System for heating an automotive glazing

Abstract

The system (100) comprises a glazing (102), a LiDAR unit (104), a first camera (106), a heating unit (108), and a control unit (110) The control unit (110) is connected to the LiDAR unit (104), the first camera (106), and the control unit (108), wherein the control unit (110) comprises a microcontroller (302). The microcontroller (302) is configured to dynamically determine the thickness of ice formed across the glazing based on the correlation between the optical data of the image at the first zone of glazing with the thickness of ice formed on the glazing. The microcontroller (302) determines the electric power required to heat the glazing to melt the ice formed on the glazing, based on the thickness of the ice and operates the heating unit to heat the glazing and thereby melt the ice formed on the glazing.

Description

SYSTEM FOR HEATING AN AUTOMOTIVE GLAZINGTECHNICAL FIELD

[0001] The present disclosure relates generally to an automotive glazing, it particularly relates to a system for heating an automotive glazing.BACKGROUND

[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0003] The formation of ice on glazing surfaces, particularly in regions experiencing cold temperatures or inclement weather conditions, poses significant challenges to the efficient operation and safety of vehicles. Ice accumulation on glazing surfaces not only obstructs visibility and may pose significant safety risk to the occupants.

[0004] Particularly, in vehicles with Advanced Drive Assistance Systems (ADAS), a camera is placed behind the glazing to capture image of the road. Further, the ADAS system determines the actions to be performed based on the image. In such scenarios, formation of ice on the glazing in the field of view of ADAS camera may have drastic impact on the quality of the image captured by the ADAS camera. This deterioration in the quality of the image may affect the performance of ADAS system.

[0005] Additionally, existing systems operates the heating unit at a standard operational condition irrespective of the thickness of the ice. This results in inefficient usage of power to melt the ice, wherein same amount of voltage is applied irrespective of the ice thickness.

[0006] Moreover, accurately determining the thickness of ice layers on glazing surfaces remains a complex task. Conventional methods for measuring ice thickness lack precision and real-time feedback, leading to inefficiencies in the operation of heating units. Consequently, this can result in excessive power consumption or inadequate ice removal, impacting both energy usage and the effectiveness of ice removal mechanisms.

[0007] In view of these challenges, there exists a critical need for a system capable of detecting ice formation on glazing surfaces, accurately determining ice thickness, and accordingly operating the heating unit.SUMMARY OF THE DISCLOSURE

[0008] In an embodiment, a system for heating an automotive glazing is disclosed. The system comprises a glazing, a LiDARunit, a first camera, a heating unit, and a control unit. The LiDAR unit is positioned on the interior side of the glazing covering a first zone of the glazing, wherein the LiDAR unit is configured to transmit optical signals towards the exterior of the glazing and receive the reflected optical signals. The first camera is positioned on the interior side of the glazing and configured to capture an image of the glazing. The heating unit configured to heat the glazing using electric power. The control unit is connected to the LiDAR unit, the first camera, and the control unit, wherein the control unit comprises a microcontroller. The microcontroller is configured to receive the data relating to transmission and reflection of the optical signals at the first zone of the glazing from the LiDAR unit. Further, the microcontroller processes the data received from the LiDAR unit to determine the thickness of ice formed on the surface of the glazing. Furthermore, the microcontroller receives the image of the glazing captured by the first camera. Subsequently, the microcontroller processes the image to correlate the optical data of the image at the first zone of glazing with the thickness of ice formed on the glazing. Next, the microcontroller dynamically determines the thickness of ice formed across the glazing based on the correlation between the optical data of the image at the first zone of glazing with the thickness of ice formed on the glazing. Finally, the microcontroller determines the electric power required to heat the glazing to melt the ice formed on the glazing, based on the thickness of the ice and operates the heating unit to heat the glazing and thereby melt the ice formed on the glazing.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The following briefly describes the accompanying drawings, illustrating the technical solution of the embodiments of the present invention, for assisting the understanding of a person skilled in the art to comprehend the invention. It would be apparent that the accompanying drawings in the following description merely show some embodiments of thepresent invention, and persons skilled in the art can derive other drawings from the accompanying drawings without deviating from the scope of the disclosure.

[0010] FIG. 1 illustrates a system 100 for heating an automotive glazing 102, in accordance with an embodiment;

[0011] FIG. 2 illustrates a schematic of the system 100 for heating an automotive glazing 102, in accordance with an embodiment;

[0012] FIG. 3 illustrates an architecture of the control unit 110, in accordance with an embodiment;

[0013] FIG. 4 illustrates a glazing 102 with multiple zones of heating, in accordance with an embodiment;

[0014] FIG. 5 is a flowchart of a method of operation of the heating unit 108 of a glazing 102, in accordance with an embodiment;

[0015] FIG. 6 is a flowchart of a method of operation of the heating unit 108 of a glazing 102, in accordance with an embodiment and

[0016] FIG. 7 is a flowchart of a method of training a machine learning model 310, in accordance with an embodiment.

[0017] Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the disclosure.DETAILED DESCRIPTION

[0018] The following detailed description includes references to the accompanying drawings, which form part of the detailed description. The drawings show illustrations in accordance with example embodiments. These example embodiments are described in enough detail to enable those skilled in the art to practice the present subject matter. However, it may be apparent to one with ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. The embodiments can be combined, other embodiments can be utilized, or structural and logical changes can be made without departing from the scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense.

[0019] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one. In this document, the term “or” is used to refer to a non-exclusive “or”, such that “A or B” includes “A but not B”, “B but not A”, and “A and B”, unless otherwise indicated.

[0020] FIG. 1 illustrates a system 100 for heating an automotive glazing 102, in accordance with an embodiment. The system 100 comprises a glazing 102, a LiDAR unit 104, a first camera 106, a heating unit 108, and a control unit 110. In some embodiments, the system 100 may optionally comprise a voltage booster 112, a second camera 114, a temperature sensor, and a humidity sensor.

[0021] FIG. 2 illustrates a schematic of the system 100 for heating an automotive glazing 102, in accordance with an embodiment. Referring to FIGs. 1 and 2, the LiDAR unit 104 is positioned on the interior side of the glazing 102 covering at least a first zone 208 of the glazing 102. The LiDAR unit 104 is configured to transmit optical signals (example: laser signals) towards the exterior of the glazing 102 and receive the optical signals reflected from the glazing 102.

[0022] Further, the first camera 106 is positioned on the interior side of the glazing 102 and the first camera 106 is configured to capture an image of the glazing 102. The first camera 106 may be positioned in a manner that the first camera 106 can capture the image of the entire glazing 102.

[0023] In one embodiment, the first camera 106 may be a wide-angle camera.

[0024] The heating unit 108 is configured to heat the glazing 102 using electric power. The heating unit 108 may be implemented as a heating grid comprising metallic wires, conductive coatings formed on the surface of the glazing 102 or the like.

[0025] The control unit 110 is connected to the LiDAR unit 104, first camera 106 and the heating unit 108. The control unit 110 is configured to operate the heating unit 108 by varying the electric power supplied to the heating unit 108.

[0026] The system 100 comprises the voltage booster 112 that is connected to the heating unit 108 and the control unit 110. The control unit 110 operates the voltage booster 112 to alter the electric voltage supplied to the heating unit 108.

[0027] The second camera 114 may be positioned behind the first zone 208 of the glazing 102. The second camera 114 may be configured to capture the image of the road from inside the glazing 102.

[0028] The second camera 114 may be used in Advanced Driver Assistance System 100 (ADAS) of a vehicle. The image captured by the second camera 114 is used to determine actions to be performed by ADAS to control the operation of the vehicle.

[0029] In one embodiment, the system 100 may comprise a single camera to perform the functions of both the first camera 106 and the second camera 114. The single camera may be a movable camera that can assume a first position inside the vehicle to perform the function of the first camera 106 and may assume a second position to perform the function of the second camera 114.

[0030] The temperature sensor 116 may be configured to measure the temperature of the glazing 102, environment or the like.

[0031] The humidity sensor 118 may be used to measure the relative humidity of the environment outside the glazing 102.

[0032] Referring to the detailed view A of FIG. 2, the glazing 102 may be a laminated glazing 102. The glazing 102 may comprise a first substrate 202, a second substrate 204, an interlayer 206, and the heating unit 108. The interlayer 206 is disposed between the first substrate and the second substrate. The heating unit 108 is disposed between the first substrate 202 and the second substrate 204, wherein the heating unit 108 interfaces with one of the first substrate 202 or the second substrate 204.

[0033] In one embodiment, the first substrate 202 and the second substrate 204 are made using a transparent material such as glass or polymer.

[0034] FIG. 3 illustrates an architecture of the control unit 110, in accordance with an embodiment. The control unit 110 comprises a microcontroller 302, a memory module 304, input modules 306, output modules 308, and a machine learning model 310.

[0035] The microcontroller 302 may refer to an electrical device that minimally includes a processor logic (e.g., one or more microprocessors) and is adapted to execute instructions based on information stored in a memory either within the microcontroller 302 or external to the microcontroller 302. Microcontroller 302 as used herein may also include any necessary timers and / or clocks.

[0036] The microcontroller 302 is configured to receive the data relating to transmission and reflection of the optical signals at the first zone 208 of the glazing 102 from the LiDAR unit 104. Further, the microcontroller 302 processes the data received from the LiDAR unit 104 todetermine the thickness of ice formed on the surface of the glazing 102. The time of flight of the signals is used to determine the thickness of the ice formed on the glazing 102.

[0037] Further, the microcontroller 302 receives the image of the glazing 102 captured by the first camera 106. The microcontroller 302 correlates the optical data of the image at the first zone 208 of glazing 102 with the thickness of ice formed on the glazing 102. The optical data may be pixel intensity, color value or the like.

[0038] The microcontroller 302 then determines the thickness of the ice formed across the glazing 102 based on the correlation between the optical data of the image at the first zone 208 of glazing 102 with the thickness of ice formed on the glazing 102.

[0039] In an embodiment of the invention, the system is configured to detect ice formation on glazing. Specifically, the control unit (110) having the microcontroller (302) is configured to detect both moisture containing materials (such as and not limited to ice, fog, snow, dew, etc.) and other materials as well. The control unit (110) having the microcontroller (302) is configured to detect the presence of ice, fog, snow, and / or other material as well basis the data sent from LiDAR and the camera solution (first camera in a preferred embodiment). The control unit (110) is further configured to detect and differentiate the presence of moisture containing material like ice, fog, snow from other materials. The control unit (110) is configured to activate the heating unit (108) only upon detection of moisture containing materials. The heating unit (108) is thus not activated for other obstacles in the optical pathway due to dust, oil or breakage and provide an alert to the user or electronic control unit (ECU).

[0040] Further, the microcontroller 302 determines the electric power required to heat the glazing 102 to melt the ice and accordingly operates the heating unit 108.

[0041] The memory module 304 may include a permanent memory such as hard disk drive, may be configured to store data, and executable program instructions that are implemented by the microcontroller 302. The memory module 304 ay be implemented in the form of a primary and a secondary memory. The memory module 304 may store additional data and program instructions that are loadable and executable on the microcontroller 302, as well as data generated during the execution of these programs. Further, the memory module 304 may be volatile memory, such as random-access memory and / or a disk drive, or non-volatile memory. The memory module 304 may comprise of removable memory such as a Compact Flash card, Memory Stick, Smart Media, Multimedia Card, Secure Digital memory, or any other memory storage that exists currently or may exist in the future.

[0042] The input modules may provide an interface for input devices such as keypad, touch screen, mouse and stylus among other input devices.

[0043] The output modules may provide an interface for output devices such as display screen, speakers, printer and haptic feedback devices, among other output devices.

[0044] The machine learning model 310 may be a set of computer-executable programs implemented on the microcontroller 302 to perform a set of tasks.

[0045] FIG. 4 illustrates a glazing 102 with multiple zones of heating, in accordance with an embodiment. The glazing 102 comprises multiple zones (I, II, III, IV, V, VI) of heating with varying priority levels. As an example, the zone corresponding to the first camera 106 may have higher priority as compared to the zone towards the edge of the glazing 102. Each of the heating zones can be independently heating using the heating unit 108.

[0046] In one embodiment, multiple heating sub-units each for one heating zone may be used in the glazing 102. Each of the heating sub-units may be connected to the control unit 110 and can be operated independently by the control unit 110.

[0047] The microcontroller 302 may operate the each of the heating sub-units at different electric power based on the thickness of the ice formed in that zone of the glazing 102.

[0048] FIG. 5 is a flowchart of a method of operation of the heating unit 108 of a glazing 102, in accordance with an embodiment. At step 502, the microcontroller 302 receives the data relating to transmission and reflection of optical signals from the LiDAR unit 104. The LiDAR unit 104 may have a transceiver that is configured to transmit optical signals towards the exterior of the glazing 102 and receive optical signals reflected by the glazing 102.

[0049] At step 504, the microcontroller 302 determines the thickness of the ice formed on the glazing 102. The microcontroller 302 receives the data relating to transmission and reflection of optical signals from the LiDAR unit 104. The microcontroller 302 then processes the data to determine the time of flight of multiple signals transmitted and reflected back. Thus, the microcontroller 302 determines the thickness of the ice formed on the glazing 102.

[0050] At step 506, the microcontroller 302 receives temperature data and the humidity data measured by the temperature sensor and the humidity sensor.

[0051] At step 508, the microcontroller 302 determines whether the temperature has reached dew point temperature or not.

[0052] If the temperature has reached dew point temperature, then at step 510, the microcontroller 302 turns ON the heating unit 108.

[0053] If the temperature has not reached dew point temperature, then at step 512, the microcontroller 302 turns OFF the heating unit 108.

[0054] FIG. 6 is a flowchart of a method of operation of the heating unit 108 of a glazing 102, in accordance with an embodiment. At step 602, the microcontroller 302 receives the data relating to transmission and reflection of optical signals from the LiDAR unit 104.

[0055] At step 604, the microcontroller 302 determines the thickness of the ice formed on the glazing 102. As explained earlier, the microcontroller 302 may calculate time of flight of the optical signals to determine the thickness of the ice formed on the glazing 102.

[0056] At step 606, the microcontroller 302 may receive an image of the glazing 102 captured by the first camera 106. The image may be an image of the entire glazing 102.

[0057] At step 608, the microcontroller 302 correlates the optical data from the first camera 106 with the determined ice thickness. To explain further, the image captured by the first camera 106 includes the image of the entire glazing 102. The optical data on the first zone 208 of the glazing 102 in the image is extracted. As an example, the optical data may be pixel intensity. Further, the optical data at the first zone 208 is compared with the ice thickness determined in the first zone 208. As a result, a correlation between the optical data and the ice thickness is obtained. In other words, through this analysis, the ice thickness for a given value of optical data is determined. Therefore, for any value of optical data the microcontroller 302 may determine the thickness of ice.

[0058] At step 610, the microcontroller 302 determines the ice thickness across the glazing 102, based on the determine correlation between the optical data and the ice thickness. The microcontroller 302 extracts values of optical data for the image across the entire glazing 102. The microcontroller 302 then calculates the ice thickness for every data point of optical value across the glazing 102.

[0059] At step 612, the microcontroller 302 determines the electric power required to melt the ice formed on the glazing 102. The electric power required may be dependent on the thickness of the ice formed on the glazing 102. As mentioned earlier, in one embodiment, the glazing 102 may have multiple zones with different priority levels. The microcontroller 302 may determine the electric power required for melting the ice formed on each zone of the glazing 102.

[0060] At step 614, the microcontroller 302 operates the heating unit 108 to melt the ice. The control unit 110 and the microcontroller 302 may be operably connected to the heating unit108 via a voltage booster 112. The microcontroller 302 may operate the voltage booster 112 to step up or step down the voltage supplied to the heating unit 108 based on the ice thickness.

[0061] Further, the microcontroller 302 may operate the multiple heating sub-units sequentially based on the priority levels of the zones of the glazing 102.

[0062] In one embodiment, the system 100 may further comprise a wiper configured to clean the glazing 102. The microcontroller 302 controls the operation based on the ice formation on the glazing 102. The microcontroller 302 may not operate the wiper if there is ice formation on the glazing 102.

[0063] FIG. 7 is a flowchart of a method of training a machine learning model 310, in accordance with an embodiment. The system 100 may comprise the machine learning model 310 to automatically predict the electric power required to melt the ice formed on the glazing 102.

[0064] At step 702, the machine learning model 310 receives data relating to environmental parameters, temperature, thickness of ice, electric power required to melt the ice and the like. The data may be historical data of the glazing 102 that is collected over a period of time.

[0065] At step 704, the machine learning model 310 determines a between the environmental parameters, thickness of ice, temperature and electric power required to melt the ice. The machine learning model 310 may apply data analysis techniques to determine the correlation.

[0066] At step 706, the machine learning model 310 may predict the electric power required to melt the ice formed on the glazing 102 based on the determined correlation. The machine learning model 310 may predict the ice thickness based on the environmental parameters and accordingly predict the electric power required to melt the ice formed on the glazing 102.

[0067] Experiment: The system disclosed herein is capable of detecting ice formation on glazing surfaces, accurately determine the ice thickness, and accordingly operating the heating unit. The table provided in the following gives a detailed view of the operations performed along with the percentage of savings obtained:Herein above, voltages VI, V2 at two different points are respectively regarded. V(r) refers to the difference between the said voltages. Current and power parameters at temperatures -20° C and 23° C are regarded to obtain the respective savings percentage. For the above case, defrosting is to be performed in 12 min and 0.4 is the standard which has been taken as a reference.

[0068] Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the system and method described herein. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

[0069] Many alterations and modifications of the present invention will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. It is to be understood that the description above contains many specifications, these should not be construed as limiting the scope of the invention but as merely providing illustrations of some of the personally preferred embodiments of this invention. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents rather than by the examples given.List of reference numerals100 - System for heating an automotive glazing102 - Glazing104 - LiDAR unit106 - First camera108 - Heating unit110 - Control unit112 - Voltage booster114 - Temperature sensor116 - Humidity sensor202 - First substrate204 - Second substrate206 - Interlayer208 - First zone302 - Microcontroller304 - Memory module306 - Input modules308 - Output modules310 - Machine learning model

Claims

CLAIMS1. A system (100) for heating an automotive glazing (102), wherein the system (100) comprises: a glazing (102) made using a transparent material; a LiDAR unit (104) positioned on the interior side of the glazing (102) covering a first zone (208) of the glazing (102), wherein the LiDAR unit (104) is configured to: transmit optical signals towards the exterior of the glazing (102) and receive the reflected optical signals; a first camera (106) positioned on the interior side of the glazing (102) and configured to capture an image of the glazing (102); a heating unit (108) configured to heat the glazing (102) using electric power; and a control unit (110) connected to the LiDAR unit (104), the first camera (106), and the control unit (110), wherein the control unit (110) comprises a microcontroller (302) configured to: receive the data relating to transmission and reflection of the optical signals at the first zone (208) of the glazing (102) from the LiDAR unit (104); process the data received from the LiDAR unit (104) to determine the thickness of ice formed on the surface of the glazing (102); receive the image of the glazing (102) captured by the first camera (106); process the image to correlate the optical data of the image at the first zone (208) of glazing (102) with the thickness of ice formed on the glazing (102); dynamically determine the thickness of ice formed across the glazing (102) based on the correlation between the optical data of the image at the first zone (208) of glazing (102) with the thickness of ice formed on the glazing (102); determine the electric power required to heat the glazing (102) to melt the ice formed on the glazing (102), based on the thickness of the ice; and operate the heating unit (108) to heat the glazing (102) and thereby melt the ice formed on the glazing (102).

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a second camera (114) positioned behind the first zone (208) of the glazing (102) and is configured to capture an image of the road through the first zone (208) of the glazing (102).

3. The system (100) as claimed in claim 2, wherein the image captured by the second camera (114) is used in Advanced Driver Assistance System (100) (ADAS) of a vehicle.

4. The system (100) as claimed in claim 1, wherein the glazing (102) comprises multiple zones of heating with varying priority levels and the microcontroller (302) is configured to operate the heating unit (108) in the zone with highest priority and accordingly operate the heating unit (108) for other zones.

5. The system (100) as claimed in claim 1, wherein the system (100) further comprises: a machine learning model (310) implemented in the microcontroller (302) or a remote server, wherein the machine learning model (310) is configured to: receive data relating to environmental parameters, temperature, thickness of ice, electric power required to melt the ice; determine the correlation between the thickness of ice, temperature and electric power required to melt the ice; and predict the electric power required to melt the ice formed on the glazing (102) based on the determined correlation.

6. The system (100) as claimed in claim 1, wherein the system (100) comprises: a temperature sensor (116) configured to measure the temperature of the glazing (102) and a humidity sensor (118) configured to measure the humidity of the environment around the glazing (102).

7. The system (100) as claimed in claim 1, wherein the heating unit (108) is defined as a heating grid formed using metallic wires embedded in the glazing (102) or as a metallic coating formed over the surface of the glazing (102).

8. The system (100) as claimed in claim 1, wherein the system (100) comprises: a voltage booster (112) connected to the control unit (110), wherein the voltage booster (12) is configured to adjust the voltage supplied to the heating unit (108) based on the thickness of the ice.

9. The system (100) as claimed in claim 1, wherein the system (100) comprises: a wiper configured to clean the glazing (102), wherein the control unit (110) is configured to operate the wiper based on the ice formed on the glazing (102) by preventing the operation of the wiper if ice is present on the glazing (102).

10. The system (100) as claimed in claim 1, wherein the heating unit (108) comprises multiple sub-units that are disposed across the glazing (102) and configured to be operated independently.

11. The system (100) as claimed in claim 10, wherein the control unit (110) is configured to independently operate the sub-units and control the electric power to the sub-units based on the ice thickness in the region of each of the sub-units.

12. The system (100) as claimed in claim 1, wherein the control unit (110) is configured to detect and differentiate the presence of moisture containing material from other material basis the data from the LiDAR unit (104) and the first camera (106); and said control unit (110) being further configured to activate the heat unit (108) only upon detection of moisture containing material.

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