be noted that the polarization contrast was lower with respect to previous case. This expected was expectedconsidering expected considering the lower considering the signal the lower level lower signal due signal level to different level due due to geometry. to different Nevertheless, different geometry. here geometry. Nevertheless, the Nevertheless, herefour road here the the was conditions four road often exhibited conditions often quite different exhibited quitevalues. In the different unpolarized values. In the case the whole unpolarized case angle the range below whole angle four road conditions often exhibited quite different values. In the unpolarized case the whole angle θ = 40◦below range couldθbe= used, evenbe 40° could could if the dry used, condition even if the wascondition the dry dry always close wasto the iceclose always curve. toWhen iceexamining the ice curve. When Whenthe range below θ = 40° be used, even if condition was always close to the curve. horizontal examining the polarized case, the horizontal we can horizontal polarized easily polarized case, see case, we that we canthe curves can easily easily see were see thatsuperposed that the the curves for curves were almost all were superposed the angles superposed for for examining almost all almost all the the angles angles (except (except for for θ θ == 65°, 65°, where where drydry and and iceice conditions conditions were were still still too too close close to to ensure ensure reliable detection), thus this case was also not taken into consideration. The vertical reliable detection), thus this case was also not taken into consideration. The vertical polarized light polarized light case provided much better results, exhibiting several angles where case provided much better results, exhibiting several angles where all the curves were well all the curves were well separated. In separated. In particular, particular, at at θ θ == 20° 20° the the graph graph shows shows almost almost equal equal distances distances among among the the curves curves in in aa
Sensors 2019, 19, 1360 6 of 8 (except for θ = 65◦ , where dry and ice conditions were still too close to ensure reliable detection), thus this case Sensors was 2019, 19, xalso FORnot PEERtaken into consideration. The vertical polarized light case provided much better REVIEW 6 of 8 results, exhibiting several angles where all the curves were well separated. In particular, at θ = 20◦ quite largeshows the graph interval of about almost equal0.12 for polarization distances among the contrast. curvesAs inaafurther advantage quite large intervalwith respect of about to the 0.12 for unpolarized case, the As polarization contrast. dryacondition had the highest further advantage contrast with respect value, to the and it was unpolarized well case, theseparated from dry condition all hadthethe other highest potentially dangerous contrast value, and it situations. In addition, was well separated frominallthis the case, other the verticaldangerous potentially polarized radiation situations. was considered In addition, in thistocase, be the thevertical best choice to radiation polarized discriminate among theto different was considered be the bestasphalt choice conditions. to discriminate among the different asphalt conditions. Thus, Thus, onceonce thethe incident incident angle angle was set to was set to about about 20 ◦ , we 20°, we measured measured thethe two two polarized polarized scattering scattering components. components. The The results resultsare areshown shownininFigure Figure5,5,with withthethe polarization polarization contrast contrast as aasfunction a function of both of both the the total scattering (Figure 5a) and the sum of the signals corresponding to the total scattering (Figure 5a) and the sum of the signals corresponding to the two orthogonal polarizations two orthogonal polarizations (Figure 5b). (Figure 5b). Backscattering configuration. Polarization contrast versus (a) the total scattering and (b) the Figure 5. Backscattering sum of the two orthogonal polarized components. Remarkably, the Remarkably, the two two graphs graphs were were very very similar, similar, which which made made the the third third photodiode photodiodeunnecessary. unnecessary. In other words, the backscattered light contained all the information needed, allowing usreduce In other words, the backscattered light contained all the information needed, allowing us to the to reduce sensor complexity. the sensor complexity. The dry The dry asphalt asphalthad hadthe thehighest value highest of both value contrast of both and total contrast scattering, and total thus defining scattering, a region thus defining a of safe condition in the parameter space, while the polarization contrast easily allowed region of safe condition in the parameter space, while the polarization contrast easily allowed the the detection of the presence detection of theof ice. Theof presence water layerwater ice. The showedlayertheshowed lower value of (back)scattering the lower due to the smooth value of (back)scattering due to surface, while the group of points corresponding to the wet condition was in the smooth surface, while the group of points corresponding to the wet condition was in the middlethe middle between water betweenandwater dry, as andexpected. The results dry, as expected. forresults The each group for eachof points groupwere well were of points separated well from the others separated from with reasonable dispersion occurring, which allowed us to divide the parameter the others with reasonable dispersion occurring, which allowed us to divide the parameter space space into four parts that four into wereparts associated to the that were different to associated asphalt conditions. the different Forconditions. asphalt example, PC = example, For 0.05 would PCdivide = 0.05the dry would asphalt and the ice states. divide the dry asphalt and the ice states. Hence, the Hence, thegeometry geometry thatthat employed the backscattered employed radiation radiation the backscattered guaranteedguaranteed the best performance. the best Moreover, employing only two photodiodes mounted on the same performance. Moreover, employing only two photodiodes mounted on the same side side of the source allowed for the of the source most compact allowed for theconfiguration most compact for configuration the practical realization of sensors for the practical based onof realization this method. sensors based on this method. 4. Discussion 4. Discussion The presented results demonstrate the possibility of implementing an optical sensor to distinguish several asphalt conditions. The information on the state is given by the position of the measured The presented results demonstrate the possibility of implementing an optical sensor to values on the parameter plane: for instance, Figure 4 suggests that the plane can be divided into four distinguish several asphalt conditions. The information on the state is given by the position of the parts corresponding to the four different states. The edges of the four parts define threshold values measured values on the parameter plane: for instance, Figure 4 suggests that the plane can be of the polarization contrast and the sum of the components, which could be represented by straight divided into four parts corresponding to the four different states. The edges of the four parts define lines. In other words, our results can be used as a calibration of the system. A sensor employing this threshold values of the polarization contrast and the sum of the components, which could be method will measure the suitable quantities by placing the corresponding point on the parameter represented by straight lines. In other words, our results can be used as a calibration of the system. A sensor employing this method will measure the suitable quantities by placing the corresponding point on the parameter plane. The surface state will be identified by comparing the position of the measured point with the calculated thresholds. Future developments of our work will encompass the increased number of detected conditions, including a study of the perturbations coming from disturbances such as the presence of dust or
Sensors 2019, 19, 1360 7 of 8 plane. The surface state will be identified by comparing the position of the measured point with the calculated thresholds. Future developments of our work will encompass the increased number of detected conditions, including a study of the perturbations coming from disturbances such as the presence of dust or water particles in the air, mixed surface conditions, or fast motion. Moreover, the implementation of an algorithm to define the threshold values has to be performed. This should include the definition of the boundaries for each zone, for instance by the statistical analysis of the groups of points over a huge number of acquisitions. Finally, the development of electronics able to automatically process the measured data and provide an alert signal linked to the state of the surface will be required. 5. Conclusions We proposed an optical method for the detection of dangerous road conditions based on the measurement of the polarization state of the scattered radiation from an asphalt surface by two photodiodes that detected two orthogonal polarized light components. Two main geometries were investigated. The first measured the reflected light, while the second measured the backscattered radiation. In both configurations we also evaluated the need for a third photodiode that measured the total unpolarized scattering. The backscattering geometry was the most reliable, allowing for discrimination among dry, wet, water and ice surfaces, and thus distinguishing the safe condition from the other three different danger levels. In addition, in this case the information from the third photodiode were redundant, allowing a compact sensor geometry. Thus, we demonstrated the feasibility of a contactless sensor that featured reliability, compactness and was low cost due to the usage of commonly-used commercial components. We stress that the method used here is not dependent on the surface under investigation, and thus it is not limited to the automotive sector. In fact, the reference surface can change from case to case, e.g., a metallic surface (rather than asphalt) in the case of ice detection on wind turbines or airplane wings. Thus, the same approach can potentially be applied to any case where different surface conditions must be identified. Author Contributions: Conceptualization, L.C.; methodology, investigation, A.P. and L.C.; validation, formal analysis, data curation, A.P.; resources, L.C.; writing—draft preparation, review and editing, A.P. and L.C.; visualization, A.P. and L.C.; supervision, project administration, funding acquisition, L.C. Funding: This work was supported by ACTPHAST (Access CenTer for PHotonics InnovAtion Solutions and Technology Support), grant number P2016-29 (SODARC—Sensor for the Optical detection of DAngerous Road Conditions). Conflicts of Interest: The authors declare no conflict of interest. References 1. Ikiades, A.; Howard, G.; Armstrong, D.J.; Konstantaki, M.; Crossley, S. Measurement of optical diffusion properties of ice for direct detection ice accretion sensors. Sens. Actuator A 2007, 140, 24–31. [CrossRef] 2. Wollenweber, G.C. Icing Condition Detection Using Instantaneous Humidity Sensing. U.S. Patent App. 15,275,013, 29 March 2018. 3. Hydrologicalusa. Available online: https://www.hydrologicalusa.com/products/snow-ice-measurement/ ice-detection-sensor/ (accessed on 6 February 2019). 4. Zhi, X.; Cho, H.C.; Wang, B.; Ahn, C.H.; Moon, H.S.; Go, J.S. Development of a Capacitive Ice Sensor to Measure Ice Growth in Real Time. Sensors 2015, 15, 6688–6698. [CrossRef] [PubMed] 5. Troiano, A.; Pasero, E.; Mesin, L. Remote Monitoring of Ice Formation over a Runway Surface. IEEE Trans. Instr. Meas. 2010, 60, 1091–1101. [CrossRef] 6. Flatscher, M.; Neumayer, M.; Bretterklieber, T. Maintaining critical infrastructure under cold climate conditions: A versatile sensing and heating concept. Sens. Actuators A Phys. 2017, 267, 538–546. [CrossRef] 7. Roy, S.; De Anna, R.G.; Mehregani, M.; Zakar, E. A capacitive ice detection microsensor. Sens. Mater. 2000, 12, 1–14.
Sensors 2019, 19, 1360 8 of 8 8. Ikiades, A. Direct ice detection based on fiber optic sensor architecture. Appl. Phys. Lett. 2007, 91, 104104. [CrossRef] 9. Amiropoulos, K.; Spasopoulos, D.; Ikiades, A. Fiber optic sensor for ice detection on aerodynamic surfaces using plastic optic fiber tapers. In Optical Sensors; OSA Technical Digest; Optical Society of America: Washington, DC, USA, 2018. 10. Vaisala DRS511. Road & Runway Sensor. Available online: http://www.vaisala.com (accessed on 6 February 2019). 11. Tabatabai, H.; Aljuboori, M. A Novel Concrete-Based Sensor for Detection of Ice and Water on Roads and Bridges. Sensors 2017, 17, 2912. [CrossRef] [PubMed] 12. Finkele, R. Optical road-ice detector operating in the near infrared. Electron. Lett. 1997, 33, 1153–1154. [CrossRef] 13. Ogura, T.; Kageyama, I.; Nasukawa, K.; Miyashita, Y.; Kitagawa, H.; Imada, Y. Study on a road surface sensing system for snow and ice road. JSAE Rev. 2002, 23, 333–339. [CrossRef] 14. Muñoz, C.; Márquez, F.; Tomás, J. Ice detection using thermal infrared radiometry on wind turbine blades. Measurement 2016, 93, 157–163. [CrossRef] 15. Abdel-Moati, H.; Morris, J.; Zeng, Y.; Wesley Corie, M.; Garas Yanni, V. Near field ice detection using infrared based optical imaging technology. Opt. Laser Technol. 2018, 99, 402–410. [CrossRef] 16. EPoSS Roadmap Automated Driving 2014. Available online: https://www.smart-systems-integration.org/ publication/eposs-roadmap-automated-driving-2014 (accessed on 14 March 2019). 17. Guerrero-Ibáñez, J.; Zeadally, S.; Contreras-Castillo, J. Sensor Technologies for Intelligent Transportation Systems. Sensors 2018, 18, 1212. [CrossRef] [PubMed] 18. Giles, D.B.; Kemp, D.R. Method and Apparatus for Detecting Ice and Packed Snow. U.S. Patent No. 5,818,339, 6 October 1998. 19. Schmitt, K.; Schaube, W. Method for Determining the Condition of a Roadway Surface. U.S. Patent No. 5,218,206, 8 June 1993. 20. Casselgren, J.; Sjödahl, M.; LeBlanc, J. Angular spectral response from covered asphalt. Appl. Opt. 2007, 46, 4277–4288. [CrossRef] [PubMed] 21. Lu, Y.; Higgins-Luthman, M.J. Black Ice Detection and Warning System. U.S. Patent Appl. No. 2008/0129541 A1, 5 June 2008. 22. Stern, H.; Schaefer, J.; Maali, F. System for Detecting Ice or Snow on Surface Which Specularly Reflects Light. U.S. Patent No. 6,069,565, 30 May 2000. 23. Zhao, D. Inflight Ice Detection System. U.S. Patent 7,370,525, 13 May 2008. 24. Casselgren, J.; Sjödahl, M. Polarization resolved classification of winter road condition in the near-infrared region. Appl. Opt. 2012, 51, 3036–3045. [CrossRef] [PubMed] 25. Colace, L.; Santoni, F.; Assanto, G. A near-infrared optoelectronic approach to detection of road conditions. Opt. Laser Eng. 2013, 51, 633–667. [CrossRef] 26. Kou, L.; Labrie, D.; Chylek, P. Refractive indices of water and ice in the 0.65- to 2.5-µm spectral range. Appl. Opt. 1993, 32, 3531–3540. [CrossRef] [PubMed] 27. O’Donnell, K.A.; Knotts, M.E. Polarization dependence of scattering from one-dimensional rough surfaces. J. Opt. Soc. Am. A 1991, 8, 1126–1131. [CrossRef] 28. Videen, G.; Hsu, J.; Bickel, W.S.; Wolfe, W.L. Polarized light scattered from rough surfaces. J. Opt. Soc. Am. A 1992, 9, 1111–1118. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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