Untitled record

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DESCRIPTION

Rain sensor with fractal condenser (s).

This invention relates to a system for detecting the presence of rain in embodiments of a vehicle windshield, one or more of the sensor capacitor (s) has a fractal geometry.

Background and Summary of Example Embodiments of the Invention

Moisture (eg rain or condensation) on vehicle windshields and / or rear windows can create dangerous driving conditions for drivers, passengers and pedestrians if not removed quickly. Windshield wiper blades are a common and well known way to remove such materials and reduce the risks of driving during hazardous conditions. Rain sensors have been developed to detect the presence of humidity (p. rain or other condensation) on the vehicle windshield, and to turn the windshield wipers on and off, as necessary, when such moisture is detected. Automatic detection of rain, sleet, fog and the like, and taking appropriate actions (for example, activating / deactivating the wiper blades at an appropriate speed) potentially reduces driver distractions, allowing the driver to better focus on the road ahead. in front. However, improperly activating / deactivating the windshield wipers or not activating them in wet conditions can also create dangerous conditions. Additionally, such systems are also susceptible to "dirt" distractions that can cause false windshield wiper readings / actions when dirt is on the windshield.

Some conventional rain sensors are based on an electro-optical concept. According to certain such techniques, raindrops are detected only by measuring the change in the total internal reflection of a light beam outside the glass-air interface. Other electro-optical techniques have attempted to analyze the luminosity of a section of a window "image" to detect water droplets or mist on a window. However, these optical techniques have limited detection areas, are quite expensive, and can result in erroneous detection indications due to the use of optical images as the sole detection method.

Netzer US Patent No. 6,373,263 explains how to use capacitive rain sensors and read the differential current between two capacitors on the windshield. Unfortunately, the Netzer system also has significant drawbacks. For example, the Netzer system may be subject to certain harmful effects of electromagnetic interference (EMI), as well as interference from other sources. For example, when external bodies (p. g., a human hand, radio waves, etc.) interfere with the function of the capacitors, the charges of the excitation and receiving electrodes can vary uncontrollably in Netzer, leading to false alarms or detections and hence to possibly produce false windshield wiper actions and / or detections. The Netzer system is also subject to possible false readings produced by drastic temperature changes in view of the reference capacitor system used by Netzer, where the Netzer reference capacitor has a different geometry / shape / size than the sensing capacitor.

A rain sensor with a condenser having a meander shape is described in US 2003/0080871.

Thus, it will be appreciated that there is a need in the art for a rain sensor that has efficient operation and / or detection.

In accordance with this invention, the capacitors are formed based on a fractal pattern. One or more of the capacitors are formed based on a Hilbert fractal pattern or a Cantor set. These fractal structures maximize or amplify the periphery and therefore result in a large capacitance for a given area. The use of two-dimensional fractal designs also allows the sensor to take up little physical space in the window and at the same time be electrically larger than its physical size. Concentrating lateral flow in a fractal geometry may also allow the sensor to detect rain / water not necessarily distributed over the actual physical area of ​​the sensor in certain example embodiments of this invention. Also, at its highest iteration (s) a fractal capacitor (s) has (have) an attribute of being its own Faraday screen or Faraday quasi-screen which can reduce the effects. EMI interference or the like.

Brief description of the drawings

These and other features and advantages will be better and more fully understood by reference to the following detailed description of exemplary illustrative embodiments in conjunction with the drawings, of which:

Figure 1 (a) is a component block diagram of an exemplary rain sensor.

Figure 1 (b) is a cross-sectional view of a rain sensor, which can use the features of Fig. 1 (a) and / or one or more of Figs. 2-12.

Figure 1 (c) is a cross-sectional view of a rain sensor, which can use the features of Fig. 1 (a) and / or one or more of Figs. 2-12.

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Figure 1 (d) is a cross-sectional view of a rain sensor, which can use the features of Fig. 1 (a) and / or one or more of Figs. 2-12.

Figure 1 (e) is a cross-sectional view of a rain sensor, which can use the features of Fig. 1 (a) and / or one or more of Figs. 2-12.

Figure 1 (f) is a cross-sectional view of a rain sensor, which can use the features of Fig. 1 (a) and / or one or more of Figs. 2-12.

Figure 2A is an exemplary optimized pattern for a quadrant capacitive system based on Hilbert fractals, wherein said capacitors may be provided in the window as a sensor system in one or more of Figs. 1 (a) -1 (f) and 4-12, for example.

Figure 2B is another exemplary optimized pattern for a quadrant capacitive system, wherein said capacitors may be provided in the window as a sensor system in one or more of Figs. 1 (a) -1 (f) and 4-12, for example.

Figure 3 is an enlarged drawing of another example quadrant capacitive system, in which said capacitors may be provided in the window as a sensor system in one or more of Figs. 1 (a) -1 (f) and 4-12, for example.

Figure 4 is an example circuit diagram including example circuitry used for a write clock pulse in read-out electronics, for use in one or more of Figs. 1 (a) -1 (f) and 5-12, for example.

Figure 5 is an exemplary circuit diagram including exemplary circuitry used for an erase clock pulse in read-out electronics, for use in one or more of Figs. 1 (a) -1 (f) and 6-12, for example.

Figure 6 is an exemplary timer diagram derived from the read-out circuitry of Figs. 4-5.

Figure 7 is an exemplary flow chart or state diagram showing how self-correction and cross-correlation data can be used to control the windshield wipers, which can be used in conjunction with one or more of Figs. 1-6 and 8-12.

Figure 8 is an exemplary flow chart showing how self-correction and cross-correlation data can be used to control the wipers, which can be used in conjunction with one or more of Figs. 1-7 and 912.

Figure 9 is an exemplary stylized view of how a raindrop can travel across a windshield.

Figure 10 is a graph plotting exemplary experimentally obtained maximum values ​​of non-normalized autocorrelations for different disturbances.

Figure 11A is an experimentally obtained autocorrelation snapshot of an example indicative of heavy rain.

Figure 11B is an exemplary experimentally obtained autocorrelation snapshot indicative of a slight haze.

Figure 11C is an example experimentally obtained autocorrelation snapshot indicative of CB radio interference.

Figure 11D is an exemplary experimentally obtained autocorrelation snapshot indicative of a body grounded to a voltage.

Figure 12A is an exemplary correlation matrix indicative of light rain.

Figure 12B is an exemplary correlation matrix indicative of heavy rain.

Figure 13 is an example of autocorrelation in accordance with an example embodiment of this invention.

Figure 14 is a table showing example cross-correlation data for capacitors C1, C2.

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Figure 15 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 16 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 17 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 18 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 19 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 20 is a cross-correlation graph, in which cross-correlation values ​​are plotted versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 21 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 22 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 23 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 24 is a cross-correlation graph, plotting cross-correlation values ​​versus time periods (time periods are in microseconds in the time domain), using certain signals from Fig. 14.

Figure 25 is a block diagram illustrating circuitry and / or signal processing when a sensing capacitor (eg C1) is present, including sigma-delta modulation.

Figure 26 is a block diagram illustrating circuitry and / or signal processing when a plurality of capacitors (eg C1-C4) are present, including sigma-delta modulation.

Figure 27 is a block diagram illustrating sigma-delta modulation; this processing being carried out by means of a circuit system, in firmware and / or software.

Figures 28 (a) and (28b) are schematic diagrams illustrating the advantages of using floating electrodes for sensing capacitors (eg C1-C4).

Detailed description

Referring now more specifically to the accompanying drawings in which like reference numerals indicate like parts throughout.

A rain detection system is provided and includes capacitance-based detection that translates a physical input signal (eg, the presence of a drop of water on a windshield, or the like) into a digital electrical voltage signal. that is received and interpreted in a software program (s) or in a circuit (s) that decide (s) whether or not the wipers should be activated, and, if so, optionally their speed adequate. In this way, capacitive coupling is used to detect water and / or other material on the outer surface of a window such as the windshield, sunroof and / or rear window of a vehicle. It will be appreciated that computational methods can be carried out by hardware or a combination of hardware and software in different example embodiments of this invention. No capacitance or reference capacitor may be required, (i.e. no compensation capacitor is necessary).

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The system can take advantage of a permittivity equation, which provides a physical quantity that describes how an electric field affects and is affected by a medium. An example basic permittivity equation is:

D = ε₀Ε + Ρ, where D is the electric flux, ε₀ is the dielectric constant of a vacuum, E is an electric field (eg the voltage established between plates or electrodes divided by the distance, or V / m), and P is the polarization. The polarization P can be described mathematically in greater detail as:

P = e_rand₀E, where e_r is the relative permittivity (eg, the dielectric constant of water, ice, dirt, or anything else that might be found on an exterior surface of a window such as a windshield). In general, a high value of will correspond to a high polarizability. The permittivity of glass is approximately 8, and the permittivity of water is approximately 85. By substitution and factorization, then, the permittivity equation can be rewritten as:

D = s_t(s_r +1) E.

In this way, it can be seen that D is the response to excitation E.

The capacitance C is given by C = Q / V, where Q is the charge and V is the potential, in volts. Furthermore, C = O / V, where Φ is the electric flux associated with the charge Q. By Gauss's Law:

<b = ÍEdA, where dA is the area of ​​a differential square on the closed surface S. By substitution, then, it is clearly seen that the capacitance is related to the potential difference:

These equations form the basis of an example technique to measure the interaction of water on glass using a sensor with a capacitive system to scan over the window (eg, glass). In particular, data from a sensor that includes at least one or two or more capacitor (s) (eg C1, C2, C3, etc.) can be used to detect whether or not there is moisture (eg. rain or the like) on an exterior surface of a window such as the windshield or rear window of a vehicle. The above equations illustrate that the presence of water on the window surface can affect the capacitance of a properly placed sensing capacitor.

Fig. 1 (a) is an example component block diagram of a humidity (eg, rain) sensor. Power supply 10 is connected to readout electronics 12 which may include one or more of hardware, firmware, and / or software. As will be described in greater detail below, the sensor includes one or more capacitors to construct a capacitive sensor 5 in certain example embodiments. Capacitors each having a pair of approximately coplanar electrodes arranged in a fractal pattern are used in the sensor according to the invention. The fractal pattern can be divided into a capacitive system. While the window can be flat or curved, the capacitor electrodes of a given sensing capacitor (C1, C2, C3 and / or C4) are considerably coplanar to each other and are supported by the flat or curved window, even though it may have a little curvature of the glass. The data from and / or related to the sensor capacitor (s) of the capacitive sensor 5 are received and read by means of an output reading electronics 12 that can be composed of one or more of hardware, firmware and / or software. The read-out electronics 12 capture electrical noise and convert it to digital signal (s). These digital signal (s) are passed to a calculation module 14 (which can be composed of one or more of hardware, firmware and / or software) that determines what

ES 2 347 005 T3 action should be carried out by the windscreen wipers. For example, the windshield wipers can initiate a single wiper action, low speed wiper actions, high speed wiper actions, etc., based on the data analyzed from and / or related to the capacitive sensor. The windshield wipers can also be made to deactivate, reduce / increase the speed of the wipers, etc., based on the data analyzed from and / or related to the capacitive sensor. A wiper control system motor 16 receives instructions from computing module 14 and commands the wipers 18. to take appropriate action.

The capacitive sensor 5 is connected to a Local Interconnection Bus (LIN bus) of a vehicle. A LIN bus (not shown) is usually a serial bus to which slave devices in a car are connected. A LIN bus typically performs a handshake with slave devices to ensure that they are, for example, connected and in a functional state. In addition, a LIN bus can provide other information to slave devices, such as the current time.

Capacitive sensor 5 includes a plurality of capacitors in the form of any suitable system.

Fig. 1 (b) is a cross-sectional view of a vehicle window that includes a humidity sensor. A vehicle windshield includes an interior glass substrate 1 and an exterior glass substrate 2 which are laminated at the same time by means of an interlayer 3 that includes a polymer of a material such as polyvinyl butyral (PVB) or the like. An optional low e (low emissivity) coating 4 may be provided on the inner surface of the outer glass substrate 2 (or even on the surface of the substrate 1). A low E 4 coating generally includes at least a thin IR reflective layer of a material such as silver, gold or the like sandwiched between at least a first and second dielectric layers of a material such as silicon nitride, tin oxide , zinc oxide, or the like. Exemplary low E 4 coatings, for exemplary purposes and without limitation, are described in US Pat.^you 6,686,050,6,723,211,6,782,718, 6,749,941,6,730,352,6,802,943,4,782,216, 3,682,528, and 6,936,347.

Fig. 1 (b) illustrates an example capacitor of the capacitive sensor. While the capacitive sensor of Fig. 1 (a) generally includes a plurality of capacitors in a system, only one sensor capacitor is shown in Fig. 1 (b) for the sake of simplicity. The other capacitors are similar in cross section to that shown in Fig. 1 (b) in certain example embodiments of this invention. The example capacitor (C1, C2, C3, or C4) of the capacitive sensor shown in Fig. 1 (b) includes a pair of separate coplanar or substantially coplanar capacitor electrodes 7 and 8. Electrodes 7 and 8 are made of a conductive material that can be printed or otherwise formed on the window. For example, the electrodes of capacitor 7 and 8 of the sensing capacitor may be made of or include silver, ITO (indium tin oxide), or other suitable conductive material. The condenser shown in Fig. 1 (b) is affected by a drop of water on the outer surface of the window because the electric field Es from the condenser extends to or beyond the outer surface of the window as shown in Fig. 1 (b) and thus can interact with the raindrop or other material on the outer surface of the window. The signals received from and / or related to the sensor capacitor (s) and the analysis thereof are described herein.

In Fig. 1 (b), an opaque insulating layer (eg, black frit or enamel, or the like) 9 is provided in the window over the electrodes 7 and 8 in order to hide the electrodes 7, 8 of the view of the passenger (s) sitting inside the vehicle. In this way, it will be appreciated that the opaque layer 9 is provided only in a small part of the window, including in the area where the capacitive system of the rain sensor capacitor system is located. The capacitive rain sensor system and thus the opaque layer 9 can be located on the windshield of a vehicle in an area close to the rear view mirror mounting bracket. The opaque layer 9 (eg, black frit or enamel) can contact the fractal pattern of the capacitor electrodes 7, 8 directly because the layer 9 is not conductive. However, even if a layer of black frit 9 were conductive (which is possible), its dielectric constant is close to that of water so it will not adversely interfere with the capture of data from and / or related to capacitors. C1-C4 and associated analysis:

FIG. 2A is a top or plan view illustrating an example capacitive sensor system that includes four capacitors C1, C2, C3, and C4. Each of these capacitors C1, C2, C3 and C4 includes separate first and second coplanar capacitor electrodes 7 and 8 as shown in Fig. 1 (b) (or in any of Fig. 1 (c) -1 (F)). The capacitor electrodes 7 and 8 of each capacitor C1-C4 can be made of conductive silver frit or the like as shown in Fig. 2A. In addition, there may be a gap 22 of about 0.2 to 1.5 mm, more preferably about 0.3 to 1.0 mm (eg, 0.6 mm), between the coplanar capacitor electrodes 7 and 8 of each capacitor (C1, C2, C3 and / or C4) as shown in Fig. 2A. In Fig. 2A, the capacitors C1-C4 are covered with an insulating black frit layer 9 which is the same as the opaque layer 9 indicated above with respect to Fig. 1 (b). In Fig. 2A, a contact plate system is provided in the center of the sensor system, and includes four contact plates electrically connected to the respective electrodes 7 of the capacitors C1-C4, and four contact plates electrically connected to the respective electrodes 8 capacitors C1-C4. An example contact plate is indicated by reference numeral 28 in FIG. 2A. The four white contact plates 28 in Fig. 2A are electrically connected to the respective capacitor electrodes 7 of the capacitors C1-C4, while the dark gray contact plates 28 of Fig. 2A are electrically connected to the respective capacitor electrodes 8 of the capacitors C1-C4 . All C1-C4 sensing capacitors are sensitive to moisture such as rain on the outer surface of the window.

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In the embodiment of the invention of Fig. 2A, each of the capacitors C1-C4 of the capacitive sensor is formed using a fractal geometry. Specifically, each of the coplanar electrodes 7 and 8 of each capacitor C1-C4 is formed with a fractal geometry. Fractal design patterns allow large capacitance to be obtained in a very small area, and are therefore desirable over other geometries in rain sensor applications.

In the embodiment of Fig. 2A, it can be seen that the coplanar electrodes 7 and 8 of each capacitor (where the electrodes 7 and 8 are shown but not marked in Fig. 2A due to the dark color of the frit 9, but they are separated by spaces 22) have fractal geometries and are arranged substantially parallel to each other over the entire sinuous length of each capacitor. In other words, each electrode 7, 8 of a given capacitor (p. eg, C1, C2, C3, or C4) has a sinuous shape in fractal geometry, but remains substantially parallel to the other electrode (the other 7, 8) of the capacitor over the entire sinuous length of the capacitor. The overall length of each capacitor (p. g. C1), along the sinuous length of the fractal, is about 25 to 200mm in certain example embodiments of this invention, more preferably about 30 to 90mm, with one example being about 50 mm.

The fractal pattern in Fig. 2A is a Hilbert fractal pattern. Electrodes 7, 8 of capacitors C1-C4 in the embodiment of Fig. 2A form a Hilbert fractal pattern. In particular, the capacitors shown in Fig. 2A have a third order Hilbert fractal shape. Hilbert fractals are continuous, space-filling fractals with fractal dimensions of two. This means that higher order fractals will have a more square shape. A Hilbert fractal can be formed using the following L system:

Hilbert {

Angle 90

Axiom X

X - -YF + XFX + FYγ s = -t-XF ~ YFY ~ -FX + where "Angle 90" sets the following rotations to 90 degrees, X and Y are defined functions, "F" means "draw forward", " + ”Means“ turn counter-clockwise ”, and“ - ”means“ turn clockwise ”. In certain example embodiments of this invention, as shown in Figs. 2A, 2B and 3, all the sensing capacitors of the sensor system can have an identical or almost identical shape.

Each of the C1-C4 capacitors in the sensor system can be electrically floating (this may be called a virtual ground in certain example cases) in order not to have a fixed common ground like fixed zero volts, and / or be spatially separate or similar which may be useful with regard to correlation functions. Furthermore, the lack of a common ground means that the capacitive system will not be subject to adverse effects of interference such as EMI interference thereby reducing the potential for false wiper actions, false detections, and the like.

The fractal design for capacitors C1-C4 can be used in any of Figs. 1 (a) -1 (f).

Fig. 1 (c) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Figs. 2-12. In Fig. 1 (c), the vehicle window (eg, rear window) is made of only one sheet of glass 10, and the electrodes 7, 8 of the capacitor are arranged on, directly or indirectly, the surface. main interior of the glass sheet 10. The condenser (eg, C1) shown in Fig. 1 (c) is designed in such a way that it is affected by a raindrop (or other material) on the outer surface of the window because the electric field Es from the condenser extends to or beyond the outer surface of the window as shown in Fig. 1 (c) and in that way it can interact with the raindrop or other material on the outer surface of the window. Each of the capacitors C1-C4 is formed similarly. It should be noted that the use of the word "about" herein covers both directly about and indirectly about, and is not limited to physical contact unless expressly stated. An opaque layer 9, similar to that shown in the embodiment of Fig. 1 (b), can also be provided in the embodiment of Fig. 1 (c) if desired.

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Fig. 1 (d) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Figs. 2-12. In Fig. 1 (d), the vehicle window (eg, laminated windshield) includes glass sheets 1 and 2 laminated at the same time by means of a polymer-based interlayer 3, and optionally includes a coating low E 4 on either substrates 1 or 2. Fig. 1 (d) differs from Fig. 1 (b) in which the electrodes 7, 8 of the capacitor are arranged on the main surface of the glass substrate 1 which is furthest from the interior of the vehicle. The electrodes of the capacitor 7, 8 may contact the polymer interlayer 3 in this embodiment, in certain example cases. The capacitor (eg, C1, C2, C3, or C4) shown in Fig. 1 (d) is designed in such a way that it is affected by a raindrop (or other material) on the outer surface of the window because the electric field Es from the condenser extends to or beyond the outer surface of the window as shown in Fig. 1 (d) and in that way it can interact with the raindrop or other material on the outer surface of the window. Each of the capacitors C1-C4 of the sensor system is formed in a manner similar to that shown for the capacitor of Fig. 1 (d). The opaque layer 9 can also be arranged in Fig. 1 (d) if desired, on a part of the window so that the electrodes of the capacitor are hidden from view of the passengers of the vehicle. In fig. 1 (d), the electrodes 7 and 8 may be formed of a conductive silver frit or ITO printed or stamped directly on and in contact with the surface of the substrate 1. However, this invention is not limited thereto, and the electrodes 7 and 8 of one or more of the sensor capacitors may instead be formed and stamped from a metallic conductive IR reflective layer (e.g. g., a silver-based layer) of a low E 4 coating that is supported by the window.

Fig. 1 (e) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Figs. 2-12. In Fig. 1 (e), the vehicle window (eg, laminated windshield) includes glass sheets 1 and 2 laminated at the same time by means of a polymer-based interlayer 3, and optionally includes a coating low E 4 on either substrates 1 or 2. Fig. 1 (e) differs from Fig. 1 (b) in that the capacitor electrodes 7, 8 (p. eg, C1, C2, C3 or C4) are arranged on the main surface of the outer glass substrate 2 that is closest to the interior of the vehicle. The capacitor electrodes 7, 8 can make contact with the polymer interlayer 3. The capacitor (eg, C1, C2, C3 or C4) shown in Fig. 1 (e) is designed in such a way that it is affected by a raindrop (or other material) on the outer surface of the window because the electric field Es from the condenser extends to or beyond the outer surface of the window as shown in Fig. 1 (e) and in that way it can interact with the raindrop or other material on the outer surface of the window. Each of the capacitors C1-C4 of the sensor system is formed in a manner similar to that shown for the capacitor of Fig. 1 (e). The opaque layer 9 can also be arranged in Fig. 1 (e) if desired, over a part of the window so that the condenser electrodes are hidden from view of a vehicle passenger (s).

Fig. 1 (f) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Figs. 2-12. In Fig. 1 (1 '), the vehicle window (eg, laminated windshield) includes glass sheets 1 and 2 laminated at the same time by means of a polymer-based interlayer 3, and optionally includes a low E 4 coating on either substrates 1 or 2. Fig. 1 (f) differs from Fig. 1 (b) in that the capacitor electrodes 7, 8 (p. e.g. C1, C2, C3 or C4) are arranged on the main surface of the interior glass substrate 1 that is closest to the interior of the vehicle, through the support element 12. The support element 12, located between the substrate of glass 1 and the electrodes 7, 8, can be made of glass, silicon or the like. The capacitor (eg, C1, C2, C3, or C4) shown in Fig. 1 (e) is designed in such a way that it is affected by a raindrop (or other material) on the outer surface of the window because the electric field Es from the condenser extends to or beyond the outer surface of the window as shown in Fig. 1 (f) and in that way it can interact with the raindrop or other material on the outer surface of the window. Each of the capacitors C1-C4 of the sensor system is formed in a manner similar to that shown for the capacitor of Fig. 1 (f). The opaque layer 9 can also be arranged in Fig. 1 (f) if desired , on a part of the window so that the electrodes 7, 8 of the capacitor are hidden from view of a passenger (s) of vehicle.

FIG. 2B is a plan view of an example pattern for a quadrant capacitive system of the C1-C4 fractal shaped capacitors for the capacitive sensor in accordance with this invention. The four capacitors shown in Fig. 2B are similar to those in Fig. 2A, except for their specific shapes. The capacitors of Fig. 2B can be used in any of Figs. 1 (a) - (f). The overlapping dotted lines show the divisions in four different capacitors C1-C4. The width of the outer line can be about 2mm, and the width of the inner line can be about 1mm.

FIG. 3 is an enlarged drawing of another example quadrant capacitive system of C1-C4 fractal shaped capacitors for the capacitive sensor in accordance with this invention. The four capacitors shown in Fig. 3 are similar to those in Figs. 2A and 2B, except for their specific shapes. The fractal capacitors of Fig. 3 can be used in any of Figs. 1 (a) - (f). The overlapping lines show an example split between capacitors C1-C4 in Fig. 3. It will be appreciated that some example embodiments may have capacitive systems with only two capacitors. However, it is preferred to have at least four capacitors to capture and obtain nuances in disturbances.

The use of fractal geometry for sensing capacitors C1-C4 is advantageous in reducing false readings due to EMI interference. Specifically, fractals at high iterations help reduce EMI interference incidences, because the Faraday cage or quasi-Faraday cage of the fractal at high iterations reduces EMI coupling, thereby reducing the adverse effects of EMI interference. . Fractals at high iterations form quasi-Faraday cages.

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The read-out electronics examine the interaction of rain and / or other disturbances on the window. This process can be carried out by sequentially charging the capacitors, reading their data, quantifying this data, and / or removing the charges.

Fig. 4 is a circuit diagram of a sense or read-out circuit. The detection circuit of Fig. 4 can be made up of an electronic unit 12 and the capacitive sensor system 5 of Fig. 1. Either of the capacitors of Figs. 1 (b) -1 (f), 2A, 2B and / or 3 can be used as the capacitors C1-C4 of the circuit of Fig. 4. The circuit system of Fig. 4 is used for a clock pulse of writing to the read-out electronics. Transistors Q1, Q2 and Q7 are p-channel MOSFETs, with transistors Q1 and Q2 being mainly responsible for a write phase. Transistors Q5 and Q6 are n-channel MOSFETs.

Still referring to Fig. 4, during a writing phase a writing pulse Clk is input._Wr at the gate of transistor Q7, which functions as a resistor or a switch, charging one or more of the capacitors C1C4 of the sensor capacitance C_s. Fig. 6 includes certain signals used in the circuit of Fig. 4 in the write cycle. In the write cycle, transistor Q1 is in saturated mode, since its gate and drain are connected, so Q1 is on. Q4, Q5, and Q6 are off, and Q2 is on during write mode. Transistors Q3 and Q4 are optional. When Q7 is activated by the write pulse, we have a write cycle, and Vcc appears in Cs through A and charges one or more of the capacitors C1-C4 from the sensor capacitance Cs. V_DC it can be a constant voltage, like 5V. One or more of the capacitors C1-C4 can be charged at the same time during a write cycle. However, the circuit charges and reads capacitors C1, C2, C3, and C4, one by one (eg, see Fig. 6). In this way, during a write cycle, only one of the capacitors C1, C2, C3 or C4 is charged.

The above process described for the left side of the sensor circuit of Fig. 4 is basically duplicated on the opposite side or right side of the circuit of Fig. 4. As current flows through the left side branch, current also flows in B across the right-hand side branch, and changes to C are imitated_s, or are substantially mimicked in the internal replication capacitance C_int. When Q7 turns on, current also flows through Q2 (which is on) and charges C_int using Vcc. In this way, the charging of one of the capacitors C1-C4 is imitated by the charging of the capacitor C_int. In other words, C_int is charged to the same level, or substantially the same level, as the capacitor (eg C1) that is being charged on the other side of the circuit of Fig. 4. The output voltage of the circuit of Fig. 4, Vout ( or Vo), is based on C_int and is taken at or near an electrode of capacitor C_intas shown in Fig. 4. An example formula reflecting Vout (or Vo) is shown at the bottom of Fig. 4. Therefore, it will be appreciated that the output Vout (or Vo) of the circuit of Fig. 4-5 refers to and is based on capacitors C1-C4 of sensor Cs. More specifically, the output Vout of the circuit of Fig. 4-5 refers to and is indicative of the state of capacitors C1-C4 and the effects on said capacitors of moisture on the outer surface of the window, even though Vout is not taken directly from capacitors C1-C4. Specifically, Vout (or Vo) is captured during a write cycle, due to the write pulse shown in Fig. 4 (see also Fig. 6). In the formula at the bottom of Fig. 4 for Vout, W1 is for Q1, W2 is for Q2, L1 is for Q1, L2 is for Q2, where W is the transistor channel width, and L is the transistor channel length; and V_T is a threshold voltage of each MOSFET. Note that alternatively the output Vout of the circuit can be taken directly (rather than indirectly through C_int) from sensing capacitors C1-C4.

Transistors Q3, Q4 are optional. These transistors can be at low voltages (eg off) during the write phase, and on during the erase phase.

The output signal Vout (or Vo) of the sensor circuit of Fig. 4 (and Fig. 5) is modulated by sigma-delta modulation. Sigma-delta modulators, which can be used in a sigmadelta digital-to-analog converter (DAC), can provide a level of shaping or filtering of quantization noise that may be present. Exemplary sigma-delta modulators that may be used are described in US Pat.^you 6,975,257, 6,972,704, 6,967,608 and 6,980,144. In sigma-delta conversion, oversampling, noise shaping and / or decimation filtering can be performed. Exemplary advantages of sigma-delta modulation include one or more of: i) the analog antislip filter requirements are less complex and therefore may be cheaper than certain example nyquist-based systems; ii) a sample and hold loop system can be used due to the high input sample rate and low precision A / D conversion; iii) Since there may be digital filtering phase (s) behind the A / D conversion, the noise injected during the conversion process such as power supply oscillation, voltage reference noise and noise in the own A / D converter; iv) since the sigma-delta converter may be essentially linear it may not suffer from appreciable differential nonlinearities and / or the background noise level (s) may be independent of the input signal level. Improved S / N ratios can be obtained.

Fig. 25 which is a simplified version of a sigma-delta modulator system, to modulate and / or convert the output signal Vout (or Vo) of the circuit of Fig. 4 (and Fig. 5). In Fig. 25, a write pulse (see pulse at the bottom of Fig. 25) is used to charge the sensing capacitor (C1, C2, C3, or C4) as explained above with respect to Fig. 5. Square wave excitation is used (p. g., for write and / or erase cycles) on the sensing capacitor to charge and discharge the same This process is duplicated or mimicked, for C_int, as explained herein. The output signal Vout (or Vo) of the circuit of Fig. 4 is sigma-delta modulated by a sigma-delta modulator 60. Modulator 60 can take the form of a hardware, firmware, and / or software circuit. Clock pulses 62 from a clock are input to modulator 60, which latches a quantizer of modulator 60. Once

ES 2 347 005 T3 that the output signals Vout (or Vo) have been modulated by the sigma-delta modulator 60, the modulated signals 64 are sent to an optional digital filter 66 (e.g. a low pass filter or the like ). Digital filter 66 processes sigma-delta modulator digital output 64, which is a train of 0s and 1s. The data is then appropriately scaled using a calibration coefficient (s). The filtered data 68 is then read through a serial interface 69 or the like and sent to a computer that performs correlation calculations for data packet segments. In this manner, a correlation (eg, autocorrelation or cross-correlation) is then applied to the data at interface 69 as explained herein. Fig. 26 is similar to Fig. 25, except that Fig. 26 illustrates a system of C1-C4 sensor capacitors that are multiplexed through a multiplexer.

FIG. 27 is a block diagram illustrating an example of sigma-delta modulation that can be performed in modulator 60 of FIGS. 25-26- Again, this modulation can be carried out by means of a circuit, firmware and / or software. The analog output signal Vout (or Vo) from the circuit of Fig. 4 (and Fig. 5) is received by an adder 70 of the sigma-delta modulator 60. Adder 70 receives the analog signal Vout (or Vo) as well as a feedback signal from a feedback loop 71 of modulator 60. The output of adder 70 is received by integrator 72 and output is received by a quantizer 74 as a quantizer. one bit. Digital output 64 is filtered 66 next as explained above, and so on. Sigma-delta modulation is advantageous in that it provides oversampling and allows noise such as EMI to be handled and their adverse effects reduced. In particular, the noise is distributed by the sigma-delta modulator in the frequency band so that the signal-to-noise (S / N) ratio can be improved.

Referring again to Fig. 4, each capacitor (C1, C2, C3, C4) is discharged before charging the next. The process of discharging each capacitor is described in connection with the blanking pulse, with respect to Figs. 5-6.

Fig. 5 is a circuit diagram of the sensor circuit of Fig. 4, with respect to an erase cycle. During an erase cycle, a previously charged capacitor (C1, C2, C3, or C4) is discharged before the next write cycle. Fig. 6 includes example signals used during the erase cycle (s). No reading is carried out during the erase phase. During a cycle or erase phase, Q7 (the write pulse Clkw_r not present), and transistors Q5 and Q6 are activated by a Clk clear pulse_Er (see also Fig. 6). In this way, the capacitor (C1, C2, C3 and / or C4) is discharged to ground (e.g. V = 0) or to virtual ground (VG), as C does._int. Again, C_int mimics the capacitance of the Cs sensor. Once the capacitances Cs and C_int have been grounded and discharged, pulse and clear cycle ends. The next capacitor (C1, C2, C3, or C4) in the sequence can then be prepared, charged, and read.

Thus, with reference to Figs. 4-6, it will be appreciated that a rain sensor comprises: a sensing circuit comprising at least first and second sensing capacitors (eg, C1 and C2) that are sensitive to moisture on an external surface of a window , and at least one copycat capacitor (C_int) that mimics at least the charge and / or discharge of at least one of the first and second sensing capacitors; in which a writing pulse (Clk_wr) causes at least the first sensing capacitor (eg, C1) to charge, and a blanking pulse (Clk_Er) causes each of the first sensing capacitor (e.g. C1) and mimicking capacitor (C_int); wherein the presence of rain on the outer surface of the window in a sensing field of the first sensing capacitor (e.g. C1) causes a voltage (see Vo or Vout) at an output electrode of the mimicking capacitor (C_int) fluctuates proportionally to the fluctuation of the voltage at an output electrode (8) of the first sensing capacitor (e.g. C1), even if no rain is present in a field of the mimicking capacitor (C_int); and in which rain is detected based on an output signal (see Vo or Vout) from the output electrode of the mimicking capacitor (C_int), in which the output signal is read at least between one completion of the write pulse (Clk_wr) and a start of the blanking pulse (ClkEr) (see "read" area in Fig. 6).

Still referring to Fig. 5, during the erase cycle, the erase pulse ClkEr causes the capacitor (C1, C2, C3 and / or C4) and therefore also the mimic capacitor C_int are discharged to ground (eg, a fixed potential such as V = 0) (see conventional ground symbol in Fig. 5). However, it has been discovered that a fixed ground can lead to certain problems. In this way, during the erase cycle the erase pulse ClkEr makes the capacitor (C1, C2, C3 and / or C4) and therefore also the mimic capacitor C_int are discharged to a virtual ground VG that is floating (see VG and the ground symbol in Fig. 5). In other words, one electrode of each of the C1-C4 capacitors is floating. It can be at a floating or reference potential / voltage. It has been found that a floating or virtual ground can be highly advantageous (e.g., a floating ground and / or a capacitor electrode (s) can lead to a considerable reduction in EMI interference problems ). For example, such a floating or virtual ground can help reduce the chances of the sensor system being distorted by EMI interference. In this regard, reference is made to Figs. 28 (a) and 28 (b) (together with Fig. 5).

In Figs. 28 (a) - (b), reference numerals 7 and 8 refer to the electrodes of a capacitor (eg, C1, C2, C3 or C4). In these figures, “q” refers to the charge and Φ refers to the potential (Φ1 is different from Φ2). In Fig. 28 (a) the capacitor (eg C1) is grounded to a fixed potential such as 0 volts (the charge on the grounded electrode is fixed at + q). In this regard, when the charge on the grounded electrode 7 is set to + q, when an external body E is brought_B (eg, the finger of a person with a higher dielectric constant) to a sensing or sensing zone of the capacitor (eg, touching the front surface of the windshield on top of the capacitor) this external body induces a change in the charge of -Aq and the other electrode 8 that is not fixed changes from a charge of -q to a charge of -q + ​​Aq in an attempt to balance the charge. In this way, if the

ES 2 347 005 T3 capacitor at a fixed potential such as 0 volts, and read an output voltage from the capacitor, charge changes caused by an Aq that is not necessary would be read, and this can lead to false readings. Comparing Figs. 28 (a) and 28 (b), Fig. 28 (b) illustrates an advantage of making an electrode 7 of the sensing capacitor (eg C1-C4) floating (eg to a ground floating or virtual). In Fig. 28 (b), the charge q on electrode 7 is not fixed. P. For example, the charge on the electrode 7 changes from + q 'to + q "when the outer body makes contact with the windshield in a sensing or sensing zone, thereby indicating the floating nature of the electrode. In Fig. 28 (b), when the external body (eg, a person's finger) is applied to the windshield above the sensing or sensing zone of the capacitor the free charges on both electrodes 7 and 8 of the capacitor they change. In this way, the adverse effect of Aq is eliminated or reduced by using the floating or virtual ground VG (electrode 7 is floating). Specifically, when the electrode 7 is floating as in Fig. 28 (b), the external body (E_B) does not adversely affect the sum of the charge because adding the charges (+ q "and -q") of electrodes 7 and 8 when the external body is present gives a result of zero or practically zero. False readings due to EMI interference can also be reduced by using this floating feature. In this way, the floating nature can allow the absolute values ​​of the charges q on the electrodes of the capacitor 7 and 8 to be the same or substantially the same even when the external body is present since the electrode 7 is floating and not fixed to ground. . This is an exemplary reason why it may be advantageous to make the electrodes 7 of the capacitors C1-C4 float, or be attached to a virtual ground VG as shown in Fig. 5. Thus, referring to Figs. 5 and 28, sensor capacitors C1-C4 are floating and their two electrodes are isolated from earth. Consequently, the rain sensor comprises at least one sensor capacitor (C1, C3, C3 and / or C4) that is sensitive to humidity on an external surface of a window, the sensor capacitor including a first capacitor electrode (8 ) receiving a charging signal and a second capacitor electrode (7) separated from the first capacitor electrode (8); and wherein the second capacitor electrode (7) is floating so that the sensing capacitor is isolated from ground.

FIG. 6 is an example timer diagram of the signals applied to or captured from the circuit of FIG. 4-5 during the write and erase modes / cycles. As noted above, capacitors (C1-C4) are charged, read, quantized, and cleared sequentially. Fig. 6 shows a write clock pulse (Clk_wr) and deletion (Clk_Er) for each capacitor C1-C4, in sequence. Then the voltages are quantized and put at the output. The variable output voltages Vol-Vo4 correspond to the capacitors C1-C4 respectively, and therefore C_int. It should be noted that the output signals Vo1-Vo4 in Fig. 6 are taken in V_out (or Vo) in Figs. 4-5. Furthermore, in Fig. 6, the output signals Vo are read or analyzed (eg, for autocorrelation and / or cross-correlation) at the peak of the reading zones (see "Read" in Fig. 6) of the output signals where the output signals are substantially stabilized and / or the capacitor saturated. Specifically, the output signal Vout (or Vo) in Fig. 6 for a specific capacitor, it is read in the “reading zone” after the end of the writing pulse (Clk_wr) for that capacitor, and before and / or until the start of the blanking pulse (Clk_Er) for that capacitor.

Still referring to Fig. 6, for example, a drop of water on the outer surface of a windshield will affect the magnitude of the output signal (s) V_out (or Vo). For example, a drop of water above the area of ​​a given capacitor (e.g. C1) will cause the level of the output signal (s) V_out (or Vo) for said capacitor in the "reading" zone of the signal is higher compared to a situation in which said drop is not present. The exact magnitude or level depends on the size of the water drop. With increasing amounts of water, the magnitude of the signal in the "reading" area becomes higher because the dielectric constant of water is higher than that of glass and / or air and this causes the capacitance to increase. Similarly, if there is no water droplet present on the windshield above the area of ​​a given condenser (e.g. C1) then this will cause the level of the output signal (s) Vout ( or Vo) for the capacitor in the “reading” area of ​​the output signal is lower compared to a situation where there is a drop present.

The signals from the capacitor (s) can be converted from analog to digital through a sigma-delta modulation scheme or similar, which can be implemented at the software level or in any other suitable way such as through hardware. The principle behind a sigma-delta architecture is to roughly evaluate the signal, measure the error, integrate it, and then compensate for that error. The data can be oversampled at a given frequency of at least 32 kHz, eg. eg, more preferably 64 kHz, although it will be appreciated that other sampling frequencies can also be used. The current quantization can be recovered by the sigma-delta modulation scheme to produce a simple binary output of 0 or 1, corresponding to on and off, respectively.In this way, the sigma-delta modulation scheme can be used to reduce noise ( p. queuing a signal) and producing a digital output train (eg, 1s and 0s).

Before discussing the detailed operation of and example mathematics behind an example sensor algorithm, a summary of the states that the sensor and / or wipers can take will be provided in connection with Fig. 7, which is is an example state diagram showing how autocorrelation and cross-correlation data can be used to control vehicle windshield wipers. The system starts in a S702 Startup / Initialization State. In this state, all buffers are flushed. Based on the inputs of the capacitors C \, C₂, ..., C_n, an analog-to-digital conversion of the signals of the respective inputs is carried out through sigma-delta modulation. The data for the plurality of channels is read over a period of time T. The S704 Operation Mode Selector State functions as a switch that selects between manual and automatic wiper mode. If the S704 Operation Mode Selector State indicates that manual mode is selected, then in the S706 Manual Mode State an automatic mode can be disabled and a pre-existing manual mode can be enabled. Then the system returns to the Boot / Initialization State

ES 2 347 005 T3

S702. However, if the S704 Operation Mode Selector State indicates that the automatic mode is selected, then the automatic wiper mode is selected in the S708 Automatic Mode State.

In the S710 Autocorrelating Engine State, at least three calculations are performed. First, a normalized autocorrelation is calculated for each signal input of the capacitive system. Second, the autocorrelation gradient is calculated. Third, the difference between the signal input and a reference undisturbed signal (Aj) can be calculated. This information is passed on to the Is It Raining? S712, in which at least three conditions are checked to determine if it is likely to be raining, there is moisture on the windshield, etc. Probable indications of rain are that the autocorrelation gradient is greater than 1, that all autocorrelation values ​​are positive, and / or that A₃ is greater than a predefined threshold value t1. If these conditions are not met, the system moves to the Stop Wiper / Stop Engine S714 State, in which the windshield wipers are deactivated (if in motion) or not activated, and the engine is stopped (if running), and the system returns to the S702 Start / Initialization State.

On the other hand, if all conditions are met (e.g., there is likely an interaction of water, moisture, or some other disturbance in the glass, etc.), the system moves to the S716 Lowest Speed ​​State, wherein the wiper motor is activated at the lowest available speed. In the S718 Cross-Correlation Engine State, the cross-correlation between the input signals from the capacitors is calculated. The shape of the cross-correlation curve is determined, and the symmetry of the two sides of the cross-correlation curve is checked. As will be described later, these checks help, for example, to determine the type of disturbance (eg light rain, heavy rain, fog, snow, etc.) that is hitting the window (eg. , the windshield). In the S720 Rain Level Assessment Status, the “rain level” (eg, heavy, light, etc.) is determined. Based on this determination, the wiper motor is activated at the appropriate speed in the S722 Speed ​​Selector State. Finally, the system returns to the S702 Start / Initialization State to determine whether or not there is any change in the conditions outside the car.

The steps carried out by the rain sensor will be described in greater detail in connection with Fig. 8, which is an exemplary flow chart showing how the autocorrelation and cross-correlation data can be used to control the windshield wipers. . In Fig. 8, in step S800 the buffers are flushed, and sigma-delta modulation is applied on the data that is obtained at the output of the circuit of Fig. 4-5 (eg, from C_int, from capacitors C1-C4), and read in S802.

The algorithm for determining whether or not to start the windshield wipers, and if so, the speed at which to start the windshield wipers, begins by autocorrecting the modulated data by sigmadelta modulation in step S804. Autocorrelation can be used to analyze value series functions, such as time domain signals. An autocorrelation is the cross correlation of a signal with itself. Autocorrelation is used to find repetitive or practically repetitive patterns in a signal, such as, for example, determining the presence of a periodic signal hidden under noise, identifying the fundamental frequency of a signal that does not properly contain that frequency component but implies within it many harmonic frequencies, etc. Cross-correlation is a measure of the similarity of two signals, and is used to find characteristics in an unknown signal by comparing it to a known one; in other words, it can be used to carry out fingerprint identification of a signal. The cross correlation is a function of the relative time between the signals. Cross-correlation is applied to the digital signals of any two capacitors (p. eg, C1 and C2), in close spatial proximity, and the system looks for any degree of correlation in time periods other than a time period of zero. This space-time correlation allows the system to extract patterns of how the falling rain electrically projects itself onto the sensor system. As an example, the system can take the case of raindrops moving on a capacitor C1 at an instant t0 and the same droplet that "touches" another capacitor C4 (spatially separated by a distance L from C1). If the drop is moving at an average velocity of Vi, time (t0 + T), where T = L / Vi, the cross-correlation function will have another extreme or fold. The normalized magnitude of this extreme value can allow the system to determine the degree of rain that falls on the sensor.

Each capacitor C1-C4 has an autocorrelation function associated with the digitized Vout that results from the acquisition of data from it (or the corresponding acquisition of data from C_int). The autocorrelation function depends on the time difference, rather than time itself. Calculating autocorrelations is beneficial because it allows, for example, to deduce the fundamental frequency independently of the phase. Autocorrelations are advantageous over other methods, such as Fourier transforms (which can also be used) that provide information only on the underlying harmonics. In this way, the use of the autocorrelation of the data acquired from the capacitors C1-C4 (which, as explained above, includes the corresponding data acquired from the C_int intimator) can be used to detect and distinguish water droplets, dirt, dust, water droplets; a downpour, etc.

Note that the data for C_int are from capacitors C1-C4 because the capacitance C_int mimics or substantially mimics capacitances C1-C4 as explained above. So when we talk about receiving data from capacitors (e.g. C1-C4), this covers and includes receiving data from capacitance C_int. In other words, the output of the circuit of Fig. 4-5 is considered to be from capacitors C1-C4, even if it is not taken directly from them.

ES 2 347 005 T3

Rain, as a function of weather, can be represented by the following formula:

<img file="ES2347005T3_D0001.tif" />

Rain projects elecn «amane from another mana to

Essentially, b takes a binary value that indicates whether it is raining (1) or not (0). It will be appreciated that b is at least two bits long, and that 24 bits can be used for sigma-delta modulation. It will also be appreciated that a scale could be introduced, potentially to capture more data related to the voltages across capacitors C1-C4 (or C_int).

At the end of a sampling cycle L, for example, the output of the circuit of Fig. 4-5, p. g., from the system of four capacitors C1-C4 (or through C_int), ranges from 0000 to 1111, using binary digital data. A single bit to one can initiate a single wiper action. In the case where all bits are at zero (0000) or all bits are at one (1111), then no action of the wiper can be started, because there is probably nothing on the windshield, the car is completely submerged, etc., since all the capacitors in the array would be reading the same which is not consistent with rain falling on a window. Thus, the most likely events in which the wipers will be needed are those in the range of 0001 to 1110 (that is, when the output of all the capacitors in the system is not the same). When the data are in this range, or even if they are not in this range, correlation functions (autocorrelation and / or cross-correlation functions) can be executed using the following integral. It will be appreciated that the integral below can be rewritten in other ways, such as a summation. The correlations between two drops over a long period of time can be calculated according to the following formula:

^x or

(η, í; r₂₉ t₂) = R_b (Ar, Ar) where R_b is the correlation of a binary event, given as a function of the resistances r, in the instants of time i,; and L is a long sampling period during which data is collected. The sampling period L can be about 10 to 100 ms, and more preferably about 20-30 ms, which roughly corresponds to the frequency at which a standard human eye can discern. R_b it is also equal to a function of the correlation of the changes in the resistances in the Ar capacitors and the change over time. When Ar = 0, the autocorrelation value is determined since the data from the same capacitor is being analyzed, and when Ar + 0, cross-correlations are calculated since the correlation is carried out on data from different capacitors.

These functions are subject to several example underlying restrictions and assumptions. First,

<img file="ES2347005T3_D0002.tif" />

This restriction basically means that a drop of water or the like is moving on a given time scale. Secondly,

<img file="ES2347005T3_D0003.tif" />

This restriction mimics or substantially mimics what happens when drops of water or the like move from one condenser to another. In this way, the correlation functions could be interpreted as discrete steps p in space and T in time. This characteristic can be represented mathematically as the following equation:

<img file="ES2347005T3_D0004.tif" />

ES 2 347 005 T3

Basically, the left part of the equation establishes a theoretical network in space and time through which a drop of water or the like moves. For example, Fig. 9 is an example stylized view of how a raindrop could travel across a windshield. Fig. 9 shows a water droplet moving across a windshield in the XZ plane during an initial time period (t = 0) and a quantity time later (t = T). The assumption that the distribution of the drops is uniform in space and time allows the creation of a binary field produced by rain that is in a broad sense stationary. The system also assumes that the temporal correlation between the preferred pixels in the same neighborhood is high in the direction of the rain. Finally, the degree of autocorrelation and cross-correlation over time quantifies the fall of rain and other disturbances.

It will be appreciated that calculation time can be saved due to the nature of the correlation matrices and the nature of the rainfall. For example, correlation matrices can be symmetric. Also, as another example, because rain tends to fall downward from the sky and move up along a windshield, it may be sufficient to compare only capacitors that are arranged vertically relative to each other in cross-correlation, by time horizontally adjacent capacitors are ignored.

It should be noted that while using binary data, gray scale data can also be used with respect to the outputs of the circuit of Figs. 4-5, or other similar or suitable circuit (s).

Once the autocorrelation has been performed in step S804 (e.g., using the equation (s) discussed above, or some other suitable correlation equation (s) ( s)), one or more checks may be carried out to improve the accuracy of the system. Examples of such checks (p. (e.g., if the Rxx autocorrelated data have negative values, if a gradient is greater than one, and / or if the shape of an Rxx curve is different or significantly different from that of the normalized undisturbed autocorrelation data stored in memory) they are shown at the bottom of the table for step S804 in Fig. 8. One, two or all three checks can be carried out.

For example, a check of the autocorrelation data in step S806 may be to determine whether the autocorrelated data from one or more of the capacitor (s) (C1, C2, C3 and / or C4; or through the C_int copycat) comprise negative values. For example, when the autocorrelated data has negative value (s), then the system or method may indicate that it is not raining, may stop the windshield wipers, and / or may not operate the windshield wipers (see step S806). This check is to determine, for example, if a detected disturbance is rain. In this regard, Fig. 10 is an example of a graphical plot of maximum values ​​of normalized autocorrelations for different perturbations obtained experimentally. Fig. 10 illustrates that water signals are higher than undisturbed and positive signals, and that external interferences such as electromagnetic waves from CB radios and from the human hand touching a window tend to be below levels. of non-disturbance and that can be negative. In this way, to eliminate or reduce false detections due to external disturbances such as, for example, the human hand touching the window, interference from radio signals, etc., any signal with negative autocorrelation values ​​is considered as an event of "no rain". Negative autocorrelation values ​​could be considered, or other measures could be taken to eliminate or reduce false detections due to external interferences by, for example, gradient comparison (e.g., any curve less than or less than the curve / plot of no disturbance of Fig. 10 can be considered as a “no rain” event), shield capacitors, etc.

A second example check of the autocorrelation data is to check whether a gradient of an autocorrelation curve associated with the autocorrelation data is greater than one; and if not then the system or method may indicate that it is not raining, stop the windshield wipers and / or not actuate the vehicle windshield wipers (see step S806). In this check, the gradient of the normalized autocorrelation of the disturbance is checked. The normalized autocorrelation gradient of an undisturbed signal is close to 1. Measuring the gradient is beneficial because it is not affected by changes in temperature. In this way, the rain sensor can be virtually immune to false readings due to temperature changes in certain example embodiments of this invention. Gradients less than 1 (or some other default value) can be considered as no rain events.

A third example of checking autocorrelation data is to determine if there is a match or substantial match between an autocorrelation curve associated with the autocorrelation data and one or more predetermined autocorrelation curve (s) stored in a database and / or in memory. When the shape of the autocorrelation curve associated with the autocorrelated data of the circuit of Fig. 4-5 is different or substantially different with respect to the autocorrelation curve referring to the data of a normalized undisturbed autocorrelation, this can be considered as a no rain event and it can be indicated that it is not raining, the windshield wipers can be stopped, and / or the windshield wipers may not be actuated (see step S808). However, when there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the circuit of Fig. 4-5 and a predetermined autocorrelation curve associated with humidity such as rain, then it can be indicated that it is raining, they can be actuated. the windshield wipers or keep moving them.

In this regard, the shape of the autocorrelation curve can be used to reduce false actions of the wiper and / or false detections. Specifically, the normalized autocorrelation of an undisturbed signal is used as a reference. Then, the normalized autocorrelation of each captured signal from the circuit of Fig. 4-5 is compared to the reference to identify the closest fingerprint in certain example cases. Generally, the more

ES 2 347 005 T3 water is present in the detection zone, the greater the difference between the reference signal and the observed signal. In this way, correlation snapshots can be compared with reference snapshots of well-known events such as the presence of rain, dirt, no disturbance, ice, and so on. In general, correlation snapshots can be normalized, although the invention is not limited thereto. Correlation snapshots preferably plot r-values ​​versus amounts of time over a discrete time interval.

When there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the circuit of Fig. 4-5 and a predetermined autocorrelation curve associated with a non-wet substance such as dirt, then this can be considered as an event of no rain and it may be indicated that it is not raining, the windshield wipers may be stopped and / or not actuated (see step S808).

In this way, it will be appreciated that the shape of the autocorrelation curve that results from the data output of the circuit of Fig. 4-5 (from capacitors C1-C4, or through C_int) can be used to reduce false actions of the wiper as a third condition. For example, a normalized autocorrelation curve of an undisturbed signal can be used as a reference. Then, the normalized autocorrelation of each captured signal from the circuit of Fig. 4-5 is compared to the reference to identify the closest fingerprint. Generally, the more water present in the detection zone, the greater the difference between the reference signal and the observed / detected signal. In this way, correlation snapshots can be compared with reference snapshots of well-known events. In general, correlation snapshots can be normalized. Correlation snapshots preferably plot r-values ​​versus amounts of time over a discrete time interval.

A potential problem with capacitive rain sensors is that rapid changes in temperature (eg, due to the radiation absorbing black frit used to cosmetically hide the sensor pattern) change the dielectric "constant" (permittivity) of the glass. This is then recorded as a capacitance change and can be mistakenly interpreted as a rain signal. However, a normalized autocorrelation function remains unchanged, or substantially unchanged, for different temperatures even though there may be differences for non-normalized autocorrelation functions for different temperatures. In this way, the sensor system is unaffected or substantially unaffected by changes in temperature.

Additionally, extremely slow build-up of water such as ultra-fine mist can slowly build up to a level that triggers sensors based on Nyquist drives. At the time of observation that concerns the human eye (eg, 30-60 Hz), the autocorrelation function is able to distinguish between ultra-slow accumulation of dew or condensation and normal mist and rain.

Figs. 11A-11D provide experimentally obtained correlation snapshots of sample. These correlation snapshots, or fingerprints of an event, can be stored as reference correlation curves or fingerprints. The observed / detected correlation snapshots (eg, autocorrelation curves) can be compared to these reference fingerprints to determine the type of event that is occurring. For example, Fig. 11A is an experimentally obtained autocorrelation snapshot indicative of heavy rain. Fig. 11B is an experimentally obtained autocorrelation snapshot indicative of light haze. Fig. 11C is an experimentally obtained autocorrelation snapshot indicative of CB radio interference. Fig. 11D is an experimentally obtained autocorrelation snapshot indicative of a body with a voltage grounded. It will be appreciated that these fingerprints are provided as examples and reflect experimentally obtained data. Actual events can differ in various characteristics. Thus, when it is determined that there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the circuit of Fig. 4-5 and a predetermined non-moisture autocorrelation curve such as that of Fig. 11C or Fig. 11D, then this may be considered as a no rain event and it may be indicated that it is not raining, the wipers may stop and / or they may not be operated (see step S808). However, when it is determined that there is a match or substantial match between the autocorrelation curve associated with the autocorrelation data from the circuit of Fig. 4-5 and a predetermined humidity related autocorrelation curve such as that of Fig. 11A or Fig. . 11B, then this can be considered a rain event and it can be indicated that it is raining, the windshield wipers can be operated and / or the wipers can continue to move. In addition to the predetermined autocorrelation curves of Figs. 11A-11D, other reference fingerprints can be stored and / or compared with correlation snapshots observed in other example embodiments of this invention.

Returning to Fig. 8, in step S806, it is determined whether each of the three conditions shown at the bottom of the S804 table is met. Specifically, it is determined in S806 whether each of the following is true: a) the autocorrelated data have no negative data; b) a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value such as one; c) the shape of the autocorrelation curve associated with the autocorrelation data of the circuit of Fig. 4-5 is different from a predetermined autocorrelation curve associated with undisturbed autocorrelation data. If all the conditions are not met, this is an indication of a no rain event and the process moves to step S808 in which the vehicle wiper (s) stop (if it is moving). ) or remain disabled, and S800 initialization starts again. However, if all these requirements are met in S806, then the process moves to S810 and the vehicle windshield wipers are activated (eg. windshield wipers) at their slowest speed.

ES 2 347 005 T3

Fig. 13 illustrates an example of autocorrelation. In Fig. 13, the values ​​of (or corresponding to) sensing capacitor C1 are, in sequential times -t2, -t1, t0, t1, t2 and t3 are 0, 0, 1, 1, 0 and 0 respectively. The autocorrelation for time 0 (aco) is determined by multiplying the values ​​corresponding to C1 in a non-lagged manner, and then adding or summing the results. It can be seen from Fig. 13 that aco is equal to 2 in this case. In this way, in the autocorrelation graph at the bottom of Fig. 13, an entry is produced in the graph at time 0 for an autocorrelation value of 2. It should be noted that the autocorrelation graph at the bottom of Fig. 13 is similar, although simpler than the autocorrelation plot of Fig. 10 and the autocorrelation values ​​can be obtained for Fig. 10 in a similar manner. Next, still with reference to Fig. 13, an autocorrelation is carried out using the capacitance values ​​corresponding to C1 for the next point in time to obtain the autocorrelation value ac1. This next autocorrelation value (ac1) is obtained by shifting the sequence of values ​​in the lower row for C1 with respect to the upper row as shown in Fig. 13, and then multiplying the values ​​in the rows that form the same line and adding the results. Fig. 13 illustrates that acl is equal to 1 for time 1. In this way, this autocorrelation value of 1 for time t1 can be entered into the graph at the bottom of Fig. 13 and a line is drawn between the two points of data entered for purposes of example and understanding. The, for the next value (or period) of time, the lower row is run again to another segment higher with respect to the upper row and the process is repeated, and so on. It can be seen that the autocorrelation plots in Fig. 10 can be obtained in a similar manner. In Fig. 13, it will be appreciated that a cross correlation can be carried out by substituting the values ​​corresponding to C1 in the bottom row with values ​​of or corresponding to another capacitor such as C2 (or C3 or C4).

Examining an autocorrelation and / or cross-correlation can also help distinguish between, for example, light rain or heavy rain. For example, if only the autocorrelation over time is high (and the cross-correlation is low), then there is probably only light rain. Fig. 12A is an example correlation matrix showing light rain. Note in Fig. 12A is that the correlations between C1 and C1, C2, and C2, C3 and C3, and C4 and C4 (these are autocorrelations) over a given period of time are high, while the rest of the correlations (the cross correlations ) are low. Based on hypotheses and confirmed experimental data, such a matrix would indicate light rain.

On the other hand, if both the autocorrelation and the cross-correlation in time between the signals from the capacitors are high, then there is probably heavy rain. Fig. 12B is an example correlation matrix showing heavy rain. In Fig. 12B, not only are the autocorrelations of the individual capacitors high (that is, the autocorrelations are the correlations between C1 and C1, C2 and C2, C3 and C3, and C4 and C4), the autocorrelations between the different capacitors are also generally high (The correlations in Fig. 12B running diagonally from the upper left to the lower right are the autocorrelations, and the rest are the cross correlations). Based on hypotheses and confirmed experimental data, such a matrix would indicate a heavy rain. The degree of cross-correlation can be quantified to determine the relative velocity of the rain. This data can, in turn, be used to activate various windshield wiper speeds, as appropriate for the speed of the rain. For example, the more high cross correlations there are, the faster the wiper speed to use.

More systematically, in step S812, cross-correlations (correlations between data corresponding to different capacitors) are calculated, and the two sides of the cross-correlation curve are used to determine a level of symmetry L. If the level of symmetry is lower at a predefined threshold t_min, step S814 directs the system to step S816 in which the windshield wipers are activated at the lowest speed, and the system is returned to the initialization step S800. If the level of symmetry is greater than t_min but less than an arbitrary value t, step S818 directs the system to step S820 in which the windshield wipers are activated at a higher or medium speed, and the system is returned to the initialization step S800. It will be appreciated that a plurality of arbitrary values ​​t, and a level of symmetry that lies between t, and t, can be specified.₊₁ it will activate a suitable corresponding wiper speed and then return the system to the S800 initialization stage. Finally, in step S822, if the level of symmetry is above a predefined level t_max, step S822 directs the system to step S824 in which the windshield wipers are activated at a higher speed, and the system is returned to the initialization step S800. In this way, the correlations of the data outputs of the circuit of Fig. 4-5 can be used to adjust the wiper speed. The more high autocorrelations there are, the faster the wiper speed to use due to the likelihood of a heavier rain.

Figs. 12-24 illustrate some examples of cross-correlations carried out. Fig. 14 shows the data of the cross-correlations, while Figs. 15-24 illustrate cross-correlation plots of certain of the data in Fig. 14 in which rain is detected. In Figs. 15-24, each period on the horizontal axis is one microsecond (1 jus) for example purposes, and sampling was carried out every microsecond. As explained above with respect to Fig. 13, in Figs. 15-24 at time = 0 (period 0), there is no time shift of the values ​​of the different capacitors that are being correlated. Fig. 14 illustrates that when rain is present (see signals S1-S5 and W1-W5), the delta signals corresponding to autocorrelation were high. Figs. 15-24 are cross-correlation plots corresponding to these signals. It helps to find symmetries between the tracings on the left and on the right of each of the Figs. 15-24 (one side of zero is compared to another side of zero). In general, if there is symmetry around the zero period axis, there is not much cross-correlation, which indicates that the detected rainfall is not very strong. However, if there is an asymmetry around the zero period axis, then this means more cross-correlation and indicates that the rain is stronger or stronger. For example, note the asymmetry in Figs. 18, 19, and 23 around the zero-period axis due to peaks or troughs on one or both sides. More cross-correlation indicates that raindrops are moving from a detection zone to a

ES 2 347 005 T3 capacitor to a detection zone of another capacitor. In this regard, each interaction of a raindrop and the surface of a windshield has its own correlation signature in the time domain. A high cross-correlation indicates that the same droplet is being detected by different capacitors, at different points in time (eg, see also Fig. 9). Note that the lowercase "t" in Fig. 9 represents the same as the period axis in Figs. 15-24.

In this way, it will be appreciated that a humidity sensor (eg, a rain sensor) can detect rain on the vehicle window without the need for a reference condenser. Spatio-temporal correlation can be used. All of the capacitors, or a plurality of capacitors, in the sensor system may have an identical or substantially identical shape in certain example embodiments. For example purposes, at a given point in time (p. eg, t1), the system can compare values ​​corresponding to C1 with values ​​corresponding to C2, and / or values ​​corresponding to another capacitor. For this time t1 the system can also compare values ​​corresponding to C1 with itself (autocorrelation), and can also compare the autocorrelation for C1 with the autocorrelation for C2 and / or for other sensor capacitor (s).